Tech-Exec Issue 22

NEWS CYBERTRUCK KAMALA HARRIS GARY DONNELLY NONNA MILMEISTER CATCH CAMERAS

Interview in our Digital Magazine

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INTERVIEW CAMPAIGN:

An opportunity for C-Level technology leaders to showcase best practice in their industry

ENERGY TECH SUMMIT

This year, we’re spotlighting the global experts who are pushing boundaries to accelerate the green transition. Join us on October 10 to witness innovation in action and collaborate with over 500 industry professionals on making a carbon-free energy system a reality. Get tickets now: October 10, 2024. Kraftwerk, Berlin

WELCOME TO OUR CONTRIBUTORS IN THIS ISSUE…

At Tech-Exec, we believe in the power of collaboration and knowledge sharing among industry experts. In this issue, we showcase thought leaders, innovators, and visionaries who are shaping the future of technology and business. Our contributors are at the forefront of the latest trends, insights, and strategies that are driving success in today’s digital landscape.Join us as we dive into the minds of these exceptional individuals who are revolutionising the tech world and inspiring the next generation of leaders.

Nonna Milmeister

Nonna is an award-winning and sought-after champion of Data Management and Information Quality. She is a thought leader in data quality, improving customer service and the productivity of Australian businesses. READ HERE

Kathleen Hurley

Kathleen worked at the intersection of technology and business operations throughout her career. She has led transformative technology initiatives for prominent firms in the commercial real estate and financial services sectors, translating business strategy into measurable outcomes. READ HERE

Jerzy Biernacki

Jerzy's passion is aiding companies in cracking their toughest problems and helping them grow their businesses by using top-class tailored software solutions and taking advantage of the latest technologies, especially AI, Machine Learning, and Data Science. READ HERE

Cedric Berger

Cedric has 25 years of experience in healthcare, both public and private sectors, in Europe and the USA, and is an expert at the interface of biology, medicine, and technology. READ HERE

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JP’S EDITOR’S NOTES

Welcome to the 22nd edition of Tech-Exec, the magazine for technology leaders by technology leaders, where we bring you exclusive news, insights, interviews and thought leadership from some of the brightest minds in the technology space.

I have celebrated another birthday since our last issue, and as usual, it gave me some time and reason to reflect. The previous three hundred and sixtysomething days had been particularly tumultuous at Tech-Exec Towers, and whilst being your own boss has plenty of benefits, running a glossy digital magazine for super-cool and talented technology leaders can, believe it or not, have its downside.

Time is one of them…or rather, a lack of it. If, like me, you are not a naturally early riser, prefer working late into the night, pass on the 5 am meditation and only ever read a book when lying on a beach with a cold beer in your hand, you’ll probably have found yourself repeating the same utterances - Where has today gone? How did that month go so fast? Where has this year gone?

On that note, we hope to have crammed as much great content into these pages as possible and that you can find the time to enjoy it at your leisure.

In our lead article, we sit down with Gary Donnelly, Group CIO at Culina Group, for an in-depth discussion on their technology roadmap.

Gary shares how Culina is navigating digital transformation, streamlining operations, and leveraging emerging tech to stay ahead in the logistics industry.

We’re also thrilled to feature a fireside chat with Kathleen Hurley, founder at Sage Inc, who delves into the growing challenges of cybersecurity. Kathleen highlights how SMEs can better protect themselves in an increasingly digital landscape and shares some practical real-world advice.

We’re also excited to introduce Jerzy Biernacki to our team of contributors. In this issue, Jerzy explores the impact of Generative AI and its potential to revolutionize industries. His expert analysis sheds light on both the opportunities and risks associated with Gen AI.

Thank you for joining us on this exciting journey. Hopefully, you can take some time to relax and enjoy the issue.

Until next time… JP

CEO AND FOUNDER

© Stroud and Clarke Ltd 2024. All rights reserved. Whilst every effort is made to ensure that information is correct upon publishing, Stroud and Clarke Ltd is not responsible for any errors or omissions, or for the results obtained from the use of this information. All information in this magazine is provided “as is”, with no guarantee of completeness, accuracy,timeliness or of the results obtained from the use of this information. This magazine may not be reproduced or used in any manner whatsoever without the express written permission of the publisher except for the use of brief quotations in a book review.

100 MURTZ DAUD

Director of Data & Analytics at British Gas Business (BGB)

CEDRIC BERGER

Seeking for sustainable digital practices.

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GLOBAL EVENTS CALANDER The le

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Revolutionising the Future for Technology Leaders

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A bitesize collection of news, content, ideas, thoughts, and papers from around the industry.

Google is providing key capabilities for an artificial intelligence assistant for Volkswagen Drivers can ask Volkswagen's in-app assistant questions like “How do I change a flat tyre?” or point their phone cameras at vehicle dashboards to receive relevant information

Mobility-challenged travellers at the Sea-Tac International Airport have a high-tech new option for getting to their gates: selfdriving, electric wheelchairs.

Alaska Airlines is testing 10 of the mobility devices at the Seattle-area airport. More than 1,500 of the airline’s passengers have tried the wheelchairs since the initiative launched in mid August.

Workplace collaboration software maker Smartsheet will be taken private by buyout firms Vista Equity Partners and Blackstone in a deal worth $8.4 billion. Smartsheet shareholders would receive a cash payment of $56.50 per share, an 8.5% premium over the last closing price of $52.09.

Shares of the Bellevue, Washington-based company hit an over two-year high and were up 6% in early trading.

Nestlé is using virtual and augmented reality to offer immersive factory tours, allowing more visits without on-site travel. This approach builds trust with stakeholders while reducing the carbon footprint of physical visits

Tesla

Cybertruck Fuels

Political Divide, Dubbed ‘MAGAmobile’ and ‘Deplorean’

CYBER TRUCK

Tesla’s Cybertruck is making waves, not just for its futuristic design, but for its unexpected role in U.S. political culture wars. Dubbed the "MAGAmobile" and "Deplorean" by some supporters of former President Donald Trump, the Cybertruck has become a symbol of antiestablishment and right-wing sentiment.

This association may boost its popularity with a certain demographic, but not for the eco-friendly reasons Elon Musk likely envisioned. As the 2024 U.S. election nears, the Cybertruck is being embraced by some as a cultural statement, fueling debate around its symbolism rather than its technological innovations.

Kamala Harris Backs Crypto and AI in First Public Comments on Digital Assets

KAMALA HARRIS

Vice President Kamala Harris has made her first public remarks on cryptocurrency, signaling her support for the industry as the 2024 U.S. election approaches. Speaking at a fundraising event in New York, Harris pledged to back emerging technologies, including artificial intelligence and digital assets, as part of her vision for boosting U.S. competitiveness. “We will partner with labor, small businesses, and major companies to invest in America's future. We will encourage innovative technologies like AI and digital assets, while ensuring consumer and investor protections,”

Reflect Orbital Aims to Extend Daylight with Satellite Technology

REFLECT ORBITAL

Startup Reflect Orbital envisions a future where daylight is adjustable. The company is developing satellites equipped with large mirrors to reflect sunlight onto specific areas on Earth. By positioning satellite rings in sun-synchronous orbit, they plan to extend daylight before dawn and after dusk. Co-founders Ben Nowack and Tristan Semmelhack believe this extra sunlight could boost solar power generation, reducing the need for additional solar farms. Their goal is to increase the efficiency of existing solar infrastructure by enhancing the amount of sunlight reaching the ground, ultimately transforming how we harness solar energy.

Gary Donnelly

How Group CIO Gary Donnelly is steering Culina Group toward a data-driven tomorrow

Back in 2022, we spoke with Gary Donnelly to hear about the change programme he’s driving at Culina Group in his role as Group CIO.

Two years later, how is this progressing – and what’s in the pipeline? We sat down to talk transformation, tech and using data to drive value across a business with 16 brands and 5,000 vehicles.

Gary DONNELLY cULINA GROUP

OUTSOURCED IT SERVICES

• End-user Support Services

• Infrastructure services

• Network Services

• Cyber Security

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• Ticket Management

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• IT Project Services

• 3rd Party Incident Management

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• Detailed Analysis & Development of IT

An Apogee Company

Legacy to leading-edge: McCambridge Duffy’s digital evolution

Leading insolvency firm saves almost half a million pounds per year

McCambridge Duffy - The Upshot McCambridge Duffy are a leading insolvency firm in the UK, helping thousands of people and businesses to eradicate their debt, with a team of 140 staff.

McCambridge Duffy were yet to fully embrace digitisation. It was operating with a conservative mindset and legacy technology which hindered efficiency, accumulated unnecessary costs and placed barriers in the way of sustainability goals, printing over a million pages in-office annually.

With Apogee by the team’s side, McCambridge Duffy has significantly reduced its costs, almost doubled its client caseloads without the need to increase headcount, and retained key individuals with a seamless remote working approach.

The Outcome

Where specific documents still needed to be printed, this could now be handled by just one device.

With the additional integration of a stable IT system, creating a full end-to-end provision of Apogee services, has enabled the firm to shift to a long-term policy of remote working. As a result of the flexibility McCambridge Duffy is able to offer its employees, the firm has been able to retain its talented individuals in a market plagued by skills shortages.

The Solution

Apogee set about to digitise the operations at McCambridge Duffy. Its Digital Document Service was integrated with the existing case management system, which controls the digital journey of documents from a centralised location. Staff can now access documents from anywhere and send forms to different stakeholders to be signed online. With printing requirements drastically reduced, the firm slimmed down its device inventory from 6 multifunction printers to just one, and four scanners.

Apogee also took over the company’s IT infrastructure. Everything from the user licenses to accessories were rolled out. Devices can now be set up within an hour, rather than a few days, and the 13 remote desktop servers have been shrunk down to four, delivering huge cost savings directly, and cutting outages to zero.

“We knew we had to accelerate digitisation across the business to continue meeting the needs of our growing client base and also our staff, but our legacy infrastructure was holding us back.”

Michael Rutherford, Business Operations Manager, McCambridge Duffy

The Benefits

• Yearly costs associated with postage have reduced from £60,000 to £8,400 – saving the firm over £50,000

• The average monthly costs of £31,000 incurred by outages have now reduced down to zero

• Client caseloads have almost doubled from 5,000 to 9,500 without needing to increase headcount

• A meeting of creditors appointment now takes just 7 days to process, down from 45, an 80% reduction

• 90% of staff are now successfully working remotely or via a hybrid arrangement, leading to retention of key individuals who want flexibility

Watch the video case study

A JOURNEY TO CONSOLIDATION AND INTEGRATION

Atransformation of the scope that Gary is spearheading in an organisation the size of Culina Group takes preparation, exactness in execution and a great deal of stamina along the way. Thankfully, Gary has plenty of experience in all three – both from his wide-ranging professional career and from running over 10 marathons. Culina Group is one of the leading logistics companies in the UK, consisting of 16 business brands that provide chilled and ambient logistic solutions and industrial support services.

Having come together through a series of acquisitions, each brand used its own processes, technology and solutions. To create operational efficiency and security and support the organisation’s strategy, change was needed. And it’s already happening. While the full transformation programme is due to be completed within five years, the initial large-scale milestones are set to be hit within the next 12 months.

It began with rationalising their IT through a major outsourcing deal with Tata Consultancy Services. They’re now supporting the IT transformation and are responsible for the group’s IT infrastructure, operations, and service desk.

Printing and mobile services have already been standardised across the group. Consolidating HR and finance systems is next, with migration to new systems steadily progressing.

But there are even more parts to this period of transformation. These range from a warehouse programme to consolidate platforms to an integration programme using Boomi and a Transport Excellence programme, which looks at standardisation and optimisation of transport systems across the group.

The scale of change achieved so far perhaps belies the challenges posed by external factors. Particularly broader economic pressures.

Powering innovation through technology-driven transformation

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We’re using data to help each business see information in a consistent manner and allow our different business units to make decisions that are more group-focused rather than brand-focused.

Gary Donnelly

Embracing Disruptions and Thriving Amid Uncertainty with Digital Transformation for Scalable and Resilient Supply Chains

Revolutionizing Logistics and Supply Chain Industry through Technology Adoption

In today's hyper-connected world, the logistics and supply chain industry stand at a pivotal juncture. Technology adoption is no longer a choice but a necessity for survival and growth. The logistics and supply chain industry are the backbone of global commerce. Efficient supply chains ensure the smooth flow of goods from manufacturers to consumers, impacting every sector of the economy.

The advent of e-commerce has dramatically reshaped the logistics (3PL& 4PL) landscape, with customers expecting faster and more reliable deliveries. Additionally, with the increasingly complex and competitive current global market landscape, 3PL & 4PL enterprise leaders must embrace digital transformation for increasing efficiency, reducing costs, and enhancing customer experiences while ensuring resilience, and scalability.

As stated by McKinsey,

“70% of logistics enterprises are adopting digital transformation, reaping significant benefits. By embracing digital innovation, companies can enhance warehousing, optimize inventory management, and improve supply chain capabilities”

“Digital solutions can save up to 75% of lost sales in supply chain operations, significantly impacting a company’s performance. Companies adopting digital transformation have seen operational efficiency improvements of 20-30% and cost reductions of up to 15%”

Enterprises face persistent obstacles that hinder growth and efficiency. Challenges like inadequate employee management and supply chain disruptions can severely limit a company’s potential, often persisting despite traditional mitigation efforts. Third-party logistics (3PL) and fourth-party logistics (4PL) providers encounter significant hurdles, including fragmented data across disparate systems and silos that impede visibility and decision-making. Manual processes and outdated technologies exacerbate these issues, driving up costs while customer demands for faster, reliable deliveries add further pressure.

Digital technologies offer transformative solutions to these challenges. Integrated platforms unify supply chain data, providing real-time visibility and informed decision-making capabilities. Robotics and AI automate operations, reducing errors and optimizing costs. Advanced analytics and AI-driven insights empower 3PL and 4PL businesses to deliver personalized, responsive service that meets market demands.

Embarking on a digital transformation journey opens new opportunities for supply chain leaders. It enables agile adaptation to market dynamics through innovative business models. AI and machine learning enhance decision-making precision, while collaborative technologies foster efficiency and transparency across the supply chain ecosystem. Prioritizing cybersecurity, robust data governance, and sustainability compliance ensures secure operations. The integration of "humans and machines" with composite AI drives robotics and decision intelligence, offering avenues for progressive solutions and differentiation in the market.

The Road Ahead for Digital Transformation

Amidst economic volatility, geopolitical shifts, environmental challenges, and technological advancements, supply chain organizations face heightened productivity demands and cybersecurity risks, underscoring the need for proactive adaptation and resilience.

To navigate this complex landscape, technology leaders and executives should:

Establish Clear Governance Processes: Engage stakeholders to prioritize innovative technology adoption and transformation of the supply chain.

Adopt Advanced Digital Technologies: Invest in supply chain data governance, cyber extortion protection, and sustainability solutions to secure and enhance business operations.

Optimize Operations with Advanced Tools: Employ machine learning, humanoid robots, AI-enabled vision systems, and an augmentedconnected workforce for streamlined supply chain operations.

Manage AI Capabilities:

Implement rigorous governance to mitigate risks and effectively integrate AI into digital transformation strategies.

As 3PL and 4PL businesses embark on digital transformations, they anticipate numerous benefits:

- Enhanced Operational Efficiency: Automation and real-time data analysis lead to faster, more accurate operations.

- Improved Customer Experience: Real-time updates and personalized communication boost satisfaction.

- Cost Savings and Increased Profitability: Optimized processes reduce errors and enhance profitability.

- Scalability and Flexibility: Agile tools facilitate business growth and adaptation.

- Empowered Decision-Making: Advanced analytics offer actionable insights for informed decisions.

New Opportunities for Competitive Differentiation

AI-enabled Vision Systems enhance inventory management and quality control to automatically identify, track, and manage inventory, reducing human error and increasing efficiency.

Augmented Connected Workforce leverages Augmented reality (AR) and wearable technologies to empower the workforce by providing real-time information and guidance- enhancing productivity, safety, and collaboration across the supply chain.

Composite AI combines multiple AI technologies to address complex supply chain challenges with predictive analytics, intelligent automation, and enhanced decision-making capabilities.

Next-gen Humanoid Working Robots are revolutionizing warehouse and logistics operations- performing repetitive and physically demanding tasks, freeing up human workers for more strategic roles.

Machine Customers such as autonomous vehicles and smart appliances, are emerging as new stakeholders in the supply chain- enabling automated procurement and delivery processes while optimizing supply chain efficiency.

Espire Infolabs: Leading Digital Transformation in Logistics

Espire Infolabs leads digital transformation in the logistics sector, serving 3PL and 4PL enterprises for over two decades. Our approach blends deep industry expertise with advanced technology to drive significant improvements in logistics operations. We provide a range of solutions:

Warehouse Management: Enhances inventory visibility, streamlines order fulfilment, and boosts productivity.

Yard & Stock Management: Real-time insights optimize space use and increase throughput.

Migration and Upgrade Services: Smooth transitions to advanced logistics platforms.

Freight Management: Improves visibility, optimizes routes, and uses AI for smarter decisions.

Transport Management: Optimizes operations, maximizes fleet use, and enhances delivery accuracy while cutting costs.

24x7 Managed Services: Monitors systems, offers support, and drives continuous improvement.

Data, AI, and Analytics to predict demand, optimize routes, manage inventory, enhance CX & drive smarter decision-making

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Hyperautomation- leveraging RPA, ML & AI to automate complex processes, reduce costs, boost productivity, and enhance flexibility.

Partner with Espire for tailored solutions that redefine logistics efficiency, ensuring growth and success in today's competitive market.

Simrandeep Sethi

SVP - EMEA & NA

Espire Infolabs

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simrandeep.sethi@espireinfo.co.uk

BUMPS ALONG THE ROADMAP

The transformation programme is complex and considered. Gary explained, “The transformation is complicated not because of the technology but as a result of the change.” This is not just the natural human response to change but also the difficulties involved in defining exactly what each process involves and needs to deliver to ensure there are no unintended gaps.

“When you ask people to talk through their processes, they often express it in a way that simplifies it. “And if you don’t have the full picture when you start to bring the systems together, the danger is there will be gaps because it was never fundamentally defined to address the original process or solution.” This particular issue is easier to predict and mitigate, whereas the external factors recently affecting the sector have been more difficult.

“It’s been a really challenging business environment. Energy costs have had a big impact on our chilled business. Staffing costs have increased. And in the cost-of-living- crisis, there’s been a move away from fresh produce to frozen, which is not an area we work in. “We’re fortunate to have a strong group which offsets risks by having a diverse set of customers and products.

“You’re starting see chief supply officers become companies”

starting to even supply chain become CEO’s of

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A LOADING QUESTION

One of the areas of opportunity is using data to improve efficiencies by reducing the number of empty miles their vehicles have on the road.

“We’re trying to find ways to improve and optimise our cost base. We’ve got five thousand vehicles across the group. “Unfortunately, the vehicles are managed in very different ways across each of the business brands, using different systemic solutions as well as different business processes.

So, what we’ve been doing is investing in data visualisation” This is allowing the different brands to see where other vehicles from the group could pick up a load for them because they’re nearer and would otherwise return empty.

Not only is this creating more cost-effective miles, but it’s also reducing outsourcing costs and fuel usage. In future, it could lead to a reduction in the the number of vehicles in the fleet – which could bring big cost savings. With small margins like those in logistics, achieving these efficiencies is key. But it’s also about bringing the brands together from across the group.

“We’re using data from these systems to help each business see the information in a consistent manner and allow them to make decisions that are more group-focused rather than brand-focused.” Creating greater unity and consistency among the group’s brands is key here. And Culina Group is keen to realise this in other ways too.

“What we’re trying to do is take the winners and roll those out more globally across the group.”

ROLLING OUT THE WINNERS

The coming together of different businesses has created complexity, but it’s also bought opportunity. Specifically, an opportunity for ‘reverse acquisition’, to create standardisation across the business where it would bring the most benefits.

Gary explained, “What we’re trying to do is take the winners and roll those out more globally across the group. And also identify future opportunities.”

Gary has a good example. The group purchased Eddie Stobart three years ago. Every Stobart truck had a tablet within it. In the event of an incident, whether a breakdown or an accident, the driver can use the help button on the tablet to immediately call the insurer and rapid response team.

While the tech may seem simple, the resulting cost saving this button brings is significant. So much so that the group has now rolled out tablets into every truck across the group. The data shows it’s worth the investment.

REWARDING DATA

Data is something Culina Group has a lot of – but historically they’ve been data rich and information are poor. That too is changing. Gary, his department and the leadership team are looking at how to use data to drive not only decision making, but also to provide incentivisation.

“We’ve created a number of data warehouses – one each for transport and warehouse and we’re looking to develop one for finance.

“If we can combine those three data elements, you can get to some very rich information about how the functions are being optimised. But also, more importantly, rather than have standard rewards, we could start to incentivise people through gamification. “For example, say I’m picking 50 pallets an hour, and you’re picking 10. Maybe I should be rewarded because I’m more effective and more efficient than you are?” And that’s not the only way they’ll be looking to data to support their warehouse workforce.

“If we can combine those three data elements, you can get to some very rich information”

PLANS IN THE PIPELINE

Once the foundational elements of the transformation programme are in place, the plan is to determine how to use data and tech to help overcome issues with recruitment into their chilled warehouses and support the colleagues who already work in them.

Gary talked us through the theory of this, “Can we use data to further improve the layout and the performance of the warehouse? So, for example, what products need to be near the front because they’re picked more frequently, versus what products should be high up out of the way? “We also need to consider things such as how we can introduce physical robots, while making sure they’re secure so they can’t be hacked remotely.

We’re looking to see if we could have automation of the forklifts and wrapping machines, plus robots that wait for the picker to drop items into them.” He’s also casting his gaze much further to the future, when autonomous vehicles in logistics will become commonplace and, alongside that, an increase in unionisation of the workforce. For now, the team are actively looking into how to unlock more value from their data by

overlaying artificial intelligence. That could support them with predicting vehicle or traffic problems or interpreting information in the business to identify patterns or provide recommendations. All with the aim of finding more efficiencies in a sector where margins are extremely tight.

PUTTING PEOPLE AT THE CENTRE

Gary is keen to point out that the transformation is a result of the people in the business working together to create change. His view is, “I’m like Scotty in the engine room in Star Trek. My job is to get us from A to B; it’s somebody else’s job to determine what information they need and in what format they need it, to allow them to make business decisions.

“While I clearly have opinions and skills that I bring to this, I need to work alongside the operators. They’re better placed to understand their business contracts, their resources and so on.” To support this, there are working parties, pilots, and invitations for ideas, as well as regular communications, to keep colleagues in the picture. Keeping in contact across all levels of the business is key here.

GARY DONNELLY - CULINA GROUP

“You can imagine a football dressing room at the weekend keeps you very grounded!”

FEET ON THE GROUND

With so much to fill his mind at work, how does Gary unwind in his down time? While he’s no longer running marathons, he still coaches what was once a young kids’ football team. The team has now grown into older teenagers with Gary still at the helm. This might not sound relaxing to everyone, but it serves an important role for Gary – beyond the primary goal of supporting young people to participate in sport, improve their

fitness and build team player skills. Smiling, he tells us, “I’ve been blessed in my career to get to quite a senior level. You can imagine a football dressing room at the weekend keeps you very grounded!” Our Friday evening interview draws to a close on that note, with Gary preparing to change hats from CIO to football coach. They’re very different roles, but share a common focus of leading teams towards greater success.

GARY DONNELLY - CULINA GROUP

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A PARTNERSHIP FOR SUCCESS :

VOHKUS DRIVES STRATEGIC IT TRANSFORMATION AT CULINA LOGISTICS:

In today’s rapidly evolving logistics landscape, having the right technology in place is crucial for maintaining operational efficiency and driving business growth. Vohkus, a leader in IT solutions and services, recently partnered with Culina Logistics to deliver a comprehensive end-user device strategy that not only met their current needs but also set them on a path for future success.

The Challenge

Gary Donnelly, Group CIO at Culina group, was faced with the challenge of modernising the company’s end-user devices to support a growing workforce and increasingly complex operational demands. With a diverse range of roles across the organisation, identifying the right technology mix was essential for optimising productivity while keeping costs under control.

Strategic Collaboration

Understanding the critical nature of this project, Vohkus engaged with Gary and his team to conduct an in-depth review of their existing end-user devices. This involved analysing current usage patterns, identifying pain points, and forecasting future requirements. With a clear understanding of Culina’s needs, Vohkus then developed a detailed roadmap to guide the selection and deployment of new devices.

Tailored Solutions

One of the key aspects of the project was narrowing down the vast array of available devices to those that best fit Culina’s specific user personas. Vohkus evaluated products from several leading vendors, considering factors such as performance, durability,

and ease of use. The final selection process was guided by the goal of ensuring that every employee, from warehouse staff to senior executives, had the right tools to excel in their roles.

“Their ability to source kit in large quantities enabled us to offer even greater financial benefits, providing Culina with a high return on their investment.”

Cost-Efficient Procurement

Leveraging strong industry relationships and accreditations, Vohkus was able to secure significant cost savings for Culina. By negotiating directly with vendors and consolidating orders, they reduced the overall expenditure on new devices. Additionally, Their ability to source kit in large quantities enabled us to offer even greater financial benefits, providing Culina with a high return on their investment.

Seamless Implementation

Vohkus’ value to Culina extended beyond simply procuring devices. Recognising the challenges associated with large-scale rollouts, they utilised their stock holding facilities to secure goods and manage inventory in line with Culina’s project timelines. This strategic approach mitigated risks

related to lead times and availability, ensuring a smooth and timely deployment.

A Future-Proof Partnership

The successful collaboration between Vohkus and Culina is a testament to the power of strategic partnerships in driving business transformation. By working closely with Gary and his team, Vohkus not only delivered immediate improvements in technology and cost efficiency but also positioned Culina for sustained success in the future. As logistics companies like Culina continue to navigate the complexities of modern supply chains, Vohkus remains committed to providing innovative, tailored IT solutions that meet their unique needs and propel their businesses forward.

WORKING

IN PARTNERSHIP WITH FORWARD-THINKING ORGANISATIONS TO DRIVE SUCCESS IN THE YEARS AHEAD.

Vohkus take pride in their ability to deliver results that make a real difference. The partnership with Culina group is just one example of how they help businesses achieve their goals through strategic IT planning, cost-effective procurement, and seamless implementation.

For more information please visit vohkus.com

“What we’re trying to do is take the winners and roll those out more globally across the group.”

The

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Nonna Milmeister

Chief Data & Analytics Officer at RMIT University, discusses the challenges of data leadership in higher education and the importance of a data-driven strategy

RMIT UNIVERSITY Q & A with Nonna Milmeister

Background: Tell us a bit about yourself, your career, and your role at RMIT.

I feel I am fortunate. I moved to Australia more than 30 years ago, and it has been the best decision I’ve ever made for my children and me. With a Bachelor’s Degree in Engineering from Russia, I enrolled in RMIT University’s Master’s Degree in Project Management, which spearheaded my career in Australia. I began working for a consulting company specialising in project management but quickly realised I was more interested in data—its quality and the value it delivers to the organisation.

When the opportunity presented itself, I moved on to establish a Data Management Consulting practice in the same company and later held multiple roles in the telecommunications and insurance sectors. These ranged from managing data quality projects, developing data quality capability, and managing large transformational data migration programmes to establishing Data Management Centres of Excellence and developing data strategies for large organisations.

Five years ago, RMIT approached me to consider the role of Chief Data and Analytics Officer. I’ve thoroughly enjoyed working for RMIT and the education sector ever since, feeling grateful to give back to the institution that did so much for me when I needed it most.

In my role at RMIT, I developed and implemented a data and analytics strategy, and we are now expanding our strategy and roadmap into the next three-year period.

RMIT University:

Can you introduce us to the university itself?

I have a soft spot for RMIT University because it gave me the chance to become who I am today. I’m proud of RMIT’s history. It was founded in 1887 as the Working Men’s College, with 320 students on its opening night. Fifty years later, it had nearly 10,000 students. Today, RMIT provides education to more than 90,000 students across campuses in Australia and overseas, with over 11,000 educators and staff globally.

RMIT’s strategy, Knowledge with Action, focuses on using our knowledge, skills, and capabilities to make a difference in the world. RMIT’s vision is to be a leading university of impact in the AsiaPacific region, leveraging technology, design, and enterprise to create an inclusive and sustainable future. RMIT’s ambition is to lead internationally in four key areas: emerging technologies, smart and sustainable cities, social innovation, and regional collaboration.

RMIT is ranked 123rd globally in the QS World University Ranking and 5th in the THE Impact Ranking, including 1st globally for Reduced Inequalities. I believe RMIT’s greatest asset is its passionate people, who dedicate their lives to educating the next generation of professionals and researchers, creating an impact in the communities we serve.

Career Transition:

Can you elaborate on the specific challenges you faced when transitioning from project management to data quality management and how you overcame them?

When you truly enjoy what you do, it’s not a challenge but an opportunity. Working as a Project Manager teaches valuable skills such as bringing people together, resolving issues collectively, and leading teams. These skills proved invaluable when I saw the opportunity to shift my career focus to data management.

I was fortunate to meet Larry English, the “Father of Data Quality”, and I received my first certification in his information quality management methodology, which provided me with the tools needed for the job. I also developed a simple way to estimate the cost of poor data quality, which is crucial and serves as the first step towards improving data quality.

Most importantly, I was able to assemble a team of like-minded and dedicated individuals with knowledge in key processes, technologies, and people. With great team members who were experts in data quality and its improvement, and through the development of training programmes for different parts of the organisation, we created a recipe for success—one I’ve replicated throughout my career.

Stakeholder Management:

How do you approach convincing stakeholders, particularly in less data-driven sectors, about the importance of data governance and data quality?

That’s an excellent question, one that’s still debated at every industry conference. In my view, the number one critical success factor is having support from the Chief Executive Officer. Before accepting the role at RMIT, I was fortunate to meet with the then Vice-Chancellor and President, Professor Martin Bean CBE. His vision and deep understanding of the value of data in education convinced me to join RMIT, and RMIT executives and the Board have always continued to support me in implementation of the Data & Analytics Strategy.

Even with such support, influencing and collaborating with stakeholders remains a key responsibility for Chief Data Officers. What works for me is establishing governance that spans all levels of the organisation, from data stewards who focus on improving data for their respective areas to executive Data Trustees who are accountable for their data domains and make strategic decisions on data policies, the data life-cycle, data literacy, and other key components of building an active data culture.

When an organisation has solid data quality metrics, clear accountability for data quality, and when stakeholders are proud to resolve key data issues and feel supported by the organisation, you know you’re on the right track.

Additionally, selecting a few issues that can be addressed quickly and without significant investment can help convince stakeholders and gain their support. For us, the work on data definitions was one such success, and it paid off significantly.

Educational Impact:

What are some of the unique challenges of implementing data-driven strategies in the education sector compared to other industries you’ve worked in?

Compared to native digital industries and technology companies, the education sector has largely been a follower rather than a leader in creating data-driven organisations. For many years, educational institutions have focused on technologies to support learning, teaching, and research.

The role of Chief Data Officer, which has grown rapidly in other industries, was slow to be adopted in education. This is changing, however, especially with the rise of Artificial Intelligence (AI), Generative AI, and the realisation that data is a key component of future success. Data literacy is becoming equally important in both student education and staff development.

In fact, students now expect the same level of service from universities that they get from technology companies— quick responses to queries, easy access to information when needed, and real-time assignment grading.

At the same time, universities are home to academics with deep subject knowledge, including analytics and AI, and researchers dedicated to innovation and impact. Collaborating with these experts is extremely rewarding. A recent example of such collaboration at RMIT was the development of an AI Governance Framework, which involved input from across the university, including academics and researchers.

Data Stewardship:

Could you discuss the process of building and maintaining the coalition of 60 data stewards at RMIT? What strategies have proven most effective in getting buy-in from different departments?

First of all, we didn’t build it overnight. What was important to us was identifying people who were interested in ensuring that data supported, rather than hindered, business processes. We started with a single task: defining key RMIT terms and aligning our definitions across the university’s various processes. We formed a working group to focus on definitions, with around 20–25 participants, many of whom became data stewards. We now have more than 400 key terms approved by the Data Trustees, and this work continues.

Since then, we have established more working groups, and we now have four Data Stewards groups: Data Quality, Definitions and Reporting, Information Architecture, and Data and AI Risk Management. Each group works on different but related issues and they come together for

“Building a network, finding allies, forming a coalition like-minded people within your organisation will lead to success.”

network, and coalition of people organisation success.”

Q&A WITH NONNA MILMEISTER

showcases to ensure alignment. I view our Data Stewards as key partners in data governance. One of the biggest compliments I’ve received in my career was when someone told me that I had created a “buzz”, and that people across RMIT were talking about data and its value.

Ethical Considerations:

How do you ensure that AI and machine learning models developed at RMIT uphold ethical standards, particularly in terms of fairness, transparency, and bias?

RMIT’s AI journey is advancing rapidly, and we’re taking a two-layered approach: experimenting with AI while simultaneously establishing strong AI governance. Our AI Governance Framework represents a carefully measured approach to managing complex risks. It includes nine agreed

AI governance principles, defines roles and responsibilities, and outlines specific governance processes that RMIT needs to implement.

More than 20 staff members from across RMIT, including our academics and AI experts, contributed to the development of this framework, which has been approved by the Information Governance Board, a sub-committee of the ViceChancellor’s Executive, our highest executive forum. Additionally, a new procedure for Responsible AI is now a resource under the RMIT Information Governance Policy.

To ensure that everyone at RMIT understands their role in the ethical development, implementation, and use of AI, these key documents are supported by the Responsible AI@RMIT Data Literacy Module, which is available to all staff. We still have

a long journey ahead to embed responsible AI principles into all core processes at RMIT, but we’ve made a strong start, and we are already integrating these procedures into RMIT’s privacy and other relevant processes.

AI in Education:

Besides the capacity model, what other AI or machine learning initiatives are you excited about at RMIT, and how do you see them impacting the student experience?

When people talk about AI, they often focus on chatbots and Generative AI. These are valuable tools, and we’re already using them at RMIT. However, I always emphasise the concept of “fit for purpose” when discussing AI with stakeholders. For example, we developed a student performance dashboard that is based on more than 20 student cohorts. It monitors attrition and success rates and enables benchmarking. This provides excellent visibility into student performance across the university and helps inform decisions for improvement.

We’re also using a predictive machine learning (ML) model for student retention, which helps us identify students at risk of dropping out. Another use of ML is identifying students near graduation who have a high propensity for further study, allowing us to offer them appropriate postgraduate programmes. Not only has this ML solution enhanced the student experience, but it has also significantly increased admissions to postgraduate courses. Alongside our Information Technology Services colleagues, we developed a private generative AI chatbot called VAL for staff and students, and we’re now expanding it for use as a policy and procedure chatbot.

“Governance and ethics play a significant role in these decisions. When done correctly, governance isn’t red tape but rather an enabler and protector for achieving strategic goals.”

Another successful generative AI use case is survey analysis. This model saves time and effort by categorising survey responses and presenting them in a user-friendly format. Given that RMIT has surveys in the field almost every day of the year, this has been a major time-saver, allowing us to implement changes that directly benefit the student experience. All credit for these use cases, without any doubt, goes to incredible team of data professionals I am privileged to lead and the relationships Data and Analytics team built with our partners and colleagues across RMIT.

Advice for Aspiring Data Leaders:

For those aspiring to be Chief Data Officers, what specific skills or experiences should they focus on to prepare for the role?

Being a Chief Data Officer (CDO) is challenging but ultimately rewarding. It’s widely known that the average tenure for a CDO is around 18 months, often because the role is not well-defined or understood, and there are unrealistic expectations that long-standing data issues can be fixed in a short time. That’s not the case. Data quality, for instance, is not a project but a continuous improvement programme that requires leadership support, accountability, and ongoing effort. Similarly, managing data risks is a continuous task, much like managing cybersecurity risks. However, it’s a

rewarding role because you deliver significant value to your organisation, and your stakeholders will increasingly demand more of your services.

My advice to aspiring data leaders is to hone your influencing, presentation, and stakeholder management skills. These are essential in this role. While technical skills are important, curiosity, asking the right questions, and understanding how organisations work and how you can help are even more valuable.

Building a network, finding allies, and forming a coalition of like-minded people within your organisation will lead to success. Your key allies will include the Chief Information Officer, Chief Risk Officer, Chief Privacy Officer, and Chief Information Security Officer, as well as business leaders who rely on data and analytics to make decisions.

Additionally, partnering with leading data, analytics, and technology companies is crucial, whether large or small. We’ve partnered with Slalom, dbt, Snowflake, and AWS to deliver our data analytics platform, Data Foundry and MIP for critical data skills, and PwC for sensitive data cataloguing.

But most importantly, you must understand your organisation’s strategic priorities and current blockers. Aligning your data and analytics strategy with issues critical to your organisation will ensure your stakeholders promote your data initiatives on your behalf. That’s what success looks like to me.

Future of Data Management:

How do you see the role of Chief Data and Analytics Officers evolving in the next decade, particularly with the rise of new technologies like quantum computing and advanced AI?

There are high expectations that AI will solve many problems, boost productivity, and free people to focus on what they do best—thinking. I believe this is true, but only if the data used to train AI models is well understood, meets quality standards, is managed ethically, and is appropriately classified and governed throughout its lifecycle.

The role of the Chief Data Officer is to oversee all of that. Is your organisation monitoring its data quality? Does it have robust data governance? How mature is its data management? How well does it manage data risks, the data lifecycle, and data sharing? Do you have a data and analytics strategy that encompasses culture, data governance, data technologies, analytics, and AI?

The latter is a relatively new addition to the already broad responsibilities of CDOs, and they are increasingly moving towards roles like Chief Data and Analytics Officer (CDAO) or Chief Data and AI Officer (CDAIO). I expect we’ll see further iterations of these roles in the future.

Some companies are creating the new role of Chief AI Officer, while others are adding this responsibility to CDO or CIO roles. Whatever the case, this journey must be supported by the right priorities and funding to stay ahead of the competition. AI involves a substantial amount of modelling, which cannot be separated from data. At the same time, AI technologies are growing exponentially, requiring integration with source systems. All major technology companies are developing their own AI tools, forcing organisations to make nwell-considered decisions.

Governance and ethics play a significant role in these decisions. When done correctly, governance isn’t red tape but rather an enabler and protector for achieving strategic goals. I’m a strong believer that AI governance doesn’t need to be separate but should be integrated into existing data governance practices, data policies, and data literacy models.

THE DATA FOUNDRY - PARTNERING WITH RMIT UNIVERSITY ON JOURNEY FROM DATA TO

The Data Foundry (TDF) was founded in late 2019 on the principle of always starting with the customer and working backwards from there. We deliberately set out to design and build a company that was everything we looked for as customers in our former lives as technology decision-makers, and yet always struggled to find.

We are deliberately narrow in our focus specialising in all things data only. We stay within our swim lane and go very deep in around that data specialisation.

Over the last 4+ years, we have built a reputation as a “one stop data shop”, with expertise centred around the design, build and run of data-

driven services and solutions on the AWS, Snowflake and Databricks platforms. Our team is comprised of Data Engineers, Data Analysts, Data Scientists, Cloud Architects, Technical Business Analysts, Frontend Developers, a Chief Data & Analytics Officer, and Delivery Assurance specialists. Every one of our customer-facing consultants holds multiple AWS, Snowflake or Databricks certifications.

Since our inception, we have delivered close to 100 data-driven projects for tens of customers, including Universities, High-performance Sports Organisations, Federal Government Agencies, High Tech Manufacturing Organisations, State Government Departments and Agencies.

TDF enjoys a trusted, long-term partnership with RMIT University, to the point where we feel like a natural extension of their Data and Analytics team, allowing them to expand and contract elastically by using TDF to supplement their team when project demands exceed the internal team’s capacity. TDF is a small, agile, highly specialised, Australian-based company that has a proven track record of delivering high-quality, secure, performant, and low-cost data-driven solutions and services for a wide range of customers over the last 4+ years.

THE DATA FOUNDRY - PARTNERING WITH RMIT UNIVERSITY TO COMPRESS TIME TO RESEARCH OUTCOMES

EXECUTIVE SUMMARY

The Data Foundry (TDF), an Australian AWS, Snowflake and Databricks Partner Partner, implemented an AWS-based HPC platform to help RMIT University researchers visualise molecular structures 100 times faster, simulate photonic chips 10 times faster, reduce wait times, and gain better visibility into costs. TDF used Service Workbench on AWS to create the RMIT AWS Cloud Supercomputing Hub.

OVERCOMING THE LIMITATIONS OF AN ON-PREMISES HPC ENVIRONMENT

The Royal Melbourne Institute of Technology (RMIT University), founded in Melbourne, Australia, in 1887, is a leading public research university with 97,000 students. Named one of the world’s top 250 universities, RMIT focuses on art and design, architecture, education, engineering, technology, business, and communications.

For years, RMIT researchers relied on a distributed high performance computing (HPC) environment, which could not scale sufficiently to support increasingly complex research by both researchers and students in areas such as photonics, battery technologies, and geospatial science. “Many of our researchers faced compute, storage, and network constraints that impacted their research,” says Dr. Robert Shen, Director of RMIT AWS Cloud Supercomputing. “Some researchers couldn’t analyse multidimensional datasets or run large computationally intensive data modeling, and a few struggled to even run simulations using small datasets. We needed more scalability and permanent data storage options for researchers.”

RMIT also wanted to provide self-service HPC access to researchers, so they wouldn’t have to rely on external HPC facilities such as Australia’s National Computational Infrastructure (NCI), which allocates public research resources on a quarterly basis. “NCI is very competitive, and not all researchers can get access to resources,” Shen says. “Also, even if you do get resources allocated, you have to submit your job in a queue.”

RMIT sought to move to a cloud-based HPC environment to overcome its challenges. “We knew the cloud would provide scalability and on-demand access,” says Shen.

BUILDING AUSTRALIA’S FIRST DEDICATED CLOUD SUPERCOMPUTING FACILITY

Because RMIT wanted to offer the first dedicated cloud supercomputing facility at an Australian university, it needed to get its new HPC platform up and running as quickly as possible. “We understood that we had limited internal capacity to build our own environment quickly,” says Nick Balkin, Technology Program Manager at RMIT. For this reason, RMIT engaged with Amazon Web Services (AWS), which introduced RMIT to a specialist AWS data partner, The Data Foundry, an Australian technology and services solution provider.

TDF’s team worked directly with RMIT researchers to understand their needs around processing power, speed, and data storage. TDF then implemented Service Workbench on AWS, a solution that offers prebuilt AWS environments with scalable governance and security. With these capabilities, RMIT researchers have self-service access to AWS resources through a web-based catalogue of preconfigured environments. Through the Service Workbench research portal, researchers can upload their study data or software directly into Amazon Simple Storage Service (Amazon S3) for storage.

RMIT and TDF used Service Workbench on AWS to create the RMIT AWS Cloud Supercomputing Hub (RACE), which can scale from 10 Gbps to 400 Gbps, enabling significantly faster data upload times. AARNet provisioned connectivity to AWS from the RACE facility in Melbourne using AWS Direct Connect services. TDF partnered with RMIT to implement the Service Workbench solution in less than two months, working closely with the initial researchers to ensure they had all the required software and configurations to continue their research using RACE. RMIT became the first Australian university to go live with a dedicated cloud supercomputing facility. “We wanted to go live quickly because we knew we had an opportunity to build something here that was somewhat groundbreaking in the sector,” says Balkin. More than 600 RMIT researchers now use RACE, which opened in July 2022, to drive advances in research.

PARTNERING COMPRESS

ACCELERATING INNOVATIVE SCIENTIFIC RESEARCH

Relying on the AWS-based RACE platform, RMIT has the scalability and performance to drive faster research outcomes. Researchers can now access greater computing power on demand to address complex challenges in areas such as battery technologies, photonics, and geospatial science.

“RMIT researchers using the RACE platform on AWS are able to test ideas and solutions up to 100 times faster compared to our former distributed HPC environment approach,” Shen says. One researcher, Professor Michelle Spencer, is using RACE to analyse data and communicate a new, faster way to screen hundreds of potential molecules that could make electrolytes for lithium-metal batteries. “Professor Spencer can visualise molecular structures 100 times faster than with the on-premises environment, which means she can more quickly analyse how molecules impact each other,” says Shen.

Associate Professor Thach Nguyen at RMIT’s Integrated Photonics and Applications Centre is simulating photonic chips 10 times faster than before by using RACE. The tiny chips can plug into optical fibre networks to make the internet faster or plug into medical diagnostic tools to quickly analyse how cancer cells spread.

RMIT Professor Matt Duckham is using RACE to design new ways to automatically pinpoint a person’s exact location using only a verbal description of the features around them. With RACE, Duckham’s team can now process massive information streams including drone imagery, satellite data, and data from sensor networks.

REDUCING WAIT TIMES AND IMPROVING VISIBILITY INTO COSTS

RMIT researchers no longer need to wait in queues to access HPC resources. Instead of waiting up to 100 hours, researchers only spend a few hours provisioning compute and storage and setting up research parameters. “Rather than waiting and getting approval, our researchers can do their work in a few hours because they no longer need to wait on resources from NCI,” says Shen.

In addition, the RACE portal gives researchers visibility and control over cloud spend. “Our researchers can see their exact cloud resource usage by logging in to the portal,” says Shen. “As a result, they get more accurate cost estimates in a browsable service catalogue, which makes it easier to estimate costs and manage budgets.”

RMIT has since deployed additional AWS services such as AWS ParallelCluster, which helps researchers access more distributed computing. “The partnership with AWS and the RACE team has been a great example of our “one team” project approach. We are delighted that RMIT and AWS chose The Data Foundry to be their technical enablement partner and we are proud of the success stories from the researchers who are on a journey from data to insight at the speed of cloud – on the RACE platform,” says Brad Coughlan, founder and managing director at The Data Foundry. “We look forward to continuing our partnership with RMIT and RACE as we expand the university’s research capabilities on AWS.”

All Information correct at original date of publish, April 2023.

A Q&A with JERZY BIERNACKI

GENAI'S IMPACT ON INDUSTRIES:

Q: GenAI's transformative potential spans various industries. Beyond the obvious benefits, what are some of the less explored but equally impactful applications you foresee in the near future?

A: Ironically, the most impactful applications of GenAI aren’t always the most original or ground breaking. We’re still in the early stages of exploring what this technology can do, which is why many large companies – with their massive budgets and access to top business, strategy, and talent – are using GenAI primarily to streamline internal tasks like marketing or HR.

And that’s a smart move. Only by understanding the strengths and limitations of current GenAI can we lay a strong foundation for the future. In fact, even with these “obvious” use cases, there’s plenty to gain. Klarna is a great example. Their simple AI assistant handles nearly two-thirds of customer service inquiries, which equals the work of 700 agents and is expected to add $40 million in profit in 2024.

At this point, I’d highly recommend focusing on straightforward, seemingly obvious applications. These help organisations build both technological maturity and a clear understanding of GenAI’s security and usability. If we can’t give our employees an interactive knowledge base, we shouldn’t be trying to develop AI-driven sales chatbots for an online store. Otherwise, we risk disasters like the well-known airline whose chatbot gave out a non-existent discount, resulting in significant financial losses.

If I had to point out some under explored areas where GenAI could be effectively implemented with the right approach, it would be in tasks that involve analysing large data sets, merging insights from multiple sources, or identifying correlations between them. With the advancements in reasoning capabilities of models like GPT-o1 or Claude 3.5 Sonnet, I see tremendous potential in these areas. LLMs are getting better at understanding complex relationships, and this opens up new possibilities for datadriven decision-making and business intelligence.

MIQUIDO'S GENAI JOURNEY:

Q: Miquido has been proactive in adopting GenAI. Could you elaborate on the key lessons learned from your early forays into this space, and how they've shaped your current approach?

A: The key lesson we learned early in our GenAI journey is pretty simple — If the standard approach doesn’t work, don’t stick with it.

When GenAI started gaining traction and clients began asking how they could use it, we initially turned to the common tech stack – popular frameworks built to quickly develop LLM-based applications. But it quickly became clear that these weren’t ready for commercial use. Relying on them would have meant creating complex systems that would expose clients to high costs and frequent errors.

So, we took a different route and built our own toolkit for language model integration. In hindsight, we’d make the same decision, only faster. GenAI is still in its infancy, and figuring out how to implement it effectively in business is a work in progress.

That’s why some sort of creativity is key when adopting GenAI. On the business side, it’s about finding innovative AI use cases. On the tech side, it’s about assembling the right stack and, often, solving problems as they come up. Many of the challenges our developers and prompt engineers face don’t have ready-made solutions yet. Much of it comes down to trial & error, and hands-on experience.

OVERCOMING GENAI ADOPTION HURDLES:

Q:The path to GenAI adoption is fraught with challenges, from technical complexities to ethical considerations. How can organisations navigate these obstacles to ensure responsible and effective implementation?

A: Adopting GenAI is far more challenging than traditional software development. The technology is evolving fast, with new models constantly emerging, and regulations tightening. With laws like the EU AI Act in place, the "Wild West" era of AI is over. Even big players like Apple have had to delay AI features due to these new rules.

To implement GenAI successfully and responsibly, companies need to do one of two things: either build a toptier, in-house AI team with skills in development, architecture, business, and legal areas, or work with a trusted partner who can provide these skills. A proven strategy we've seen is team extension – bringing in experts like interim CTOs and AI developers to enhance your internal capabilities. This not only deepens AI expertise but also lays a strong foundation for future AI initiatives.

Take NOLEJ, a French edtech startup, as an example. Their goal was to use AI to make learning more personalised and accessible for students by automating course generation. They quickly realised offthe-shelf solutions weren’t enough, so they collaborated with us to build a custom AI system that generates highquality, relevant learning content. Along the way, they had to navigate both technical and legal challenges.

Thats why we developed AI Kickstarter – to avoid these risky compromises

By extending their team with outside AI expertise, NOLEJ was able to gradually strengthen their internal AI capabilities while focusing on their core mission of improving education. As a result, they grew their user base quickly and earned multiple prestigious awards in edtech.

The bottom line? Success in GenAI comes from having the right experts with real-world experience across technical, legal, and business fronts. With the right team in place, companies can unlock the full potential of GenAI and see longlasting results.

THE AI KICKSTARTER FRAMEWORK:

Q:Miquido has developed its own AI Kickstarter framework to address these challenges. Could you explain its core value proposition and how it empowers businesses to build robust GenAI solutions?

A:Our framework addresses the key technological, business, and security challenges in developing GenAI-based software. It’s a collection of best practices, AI tools, and architectural standards that enable the creation of reliable, secure, and cost-effective, production-ready products using language models.

The AI Kickstarter framework allows for quick integration with various language models like Gemini, GPT, Mistral, and Llama. It also provides essential resources to build robust AI applications: over twenty tools for effective data processing, guardrails for enhanced security, solutions to log system usage and

track key performance metrics, and a RAG (Retrieval-Augmented Generation) architectural standard to ensure precise outcomes.

From the start, we focused heavily on the business side of building GenAI products, particularly ensuring reliability and security at scale. One of the critical challenges we addressed was how to make sure GenAI systems are secure enough for commercial use. These systems need to handle a range of threats, from cyberattacks to overall data security, while also dealing with AI’s tendency to produce misleading or irrelevant answers – a common issue known as hallucination.

Most companies rely on open-source frameworks to build AI solutions. However, as someone with a PhD in Computer Science, I can say these frameworks often feel like a patchwork of outdated components stacked with new features. This results in rising technical debt, complex solutions that are hard to debug, and, ultimately, high costs. Worse, AI systems built on these platforms may lead to significant risks, like hallucinating chatbots giving incorrect or even harmful

BIERNACKI

advice, or making bad business offers that could cost a company money. Legally, AI outputs from a company can be binding, meaning any such mistakes could lead to real legal and financial consequences, which is an unacceptable risk.

Before AI Kickstarter, the only alternative was custom-built AI from scratch, but this too lacked the built-in security features, such as AI testing frameworks and evaluators, that are critical for safe commercial deployment.

That’s why we developed AI Kickstarter – to avoid these risky compromises. We built a framework from the ground up, with a strong focus on security and reliability for applications based on large language models. Our framework includes built-in features for data anonymisation, protection, evaluators, and guardrails. In AI Kickstarter, production readiness, data security, ethics, and high-quality, clear code that’s easy to debug are standard, not extra.

These pre-built components also allow us to significantly reduce development time, delivering faster ROI for GenAI projects. AI Kickstarter accelerates the process by nearly three times, reducing what would typically be a $40,000 project to around $15,000.

In short, AI Kickstarter is a trusted tool for businesses. It’s specifically designed for commercial deployment, offering the perfect blend of security, reliability, low costs, and fast ROI, making it an ideal solution for companies looking to harness GenAI effectively.

ADVICE FOR ORGANISATIONS:

Q: What's your key piece of advice for organisations looking to harness the potential of GenAI, while mitigating risks and ensuring long-term success?

A: My key advice for organisations looking to harness the potential of GenAI is to view it as a tool – a powerful one, but still just a tool. It can greatly improve efficiency, cut costs, and increase profits, but it’s not a magic fix for all business problems, especially those that haven’t been clearly defined.

The key to success lies in the sequence of actions you take. Before diving into GenAI, start by engaging with different departments to understand their specific needs and challenges. This is something I’ve seen used effectively by major companies like Uber; their Head of IT, Parul Saini, shared this approach during a Google Gemini panel discussion. Once those needs are identified, technical leaders – CTOs, Heads of IT, and so on – should evaluate which of these challenges can realistically be addressed by GenAI. Then, prioritise them based on potential outcomes, like financial returns, productivity improvements, or unlocking key resources.

Jumping into GenAI just for the sake of it, without a clear plan, can lead to more issues than it solves. The right approach is to focus on real business needs and organise your efforts around achievable, measurable results.

This is the message we share with all our clients. GenAI can be a fast track to business success, but only if there’s a clear vision of what success looks like. You need to define the key metrics to target and identify the core operations to enhance, all while planning carefully and involving the right people. It takes effort, but if done right, it’s a decision you won’t regret.

Lastly, if you ever need support, I’m always here to help. I frequently offer free consultations to companies looking to adopt AI, and I’m passionate about helping businesses use it effectively. Feel free to reach out anytime if you’d like to explore how GenAI can work for you.

Gen AI can be a fast track to business success

JERZY BIERNACKI

Tech Talks with CEDRIC BERGER

Head of Knowledge Extraction and Integration at Roche

Seeking sustainable digital practices.

01 02 AND SOVEREIGNTY

HISTORICAL PERSPECTIVE:

How do you view the evolution of the human brain and its impact on the development of technology? Are we seeing parallels in the way modern technology evolves?

The evolution of the human brain was guided by fundamental principles: maximize pleasure and minimize pain and energy spending. This is reflected throughout human history by the creation and use of tools designed to alleviate difficult or painful tasks including modern "cognitive tech".

Throughout human history, we observe parallels in the development, usage and societal changes cognitive tools have triggered such as the apparition of language enabling fire domestication, of writing facilitating accounting, the various graphical representations of information such as paper printing

sparking religious revolutions, and information integration such as the World Wide Web as a network of linked documents enabling business globalisation or, in its most advance form, a network of linked data supporting nowadays Artificial Intelligence (AI).

However, over the past 200 years, technological developments overly relied on the consumption of fossil energy, resulting in ecosystem pollution. This approach is not sustainable, necessitating preparation for substantial changes in our technological trajectory and energy use.

PROGRESS AND INNOVATION:

How do you define progress within the pharmaceutical industry, especially in the context of technology's exponential growth? Is there a philosophical boundary to innovation?

Progress does not only encompass advancements in technology but also science and social efficiency. Innovation specifically refers to the introduction of new ideas, methods, or products in an incremental or disruptive fashion.

The pharmaceutical industry, dating back to the mid-1800s, has experienced a different trajectory of technological innovation as compared to digital-native industries.

It is a multifaceted business spanning the secondary sector (drug manufacturing, high-throughput processes) and the tertiary sector (research, development, regulatory, and commercial divisions, along with support functions like HR, finance, and management). While the secondary sector has seen increasing

automation through mechanical robotization, artefacts flowing through tertiary activities went from paper to digital format. However, the underlying process hasn’t fundamentally changed since the 90’s. The pharmaceutical industry is not benefiting yet from the full potential digital transformation can bring.

Pharma innovation faces both general and industry-specific obstacles. General barriers include practical and material limitations, ethical considerations, epistemological constraints, and socio-cultural factors affecting adoption. Industry-specific challenges stem from the nature of the business itself: its direct impact on human health leading to a stringent regulatory environment, and some complacent conservatism born from historical profitability.

TECHNO-MESSIANISM:

The idea of "techno-messianism" suggests that technology can solve all human problems. Do you believe this mindset is relevant or potentially harmful in the pharmaceutical sector?

The concept of "techno-messianism" can be detrimental to both individuals and organizations. The tech industry capitalizes on the human brain's inherent tendency to minimize pain and efforts by inundating the market with machines and system assisting people and organizations.

However, over-reliance on poorly designed assistive technologies can downgrade human or organisational capabilities instead of augmenting them.

Let’s take a concrete example. The pharmaceutical industry, given its complex nature, employs thousands

of different IT systems, each operating its own vendor-specific internal logic. This results in the management of millions of datasets in diverse ways.

As a result, the data landscape is fragmented, disorganised, of various quality levels, and confined within the proprietary logic and databases of IT systems. This has led to the current situation where likely no single individual possesses a comprehensive, end-to-end understanding of the data landscape, hence of the business itself. The over-reliance on disparate and uncontrolled technological solutions has resulted in a loss of holistic insight.

COMPUTING POWER & PHARMA:

With the increased computing power and advancements in AI, how is the pharmaceutical industry leveraging these technologies to enhance drug discovery, development, and patient care?

The multi-faceted pharmaceutical business presents numerous opportunities for digital transformation: AI-powered drug discovery, smart adaptive clinical trials, drug manufacturing 4.0… Many of these are riding the current AI hype cycle largely built on the economy of promises. The critical factor is not the multiple potential uses of AI but how it truly adds value.

Not all pharma companies prioritise digital transformation in their business strategy. This requires a deep understanding of required resource mobilisation, disruptive consequences, and the required long-term commitment and support yielding no substantial return on

investment in the short run (see The Uncensored Data Strategy). Without a central top-down program, digital maturity often varies across departments and AI applications proliferate in a disorganised hence inefficient and risky manner.

Pharma executives often claim "digital isn't our core business”. But when did we last handle paper documents? This mindset, combined with the industry's inherent difficulties in innovation and the relentless external push of techno-messianism, makes it clear why many pharma companies are not fully realising the benefits of digital transformation.

AI & DATA QUALITY:

You mentioned that big organizations need clean data for effective AI, yet they hope AI will clean their data. How can we escape this dilemma?

It is a chicken-and-egg story and the egg (the data) came first. Everybody agrees that we need clean data, but nobody wants to either engage in a program to clean legacy data or to set the foundation to produce future clean data.

Why is that? I think human beings are geared to react to what they sense, particularly what is painful. If you don’t see or feel something, you don’t understand it hence, you don’t react accordingly. Even if reported in the news, catastrophes due to climate change happening far away from our modern urban comfortable way of

life don't incentivise us to change our habits, although we should. The same happens with unperceived data quality consequences by decision-makers until it hurts really bad (see recent fines in the bank industry lacking data governance).

For the pharmacy industry, the question is: when and what event will occur, that will be financially painful enough, to motivate decision-makers to sponsor long-term programs (triggering a cultural change) to put data quality as the strategic priority?

DIGITAL & BIOMEDICAL:

In an industry as highly regulated and complex as pharmaceuticals, how data and algorithmic advancements, including AI, support innovation and new insights generation?

Over the last three decades, technomessianism led to the proliferation of IT systems across pharmaceutical organizations. A typical big pharma operates thousands of systems, containing thousands of databases handled differently by different proprietary systems and logics. This creates a highly complex data landscape that is hardly understandable end-to-end.

If data is "the new oil," why focus primarily on the pumps and pipes (IT systems)? This complex data integration at machine level yields poor data quality at user level resulting in

inefficiencies amounting for hundreds of millions of opportunity savings or gains. The later are hard to identify and quantify precisely though without a dedicated solution to decipher the complexity of such a digital ecosystem.

Beyond primary business, poor data quality also secondary usage of data such as data science and nowadays AI. A system is as smart as the data available to it. Despite significant recent progress, Large Language Models (LLMs) don’t to reach their full potential due to the "garbage-in/ garbage-out" phenomenon. It's key to understand that there is no AI without IA - Information architecture.

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Despite significant recent progress, Large Language Models (LLMs) don’t reach their full potential due to the "garbage-in/garbage-out" phenomenon. It's key to understand that there is no AI without IA - Information architecture.

Cedric

Berger

Head

of Knowledge Extraction and Intergration at Roche

Start your knowledge graph journey today...

The pharmaceutical industry, given its complex nature, employs thousands of different IT systems, each operating its own vendorspecific internal logic. This results in the management of millions of datasets in diverse ways. As a result, the data landscape is fragmented, disorganised, of various quality levels, and confined within the proprietary logic and databases of IT systems. This has led to the current situation where likely no single individual possesses a comprehensive, end-to-end understanding of the data landscape, hence of the business itself. The over-reliance on disparate and uncontrolled technological solutions has resulted in a loss of holistic insight.

Cedric Berger Head of Knowledge Extraction and

Intergration at Roche

CRITICAL DECISIONS RELY ON HOLISTIC, CONNECTED AND CONTEXTUALIZED DATA

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REGULATION VS. INNOVATION:

Given that the pharmaceutical industry is highly regulated, how do you strike a balance between meeting regulatory demands and fostering technological innovation?

When collaborating with Health Authorities (HAs), there are two fundamental principles to follow: early involvement of HAs, and proactive transparency. These principles are particularly important when implementing innovative AI-driven solutions.

In the pharmaceutical industry, innovation typically follows a slow incremental path rather than a disruptive one. For instance, traditional testing and validation processes designed for deterministic solution fall short when it comes

to stochastic AI solutions. This gap necessitates to innovate internally and externally with Health Authorities.

As with any project, AI implementation initiatives should identify risks, disclose them transparently and develop comprehensive mitigation plans. An often overlooked but critical aspect are educational programs. These are vital in ensuring users will get the maximum value out of AI, minimising risks and alleviating concerns –whether founded or not – about AI and robotics replacing human jobs.

COMPLEXITY AND PROFITABILITY:

The pharma business is both complex and profitable. How do you manage this complexity while ensuring sustainable profitability in an era of rapid technological advancements?

The drug development and commercialisation effort has steadily increased over the last decades, with costs ranging nowadays from $1 billion to $2.6 billion and timelines extending to 10-15 years. This is mostly due to stricter regulations, longer R&D cycles, and high failure rates of drug candidates.

The industry is also grappling with a decline in the quality of foundational public research, partly attributed to the "reproducibility crisis" and a dysfunctional peer-review system favouring mainstream research and driven by the business of scientific publishing. This necessitates additional efforts from pharmaceutical companies to verify and complement public research findings.

Moreover, easily targetable diseases have been addressed with the understanding of the bio- mechanistic of about 1600 proteins. Remaining unmet medical needs relies on complex, multi-factorial/protein mechanism, making traditional drug discovery methods less effective.

As mentioned earlier, a genuine digital transformation that focuses on data rather than systems could address many of these challenges. However, this transformation is not yet considered essential because the pharmaceutical industry remains profitable due to its substantial financial momentum and frequent management manoeuvres, which unfortunately add to the complexity of the human, data hence knowledge ecosystem.

IMPACT-DRIVEN INNOVATION:

You’ve mentioned that your approach is "why first, then impact." How does this philosophy shape the way you lead your teams, and how do you align it with the goals of leveraging technology for transformative impact in healthcare?

We all seek for meaning in our lives, in our work. Starting with the question “why” serves several purposes: it helps determine what we do makes sense, it supports stakeholders engagement by clarifying the context. Context is crucial because everything is defined by its relationships and interactions (as Tim Berners-Lee notes). Without context—the “why” as well as other key questions like who, where, when, and how—you cannot define or understand anything.

Everyone sees the world differently, and only through discussions based on shared contextual understanding can we agree, especially regarding the concept of added value. This is where

knowledge graphs (KGs) can help as a powerful cognitive technology to support system-thinking and scientific thinking, two key methodologies to navigate our VUCA (Volatile, Uncertain, Complex and Ambiguous) world safely and efficiently.

I don’t see myself team leader but more a team supporter. Providing context transparently and explaining the “why” and the value of what we are doing are the foundation of any collaborative endeavour, especially when it comes to digital initiative. We tried to demonstrate the advantage of such an approach in our paper about Data Governance 4.0.

https://arxiv.org/abs/2311.02082

ENTREPRENEURIAL MINDSET:

As an e nt repreneur known for "getting things done," how do you navigate the intricate dynamics of a complex industry like pharmaceuticals? What role do people and data play in your decision-making process?

As an entrepreneur, my life is not easy. I need to navigate intricate dynamics, balancing two opposite forces: the established order's conservatism and the drive for innovation and change.

My strategy focuses on identifying early adopters (Innovation diffusion theory) and exclusively collaborating with them. Engaging with the majority is futile due to their significant resistance to change. Adopting new ways of working such as data-centricity, obvious but neglected methodologies such as

scientific- and systems-thinking, and cutting-edge technologies like KGs requires curiosity, courage and high organisational skills to work the extra mile. Additionally, these new approaches are perceived as threats by individuals who benefit from the complexities of the established order.

It's really about striking the right balance between established practices and innovative approaches and applying it surgically where it adds value. This is an art more than a science and require rare profiles with a dual digital/business expertise.

Additional information www.biomedima.org

Thanks to Stephane Manara for providing valuable insights. Thanks to Gilles Hubert for taking the photos.

GENERATIVE-AI

REVOLUTIONISING THE FUTURE FOR TECHNOLOGY LEADERS

In recent years, the rapid pace of technological advancement has made it clear that artificial intelligence (AI) is no longer a niche concept but an integral part of enterprise operations. Among AI’s most transformative fields is Generative AI, an area that has captivated industries due to its ability to create new content, innovate processes, and push the boundaries of what machines can accomplish. For technology leaders, the question is no longer "if" but "how" Generative AI will revolutionise the future.

WHAT IS GENERATIVE AI?

Generative AI refers to a subset of artificial intelligence that focuses on creating new data or content that mimics the properties of a given dataset. Unlike traditional AI, which focuses on identifying patterns and making predictions, Generative AI can generate new artefacts based on learned input. This could mean anything from synthesising realistic images and videos to writing convincing text or composing music.

Some of the most well-known implementations of Generative AI include large language models (LLMs) like GPT-4 (which powers this very article) and image generation models like DALL-E, which can create realistic or artistic images from textual descriptions.

However, beyond text and images, Generative AI is now making strides in coding, drug discovery, architecture, and personalised marketing, demonstrating its versatility across various industries.

THE BUSINESS IMPACT OF GENERATIVE AI

For technology leaders, the promise of Generative AI extends far beyond the novelty of creating art or automating routine tasks. Its true power lies in its potential to reshape business processes, customer engagement, and innovation fundamentally.

1. AUTOMATION AT A NEW SCALE

One of the most significant impacts of Generative AI is the automation of creative and complex tasks previously thought exclusive to human expertise. Content creation, from marketing materials to technical documents, can now be partially or fully automated using generative models. This increases efficiency and allows

businesses to scale their creative output exponentially.

For example, instead of relying on manual coding, developers can use tools powered by Generative AI to auto-generate code snippets, debug, or even suggest architectural improvements. This could reduce the workload of technical teams, speed up development cycles, and lower operational costs.

2. ENHANCING PRODUCT DEVELOPMENT

Generative AI opens up new frontiers in product development by allowing organizations to experiment with designs, formulations, and processes that would have taken months or years to develop using traditional methods. In sectors like pharmaceuticals, for instance, AIdriven models are already assisting in the design of new drugs by predicting molecular properties and generating potential compounds for testing.

In engineering and design, AI can create prototypes and offer alternatives that a human designer might not have considered, pushing the boundaries of innovation.

3. PERSONALIZATION AND CUSTOMER EXPERIENCE

In today’s digital economy, personalisation is a key differentiator. Generative AI can supercharge personalization efforts by creating tailored experiences at scale. From dynamically generated product recommendations to personalised marketing emails, AI can create content that resonates with individual customers based on real-time data and behavioural insights. This capability not only drives customer engagement but also significantly enhances conversion rates.

In retail, for instance, companies can use AI to generate personalized product recommendations based on the customer’s past purchases and browsing behaviour. In customer support, AI-driven chatbots can handle complex queries with contextually accurate and human-like responses, offering a smoother, more efficient customer experience.

EMERGING USE CASES ACROSS INDUSTRIES

As Generative AI continues to evolve, its applications are expanding across industries in ways that are reshaping both products and services. Here are a few cutting-edge use cases that demonstrate the transformative potential of Generative AI:

1. HEALTHCARE AND LIFE SCIENCES

Generative AI is being used in healthcare to develop new diagnostic tools, create personalised treatment plans, and even assist in surgical procedures. For example, AI models can analyze medical images to identify abnormalities or suggest treatment options based on patient data. Generative AI can also be used to synthesise data for rare diseases, helping to bridge the gap where large datasets may not exist.

In drug discovery, AI has become an invaluable tool for predicting how new drugs might interact with the human body, thereby reducing the time and cost of bringing new treatments to market.

2. ENTERTAINMENT AND MEDIA

Generative AI is pushing the boundaries of content creation in the entertainment industry. From generating movie scripts to creating photorealistic scenes in gaming, AI models are increasingly being used to assist or completely automate creative processes.

Deepfake technology, for example, uses Generative AI to create hyperrealistic video and audio, allowing for innovative approaches in marketing,

content creation, and storytelling. While the ethical implications of deepfakes are hotly debated, the underlying technology showcases the immense power of Generative AI in creating realistic media experiences.

3. MANUFACTURING AND DESIGN

In manufacturing, Generative AI helps companies optimize design and production processes. For instance, AI can generate design variations based on specific constraints, such as material costs or environmental impact. Engineers can then choose the most efficient or sustainable design, streamlining the innovation cycle.

Additionally, in industries like automotive and aerospace, AI is being used to design parts and systems that are lighter, stronger, and more efficient, helping companies achieve greater performance at lower costs.

4. FINANCE AND RISK MANAGEMENT

Financial institutions leverage Generative AI to improve risk assessment, fraud detection, and decision-making processes. By generating synthetic data, AI models can simulate market conditions, test investment strategies, or identify vulnerabilities in portfolios.

For insurance companies, Generative AI can create detailed risk profiles for individual customers based on their behaviour, health data, and other factors. This enables more accurate pricing models and better risk mitigation strategies.

CHALLENGES AND CONSIDERATIONS FOR TECHNOLOGY LEADERS

As with any transformative technology, the adoption of Generative AI comes with its share of challenges. Technology leaders must approach its implementation thoughtfully, weighing both the benefits and potential risks.

1. ETHICAL CONSIDERATIONS

Generative AI raises significant ethical concerns, particularly around the creation of deepfakes, intellectual property theft, and the potential for misuse. AI-generated content that is indistinguishable from humangenerated content can be weaponised to spread misinformation or defraud consumers.

Tech executives must establish clear ethical guidelines for the use of Generative AI in their organisations, ensuring that its deployment aligns with legal and ethical standards.

2. DATA PRIVACY AND SECURITY

Generative AI relies heavily on vast amounts of data to train its models. This raises concerns about data privacy and security, mainly when dealing with sensitive information like personal health records or financial data.

C-level leaders must ensure that data governance policies are robust and that AI models are compliant with regulations such as GDPR and HIPAA. Moreover, transparency in AI decision-making processes is needed to build trust with consumers and stakeholders.

3. TALENT AND SKILL GAPS

Successful implementation of Generative AI requires specialised skills, from data science and machine learning expertise to ethical AI governance. However, a global talent shortage in these areas makes it difficult for organisations to find the right talent to manage and deploy AI initiatives effectively.

To bridge this gap, technology leaders may need to invest in upskilling their workforce or partnering with external AI vendors and research institutions.

4. MANAGING EXPECTATIONS AND ROI

While the potential of Generative AI is vast, it’s essential to manage expectations realistically. Not all processes are ripe for automation, and not every application of AI will yield a clear return on investment (ROI) in the short term. Leaders must take a long-term view, carefully identifying the areas where AI can provide the most value and ensuring that resources are allocated strategically.

PREPARING FOR A GENERATIVE AI FUTURE

The future of Generative AI holds immense promise, but technology leaders must be proactive in shaping how it will impact their organisations. Here are some steps C-level executives can take to prepare for the Generative AI revolution:

Develop a Clear AI Strategy: Generative AI should be part of a broader AI strategy that aligns with the organisation’s goals and risk tolerance.

Invest in Infrastructure: To succeed, AI initiatives require robust data infrastructure, cloud computing resources, and cybersecurity measures.

Foster Innovation: Provide teams with the resources and autonomy to innovate by encouraging them to explore and experiment with generative AI.

Build Ethical Guidelines: Establish clear policies around the ethical use of AI, focusing on transparency, accountability, and fairness.

CONCLUSION

Generative AI is not just a passing trend; it is a revolutionary technology that reshapes industries and redefines how businesses operate. It offers technology leaders an unprecedented opportunity to drive innovation, improve efficiency, and create personalised experiences at scale. However, it also presents challenges that require careful consideration, from ethical concerns to data privacy and skill gaps.

By strategically and thoughtfully embracing Generative AI, organisations can position themselves at the forefront of the next wave of digital transformation, unlocking new possibilities for growth and success.

STATS YOU NEED exec.index

By 2025 it is estimated that 85% OF CUSTOMER INTERACTIONS will be managed without human intervention, thanks to technologies like chatbots and virtual assistants.

(Source: Gartner)

The global telemedicine market is projected to reach

$185.6 BILLION by 2026, growing at a CAGR of 23.5%.

(Source: Fortune Business Insights)

FinTech investment globally reached $105 BILLION in 2020, with a 17% year-over-year growth.

(Source: KPMG)

E-commerce sales worldwide are projected to reach $6.54 TRILLION by 2022 accounting for 21.8% of global retail sales.

(Source: eMarketer)

The use of Artificial Intelligence (AI) in healthcare is expected to generate $150 BILLION in annual savings for the U.S. healthcare economy by 2026.

(Source: Accenture)

Digital payments are expected to reach a transaction value of $6.7 TRILLION by 2023.

(Source: Statista)

WEARABLE DEVICES

in healthcare are estimated to reach 105.4 million shipments globally by 2023.

(Source: International Data Corporation)

Blockchain spending in the financial sector is expected to exceed $16 BILLION annually by 2024.

(Source: International Data Corporation)

The global smart manufacturing market is expected to reach $479.01 BILLION by 2025, growing at a CAGR of 10.7%

(Source: Grand View Research)

The adoption of Internet of Things (IoT) technologies in manufacturing is predicted to reach $1.4 TRILLION in value by 2030.

(Source: McKinsey & Company)

Industrial automation is anticipated to lead to a 33% REDUCTION in manufacturing costs by 2025.

(Source: Capgemini)

67% OF MILLENNIALS prefer to shop online rather than in-store.

(Source: BigCommerce)

These statistics highlight the transformative impact of technology in various sectors, shaping the future of retail, healthcare, finance, and manufacturing.

MURTZ DAUD Director of Data & Analytics at British Gas Business (BGB)

Curiosity, culture and solving complex problems using data

In conversation with MURTZ

DAUD

Director of Data & Analytics at British Gas Business

(BGB)

People-centric leader. Passionate about evangelising data. Pursuer of positive change through datadriven strategies.

Murtz is on a mission to develop a team and an environment that’s empowered to drive value through data in his role as British Gas Business’ Director of Data & Analytics.

Murtz already has a successful history of delivering innovations and opportunities to drive value with data within organisations and establish best practices for ‘single source of truth’ data.

His many successes so far have included transforming organisations across various industries into being truly value-driven through data, focusing not only on technology but also on enhancing data culture and data fluency across businesses.

Welcome Murtz, it’s a pleasure to have you here today. Firstly, we’re curious to find out more about your data career so far.

It has spanned many sectors and resulted in real-world improvements for patients and customers. Can you tell us what drove you towards this career and the path you’ve taken to get to where you are today?

Of course. I studied for a degree in business information systems, which gave me the option to go into tech or business. I wanted to keep my options open as I was unsure at that stage what I wanted to do. Luckily, one of my placements during my degree was in data, covering for maternity leave, so I was doing a real job. Data wasn’t seen as such an important strategic asset in 2007, but even then, I could see the value it brings.

Following the global market crash in 2008, people really started looking at data. So, I came into the industry at the right time and in a role where I was creating technical data solutions. I have worked in various industries, including healthcare, financial services, and investment.

I also undertook some contracting. This varied from going into organisations and being hands-on to completing organisational redesigns and setting up data departments from scratch.

I had a fortunate opportunity to do an Executive MBA a couple of years ago. I value that experience so much because it not only improved me as a leader, it also helped me understand different environments, industries, sectors and departments.

It taught me about marketing, business finance, operations and strategy in such a way that I can actually now start to think about how we can help in those areas without them coming to us. More recently, I’ve moved into the energy sector for the first time as Director of Data & Analytics at British Gas Business.

It sounds like you relished being hands-on in creating solutions. Do you miss that as a leader?

I really liked the exciting buzz of solving complex problems with technical solutions – loved it, absolutely loved it! But I moved into leadership many years ago and I’ve never looked back. The buzz I used to get from developing solutions myself has been completely replaced by the buzz I get from developing people.

I have a people-first approach –supporting people to grow and self-actualise is an amazing feeling and I’m a huge fan of personal development for my teams. It’s the thing that gets me up in the morning and makes me into who I am.

It’s easy to hear how passionate you are about people. But when it comes to you and your team implementing data-driven solutions, what do you think are the key barriers to organisations? And how have you overcome them?

People think technology is the biggest barrier, but technology is actually a little more predictable. The biggest challenge can be something as trivial as how do you get people to use your data and analytics solution in the first place to actually harness the value it may bring?

I see myself more as a chief adoption officer. I need to evangelise data and showcase its value – and almost ignore the word ‘data’ because nobody cares about data apart from data people! Users and stakeholders want value, and I try to adopt a value-driven approach.

For example, in my role at St Andrew’s Healthcare, my team and I delivered analytical solutions to help clinicians tailor treatments for patients, helping to improve care. Looking after the patients was central. Here at British Gas Business, clearly, we don’t have patients, but we do have customers, and again our aim is make their lives better.

I’m obsessed with customer impact and value generation. Everyone wants to be data-driven, but actually you can be data-driven and that results in zero value. You need to be valuedriven, of which data happens to be one of the assets that you can leverage.

I think expectations can also be a challenge. If you look at the last year or two, everybody wants Gen AI, but you need to ask the question of why you want it, what areas of business will gain the most value from it, and do we have the basics such as Data Governance and Quality right first?

Managing expectations is crucial to make sure we do things for the right reasons, in the right way that’s sustainable. We also need to explain that, for example, after implementing a solution, the day one value might be 10% of day 365 value, so stakeholders are clear on what to expect and when.

How do you go about addressing that resistance that you noted some people have towards change?

In data, we are still seen as disruptors. People sometimes want to go on gut feel based on their experience in that sector or in their role, rather than to be informed by data. I still remember a previous role within the healthcare sector where we’d created some really important solutions for improving patient care. I had a conversation with a doctor about it, but he wasn’t open to it. He’d been doing his job for 20 years, whereas I wasn’t a healthcare professional, so there was an element of ‘Who are you to tell me what to do?’

So back then – and still now – it’s my job to articulate that we are not replacing human judgement. We are augmenting it. We are providing our users with all the information at their fingertips to make that final decision. To help overcome change resistance, I want as many people as possible to move towards becoming supporters and advocates instead of critics or neutrals. I need to strategically leverage the existing advocates and supporters to do that.

I learned this the hard way! I quite often used to go on stage and showcase the product that my team had built – for example, talking to financial experts about a financial model. But as I wasn’t part of their world, they were sometimes apathetic about it. I learned to leverage other influential people who will be listened to and are part of that world to do the talking on my behalf.

You need to build that guiding coalition of influential people. You need a volunteering army from every level of the business, from the shop floor to the boardroom, who can showcase the value of the data strategy that you’re looking to build and who will communicate and drive adoption with you. It’s about having tactical ploys on how you disseminate communication to drive adoption. Creating a culture that embraces change is a challenge that will be familiar to many of our readers, so the point about helping to augment human judgement through data is really pertinent.

You mentioned that tech is simpler than overcoming change resistance, but does it present any challenges?

It isn’t the hardest challenge, but it is still a challenge. The landscape is ever-changing. We’re setting up mechanisms to constantly be ahead of the curve. Even though we may not be able to leverage that tech today, being aware of it and adapting to it will be useful for the future. Otherwise, if you completely ignore that tech, you’re going to be so behind the curve that when you need the new technology, you’re already two years late. That constant learning and improvement culture is extremely important. This can be done through internal training and development and through external networking with like-minded peers in Data and Analytics.

And what about today’s tech? You mentioned Gen AI. Where are you with that?

People are really getting disrupted by Gen AI, including, for the first time, data people themselves. The

disruptors are being disrupted. I think you need to crawl before you can start jumping to the moon and that’s the most important thing at the moment. I referenced earlier that there’s a push for Gen AI, so I’m almost using Gen AI as the lever to focus on the less-desirable basics first, such as data governance and quality and data fluency in the business, before getting to the exciting stuff.

Being in a role that people look to for innovation and creativity, then having to say, “But hold on, we can’t do the innovation and creativity until we fix the foundations of data governance and quality because, otherwise, we’re building on quicksand”, can be quite a challenging dichotomy. Especially as I’m usually innovation-first!

We also need to be very strategic about it. How does it help the business today? And tomorrow? How can we do it in a responsible and ethical way?

You must have seen Jurassic Park? Jeff Goldblum’s character makes a great point that’s so pertinent to us today. He said, “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” Today, there are so many things we could do with Gen AI, but we need to balance that with what’s the right thing to do for our customers, our people and our planet.We need to make sure to do it in the right way, keeping an eye on ethical and moral boundaries, as well as security and confidentiality. You must have an ethics committee that can review every initiative. You need those governance processes in place to balance external regulations against what you really want to do as a business.

You spoke about innovation and creativity there. How have you harnessed creativity and knowledge from across different departments of an organisation to help boost innovation or create better ways of working?

I think people sometimes overlook that data isn’t a technical problem. It’s not ‘digital change’- Data is now in a place where it is ‘business change’. So, with that in mind, you want people with soft skills. Communication is inherent to success, as is stakeholder management, engagement and relationship building. Problemsolving, a growth mindset and curiosity are also important.

I want to build that capability internally. It’s not just about getting certified technical skills. It’s about getting the right people that can give you the right qualities to be successful.

The biggest part of my career is developing people and I’m a firm believer you can find the right people from anywhere. I want to find people hidden within the organisation who have the soft skills and the inherent qualities required in data analytics and grow them into technical marvels. Because, whilst the hard skills required in data and tech can be taught with a comprehensive training plan, the softer skills can be more challenging to develop.

This all helps to build a diverse team that deeply understands business problems and addresses them through innovative and creative solutions.

Can you tell us more about any of your future areas of focus in your current role?

We’re looking at how we can leverage data analytics to optimise our customer experience and customer journeys, optimise energy usage, save costs and be more sustainable going forward for a fairer and greener future with a triple bottom line approach – not just people and profit, but planet as well. Our data strategies need to be in line with that. And we want to drive innovation and let diverse voices be heard. I am a firm believer that innovation doesn’t live in the boardroom. Innovation lives on the shop floor. That’s where the great ideas are.

Cost can be one of the biggest barriers in any organisation, but there’s an Indian concept called jugaad, which is about solving complex problems without the need of an army. How can we do that here?

Because actually innovation isn’t just about shiny, expensive technology. Innovation is about fixing a real-life problem that is causing an individual pain on the shop floor. That for me is innovation – using creativity to fix that problem. At St Andrew’s Healthcare, I protected time for that innovation to make sure people could have that safety to play. I’m already starting to think about how we can do that here at British Gas Business as well. At St Andrew’s Healthcare, I protected time for innovation to ensure people could have that safe space to play. I’m already starting to think about how we can do that here at British Gas Business as well.

“Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.”

Jeff Goldblum - Jurassic Park

We need to focus on automating and streamlining processes so we can free up time to do the fun stuff that we really want to do. For example, how do you create more capacity to reduce the burden of BAU and focus on innovation?

It’s not just about bringing in more people; it’s also about looking at trivial things like using time in the most effective way. We then need to be disciplined enough to use that extra time to focus on innovation and not fill it with more BAU work. Because I'm not the person who does innovation. I'm the person who enables innovation, and I think that's the key thing. We in data create products for the business to be innovative – we enable that. Having that very clear guideline is crucial as it empowers people from other departments to be innovative and bring us ideas to innovate around. We would mobilise this to be a culture change. It has to be a cross-functional, organisation-wide

analytics, and Gen AI, you need a firm foundation or the solution will sink at some point and the ramifications could be catastrophic. I also take a culture- and peoplefirst, tech-second approach – because tech is the backend that provides the value. How you use the tech is the real difference-maker.

Of course, you need to understand your value-added initiatives completely. For example, the ratio between revenue generation versus cost optimisation at every level of granularity. Plus, you need to link what you're doing with the organisation’s strategy. Quite often data strategies can be in silos, but it’s important to have a clear link to the organisation’s strategy and communicate what that is.

And lastly but certainly not least, can you share a little more about the traits that shape you as a leader?

With that in mind, can you provide a quick overview of how

data-driven strategies?

All of my strategies start with the basics – the culture, the organisation and whether we’re actually treating data as an asset. By ‘an asset’ I mean are we governing it and are we ensuring its quality? Those are the two key things for me because it’s those foundations that you build from to build innovative solutions through business intelligence, data science, predictive

One of my traits is curiosity. I ask so many questions. If someone says they want a dashboard, I want to know everything from how they’re going to use it to what the success metrics are. I want to put myself in their mindset because then I can help them better.

I think another trait that’s absolutely crucial is adaptability. Especially in the changing landscape of people, culture and technology, you need to be always adaptable and on the front foot. I'm also constantly motivated and I hope that my energy and my passion for the role transfers over to my team. I love working with people and seeing the value at the end of our hard work.

“I see myself more as a Chief Adoption Officer!”

Murtz Daud

In partnership with

Saving &

Saving Time Money with Technology Kathleen Hurley

A FIRESIDE CHAT

We sit down with Kathleen Hurley, Founder of Sage Inc in New York, to discuss her unique approach to technology and business in the cybersecurity space.

From English major to tech communications specialist, your career path is unique.

How did your background in English shape your approach to technology and business?

I think there are 2 threads that are worth pulling here. One is that I wasn’t just an English major -I was an English major intending to be a teacher. This theme revolves around education and the translation of ideas. No matter what room I’m in, I find myself explaining some esoteric nerdy nonsense to human beings in human being language because I speak both languages, and I love that. I can come up with some crazy analogies! The latest one I remember compared a tired three-year-old’s pre-logical brain to the way an AI can sometimes go off-track, given enough iterations without re-training.

The other thread that could apply is the theme of communication.

I believe that computers and the technologies that connect them together, whatever other good and bad they bring to our lives, help us to communicate. They are tools that democratise the spread of thoughts, just like the printing press was some few years back. Just like the printing press, these tools are used for good and not so good. In my view, those of us who understand how people communicate with each other and who also understand how computers can work have a responsibility to try to help each other exist safely and to try to understand the possibilities.

You’ve witnessed significant technological shifts, from the early days of email to modern cybersecurity challenges.

What key factors do you use to differentiate true innovation from industry hype?

I really have! When I started my first real job after college, nobody was sure that e-mail was going to have large implications for business. What was wrong with the fax machine!? This is where the MBA comes in handy, I think. Having taken a very global program, I was fortunate to sit in Zurich and then study in the United States at an HBS class! No irony there, honestly, because it was an amazing program about the Microeconomics of Competitiveness. Some people say this doesn’t really apply anymore, but I disagree. When clusters of businesses begin to look a certain way, look in a certain direction or gather in a certain area, there’s something happening. I pay attention. Some think it may be harder to see these days because we don’t operate in a physical world all the time. I think it’s easier because I don’t have to be in Manhattan all the time to see it.

Another differentiator is how many cycles out of the gate we’re seeing focus on one factor versus how much wide-spread conversation is happening about adjacent topics. For instance, with AI, we are not focused just on Chat-GPT. We are talking about data centers, cooling, transit, and all manner of adjacent technologies, logistics, and life impacts. That isn’t hype; it’s long tail. It doesn’t mean it will work out, but it’s not hype.

How did pursuing an MBA with a global perspective at the University of Wales broaden your understanding of technology's role in different cultural and business contexts?

I think that the program of study acted as intended. It forced me to think about technology in the context of business strategy, as any good MBA should. Then the layer of globalisation made me think about business strategy in the context of the different perspectives we bring to our day-today lives. Because it was a well-designed program that encouraged a great deal of interactivity among the students and with the professors and required a lot of each of us in the program, those differences naturally became apparent. It was embedded within some cultures that bribery and graft were required in order to get permitting and functional work done. In some cultures, DEI was a no-go if approached overtly but could be handled covertly within sub-cultures. As we began exploring the nuance, it became (as it so often does, I think) apparent that we have so, so much more in common to bring us together than we do differences to drive us apart. That inspires me and drives me forward every day.

It forced me to think about technology – in the context of business strategy

Navigating through crises like ransomware attacks, SEC audits, and the global pandemic is no small feat. What leadership principles or strategies do you rely on in critical moments to keep businesses on track?

Oh, it was nothing (waves hand). That was not a small year! I think there are two core things that are helpful to keep in mind no matter how much is going on at once. First, it’s all possible to break down into smaller problems, and you can work things one stage at a time. If you’ve done a reasonable job learning the foundations of, for example, cybersecurity, then you will probably see even the largest problem in terms of its foundational principles, anyway.

Given that baseline education, it’s likely that you set up the cyber program with a lot of riskbased and reasonable monitoring, prevention and mitigation measures in the first place so that when the bad day does come – as they do – you are not as poorly off as you might be. This is one of the things I’m pleased to bring to our customers at Sage, who are small businesses and have often been in a really reactive position on a bad day. We don’t want them there. We can help them stand as ready as the biggest firm, thoughtfully prepared, and perhaps even better prepared than those big guys because they care a great deal about every one of their customers, just as we do. We all want to keep each other safe and well. Caring goes a long way to doing cybersecurity well, in my experience.

That’s the second thing which I learned through my experience working with, I believe, the finest Compliance team ever to comply. We sought to protect the company from risk in a very reasonable and thoughtful manner, and I believe that we extended a high level of institutional thought and protection to a firm that is a class act. It was a privilege to work with this group – we helped each other reach for our best. What I learned, and take as a strategic leadership principle, is that you can outperform your wildest expectations and generate some amazing feats of business through the magical power of caring about your team, your clients and your business.

If you combine those two things—a really firm foundational education and a really basic level of care and investment in what you are doing and who you are doing it with—I think you come up with a recipe for a delicious business.

It was a privilege to work with this group – we helped each other reach for our best

You have been passionate about process definition for decades. Could you share an example of how your process-driven approach transformed a company’s operations or profitability?

When I was but a young(er) IT professional, I was honoured to serve at CBRE in the Mid-South region with a team of dedicated Asset Management and business strategy professionals who were very smart, had an incredible level of integrity and saw the GFC coming. One of the things that we undertook was an operational review to see where we might find synergies and solutions that could help us go faster, move smarter and harness technology and new ideas. It was brilliant. We were able to define the work that we were doing and how we were doing it, and with the blessing of senior leadership, we were able to brainstorm and transform the business – not only with technology but certainly that was a big part of it – ahead of the storm. I see us, culturally, at a similar moment now with the advent of AI. I think that the opportunity exists for businesses to take a serious look at where they are and what they are doing and define themselves again before the business cycle defines them.

Cybersecurity and compliance are crucial in today’s landscape. What have you learned about seamlessly integrating these two elements, and how does teamwork play a role in developing these solutions?

Cyber and compliance go together like chocolate and peanut butter, but without good old information technology, the whole thing gets sticky and melts. Or some analogy that works better. You see, compliance is about regulation, and that’s so important. Cybersecurity is about managing risk, and that’s so important. Put those two together, and you’ve sort of got a governance package that’s missing anything in the real world! You need IT to stick it all together and give it a shape. Technology is what connects us to reality in this scenario. The programmers, the computers, the software and the infrastructure – it’s where the cyber, the regulatory, and the technology touch the humans. A friend of mine used to say the darn computers were perfect until the people touched them. So a Governance program without IT works great unless there are people involved!

When I talk with people about cyber and risk, I advocate for a GRC-based platform. This isn’t a vendor-driven thing – there’s no GRC company or anything like that. It just stands for Governance, Risk and Compliance. It gives us a way to talk about Risk with the Lawyers, Governance with the Business and Compliance with the – well, sometimes more Lawyers, but you get the idea! We all need to contribute to the conversation. It’s gotten too big, even in a smaller business, for one voice to be the only voice.

Sage Inc. focuses on small and midsized businesses often overlooked by larger tech providers. What unique challenges do these companies face, and how does Sage tailor solutions to their needs?

It’s become obvious through some really nice research recently that the smaller companies around the world are being shut out of not just high-quality information technology support services but any services at all. And it’s not just services like how to improve themselves: they are missing out on what I consider basic cybersecurity hygiene. What small company can afford to sink $10,000 on a firewall? That’s a part-time employee’s salary in some places.

And then there’s the fact that this cybersecurity and regulatory stuff is straight terrifying to some people. When you just read about it – what percentage of small businesses can survive if they get attacked by ransomware? – it makes you want to never use email again. And you read stuff that’s contradictory or doesn’t make any sense, and why would the Russians really be trying to hack me if I sell purses in Norfolk, Virginia? How can that be real in any way?

So, faced with the decision to spend money they don’t have on something they don’t understand, most people ignore the problem, hoping it doesn’t apply to them. That’s not only dangerous for their own company; it makes the whole ecosystem more dangerous for everyone. We are only as strong as our weakest link.

So, I started Sage with a focus on SMBs. We have no “territory” because SMBs are the same all over the world. We help companies with all of their technology, cybersecurity, automation, and AI needs, and we do it all in a bespoke manner. There is nothing you don’t need—no package solutions—and we do it at a price that’s realistic for a small business.

"The darn computers were perfect, until the people touched them"

In your experience, what are some of the biggest misconceptions small businesses have about cybersecurity, and how do you help them overcome these challenges?

There are two big myths I still hear – one is that Apples and Macs can’t be infected. Not true! The other is that there isn’t any way their small

business will get hit because they’re too boring/unsuccessful/insignificant. This isn’t true, either. Now it’s a numbers game, and the bad guys will hit you just to have you in their stable.

A lot of what we do is education, helping people understand what’s going on in an approachable—we hope sometimes fun—way and breaking down the barriers to understanding.

As someone who thrives on facilitating discussions that lead to effective business operations, how do you approach getting stakeholders aligned on complex IT or compliance decisions?

I think that is the same answer as above. If you’re doing the right job of breaking down the problems so they are understandable – get it into bitesized chunks – and you’re aligning to the core business strategy that’s driving your client’s business, there’s no way you aren’t already aligned. If it doesn’t sound like you’re aligned, or you seem to be butting heads on something, there’s a communication issue, is my guess. I think that the simplest path to figuring those out is sometimes drawing the problem. Flow-chart what it is that you’re saying, and have the other party do the same. Where they start to divert, you’ve found the miscommunication.

do you envision Sage Inc.’s role evolving as technology and regulatory environments continue to shift? What trends or innovations are you most excited about for the future of small business technology?

Keeping up to date with the various regulatory requirements and technologies out there is certainly part of our remit. As certified professionals, we’re required to stay abreast of that sort of evolving topology and keep up with our continuing education. I think that’s a good thing. I think that the business will need to remain as agile and responsive as we are today, even as we grow because our clients will need us to be right beside them. Things are going to continue moving more quickly.

I think that there is great promise in what genAI brings to the table. It will take a little longer than some people thought for that potential to be realised, but not that long, and for those businesses that are able to plan now, it won’t be long at all. I also think that there is promise in the further evolution of the personalisation revolution. Some people think that’s over; I don’t think it’s really started, particularly for the smaller firms. I’m also excited about where we go with the future of battery technology. It’s been a long time coming, and now we are having some real breakthroughs. That could unlock whole mountains of innovation that make us all more portable and revolutionises how we work again.

Looking ahead, how

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