AI ADOPTION IN ENTERPRISE HOW TO DRIVE SUCCESSFUL

ANNA KOPP has been director of Microsoft Digital at Microsoft Germany since 2015 and has been with the company since 2004 in multiple global roles in sales and operations. She’s the branch manager of the main office in Munich, global co-chair of Women@ Microsoft, co-founder and board member of Female CIO Circle, on the advisory board of CIOnet CIDO Women, and vice chair of the Board of Directors for Münchner Kreis.

Anna comes from Sweden where she studied international communication at the University of Stockholm, and has lived in Germany since 1992. In 2020 she was voted one of the most inspiring women in Germany, in 2023 she was chosen by The Promptah AI magazine as one of the Top 100 Role Models for a Better Future, and she has just been named one of the most innovative women in AI and Data by CIO Look magazine. She is a specialist in the new world of work and AI from a cultural, political and practical perspective. She advocates flexible working models and is a gender equality role model in the German tech industry. In her spare time she enjoys off-road motorbiking and sings in a rock band. She even won a Rock Oscar as Germany’s best singer once upon a time!

What’s the most important thing to consider when driving AI adoption in large enterprises?

ANNA KOPP

Apart from the technology itself, transformation and accountability are key. You can’t roll out new technology without guiding people through the change. It’s about more than just introducing tools; it’s also about re-evaluating and adapting processes.

Many companies treat transformation as something they can squeeze in whenever there’s a bit of spare time. But transformation isn’t a hobby. You need to assign someone to take ownership, manage the process end-to-end, and prioritise the starting points. You have to decide what technology will be used, who will use it, and how processes and workflows will need to change.

Another critical aspect is workforce upskilling. Implementing AI means addressing a skills gap because the technology is so new. Prompting AI to do tasks might seem simple at first, but there’s nuance in crafting effective prompts. AI is excellent at executing what you ask, but not necessarily what you intended. Learning how to prompt effectively is a skill that improves with consistent use.

Using AI involves more than a one-off training session: organisations need to allocate the time for employees to learn and integrate AI into their roles.

Should organisations have a chief AI officer or an equivalent role? And why is it important?

Absolutely. You need a chief transformation officer or a similar dedicated role; someone with the skills to drive change. Managing transformation involves specific competencies, like milestone tracking and prioritisation, which are distinct from standard management skills.

Many companies, including Microsoft, have introduced roles like the chief AI transformation leader. We did this three months ago because the skills required for AI transformation are unique. It’s a significant investment – these roles demand highly skilled individuals, and they don’t come cheap – but if you want the job done well, you need the best people. Having someone with expertise ensures the transformation happens efficiently and stays within budget.

This role should also report to IT, specifically to the chief information officer (CIO). It’s similar to how cybersecurity typically reports to the CIO through a chief information security officer (CISO). AI transformation is a technology-driven process, and IT is best equipped to oversee it.

Additionally, AI implementations require crossfunctional collaboration. For example, integrating AI involves data management, but it also needs to align with cybersecurity measures. These teams need to work together seamlessly, and it’s the responsibility of the transformation leader to facilitate that collaboration.

Does Microsoft have one chief AI transformation officer in a specific region, or are there multiple transformation leaders globally?

About eight years ago we implemented the Chief Transformation Office, starting with one global chief transformation officer. Each country then had its own chief transformation lead, supported by a few program managers. This structure was designed to oversee the overall digital transformation, which was a major focus at the time.

That transformation was significant, but now I think a better term is evolution. We’ve already digitally transformed, and now we’re evolving into the age of AI. Rather than having a dedicated chief AI transformation lead for each market, we’ve created a global organisation to drive the programmatic approach. This structure breaks things down and works directly with regional sales segment leads, often collaborating with roles like sales excellence managers and chiefs of staff in each market.

Using AI involves more than a one-off training session: organisations need to allocate the time for employees to learn and integrate AI into their roles.

When it comes to piloting AI solutions in large companies, who should be involved?

There are two critical aspects to consider: testing groups and governance boards.

Before you even start piloting, I always recommend establishing a governance board. This is essential because, once you begin, issues will inevitably arise. For instance, employees might feel disadvantaged, or unexpected challenges may emerge. A governance board is there to address these situations.

The board should include representatives from IT, HR, the works council (if applicable), your data privacy lead, and possibly your legal department. Often, the data privacy officer reports to the legal department, so they might serve as the representative. It’s important to have clear communication channels so employees know how to reach the board with concerns or questions. This ensures there’s a structured process for handling unforeseen issues that arise during implementation.

Once the governance board is in place, you can move on to testing. At Microsoft, we decided that the people who would eventually sell the product should be heavily involved in the testing phase. Since they work with it daily, their feedback is invaluable in identifying what works and what doesn’t. Alongside them, we included IT, HR, engineering, and the works council.

For countries with works councils (there are around 13 in Europe), it’s especially important to involve them early. Works councils represent employees’

ANNA KOPP

interests in labour relations and play a significant role in determining whether the technology can be implemented.

This is particularly crucial if the technology wasn’t developed in Europe. If you’re adopting software from the United States, China, or India for example, the engineers building it may not fully understand the specific regulations in various European countries. Early feedback from works councils helps ensure the technology or its configurations comply with local regulations, and can be adapted if necessary.

If AI has the potential to be capable of replacing human workers, would organisations like works councils ensure that this won’t happen?

Works councils are there to protect employees and their jobs. But displacement isn’t going to happen overnight. Instead, it will evolve gradually, and specific types of jobs – particularly those that are highly data-driven –may be more susceptible.

Take translators, for example. That’s a role where AI has made incredible strides, and it’s now capable of handling translations very quickly and accurately. So it might not be a career someone wants to focus on today. But it’s not about all jobs disappearing; it’s about slowly replacing certain tasks or roles.

At the same time, we’re facing a paradox in the market. On one hand, HR departments say there’s a talent shortage; they can’t find the skills they need to run their organisations. On the other hand, there’s fearmongering about AI being a job killer. The reality lies somewhere in the middle.

To address this paradox, companies need to focus on two things simultaneously: automating what can be easily automated and upskilling or cross-skilling their workforce. These two efforts go hand in hand. You automate repetitive or routine tasks, and then you redeploy your workforce to focus on areas where human input is essential.

So far, we’re still seeing more jobs being created with AI than eliminated. Someone still has to engineer, develop, manage projects, interact with customers, and truly understand their needs. Even with sophisticated AI, there’s an ongoing need for a human in the loop: someone to take accountability and make final decisions. AI is a tool, like a hammer or screwdriver, that supports your work. But it’s still humans who decide how to use it.

Works councils play a key role in this evolution. They’re involved to ensure these principles are upheld: protecting jobs while also supporting the company’s success. For a company to safeguard jobs in the long term, it needs to remain efficient. If there aren’t enough people to fill critical roles, the company has to turn to automation. It’s a complex balance, but works councils are part of ensuring that the process is fair and sustainable.

What does a well-functioning setup for AI adoption look like?

The first step is to get the basics right, and give employees time to engage with the material. There are a few key components to a good learning path:

First, if there are jobs that require AI-specific skills you need to establish an academy – a series of structured, sequential trainings. I usually describe it as a pyramid. At the base of the pyramid are foundational skills for all employees. These are broad competencies that everyone needs, regardless of their role.

The next layer is role-specific training. For example, what does a salesperson or marketer need to know to integrate AI into their specific job? This layer focuses on the skills and knowledge tied directly to roles within the organisation.

At the top of the pyramid, you have ad hoc, situation-specific training. These are small, snacksized trainings designed to address immediate needs. For example, someone might encounter a situation and wonder, ‘How can AI help me with this?’ or ‘How do I achieve a specific task using AI?’ This is where having a searchable, on-demand database of resources is crucial. Employees need quick and accessible answers without spending days in a classroom.

At Microsoft, we allocate ten learning days a year for training. These include mandatory sessions, as well as AI-specific learning days where employees can dive deeper into topics like prompt engineering.

Finally, businesses should supplement their internal training programs with external industryspecific content.

In summary, a strong learning path starts with foundational training for all employees, builds rolespecific competencies, and provides on-demand resources for immediate challenges. It’s a layered approach that evolves as employees learn and grow.

As issues arise during AI adoption, how does Microsoft ensure clarity on what employees should do in those situations?

This is where HR and IT collaboration becomes essential. There are certain things employees shouldn’t do with AI – guardrails that need to be established. These rules of engagement can sometimes be built into the product itself. For example, you could design the system so that if someone tries to do something inappropriate, the functionality is simply greyed out with a clear ‘no-go’ message.

ANNA KOPP

However, overly restricting the technology can reduce its value. Striking the right balance is critical, especially in regulated industries like finance, where compliance with data privacy laws is a priority. Companies need to develop clear guidelines tailored to their specific processes and culture. While universal principles for responsible AI exist, every organisation must create rules that reflect its unique needs and values.

This requires assembling a team to define these guidelines. It should also be clear what happens if someone violates them. For example, if a manager uses AI inappropriately by tracking an employee unfairly and giving them a lower bonus. Employees need to know exactly where to escalate the issue. This is where the governance board I mentioned earlier comes in. It provides an independent review to ensure fairness, holds people accountable, and determines consequences.

Establishing this kind of structure ensures employees feel protected while enabling responsible AI usage. Of course, it’s a complex challenge that varies from company to company.

What are some of the tactical elements Microsoft has implemented to support co-pilots and ensure adoption?

One of the tools we use is the Work Trend Index (WTI), which you can find on the Microsoft website. It’s a research initiative that gathers statistics on what employees and managers expect from AI. The top expectation from employees is that AI should handle the ‘boring stuff’; tasks like taking meeting notes, transcribing discussions and other repetitive administrative work.

We’ve all been in meetings where someone asks, ‘Who’s going to take notes?’ and everyone avoids eye contact. AI can take over those unenjoyable tasks, helping employees focus on more meaningful work. Beyond that, people want AI to help them keep order amidst the chaos – organising information and making it easy to find what they need.

To meet these needs, we’ve created unified landing pages where everything related to AI is centralised.

These pages include:

● Training resources: Where to find training materials and courses.

● AI champions: A list of experts employees can reach out to for help.

● Office hours: Opportunities to ask questions and get guidance.

● Reference materials: Links to videos, white papers, and detailed guides on how AI works.

● Re sponsible AI guidelines: Clear recommendations and policies for ethical AI use.

● HR resources: Connections to HR guidelines and support.

Having all this information in one place ensures employees always know where to go for support. It’s about making engagement with AI as easy and intuitive as possible. Accessibility and simplicity are key to driving adoption.

We also have an organisation called Worldwide Learning at Microsoft which strategically develops training programs, because learning doesn’t just happen on its own; it has to be planned. If someone needs to be certified for a task, that requires structured training. Certification programs must be integrated into daily work routines, alongside other activities like dealing with customers.

This learning organisation sets the overall strategy, organises learning days, and designs the various types of training: academies for in-depth skill-building, snack-sized ad hoc training, videos, or searchable databases. These resources support ongoing learning.

The way we learn today is different from 30 years ago. Back then, you might attend a three-day classroom training for a new ERP system and then use that tool for 10–15 years. Today, with agile methodologies and rapid software updates (often new releases every two weeks) you need to continuously adapt.

ANNA KOPP

What criteria should companies use to pinpoint which business functions will benefit most from AI? We start by identifying roles or areas with the most specialised, content-heavy tasks. For example, sales teams working with CRM tools that handle large catalogues of products or SKUs – they need to deeply understand the products. Similarly, operational teams like HR, procurement, finance, and legal often benefit significantly.

Take legal tasks, for instance. Something as straightforward as drafting a non-disclosure agreement, checking when the last one expired, and creating a new one can be done quickly with AI. These are timeintensive, repetitive tasks – mostly involving copypasting and searching – that AI can handle efficiently.

The second recommendation is to start small. Find one specific problem and solve it. Don’t aim for a massive, all-encompassing platform right away. I discussed this in a TED Talk I gave about the Vasa ship – a Swedish warship that sank because too many big ideas were crammed into one project without proper planning. The same principle applies here: if you try to build a massive AI solution over several years, employees will lose patience and resort to quick fixes for their smaller problems.

Analyse your organisation and identify areas where the most time is wasted or where repetitive tasks occur frequently. Start with one problem and one solution. Invest just enough to see if it works. If it does, build on it. If it doesn’t, scrap it and move on to the next issue. By narrowing the focus to specific use cases, you can create a clear path to success. Solve small problems first, and over time, you’ll build a suite of effective, smaller solutions before tackling a larger platform. Additionally, leverage the tools and products already available. There are robust suites, like Microsoft’s, that help with the overall office environment. Instead of reinventing the wheel, focus on your line-ofbusiness applications and industry-specific tools. Also, consider how AI can enhance your service-level agreements (SLAs) and responsiveness to customers.

What are some of the key lessons you’ve learned from driving AI adoption in a multinational company like Microsoft?

Localisation is a big one. First, there’s language localisation, but it goes beyond just translating words and grammar. It’s about understanding cultural nuances. For example, in Germany, when addressing customers, we often use formal titles – like Herr Müller or Frau Müller – and we use formal speech, which we call Siezen . It’s hard to fully explain in English, but it’s about speaking formally versus informally. In contrast, in the U.S.,

For most employees, that’s the key: reducing the overwhelming workload so they can focus on what they enjoy and the work that made them choose their career in the first place it’s much more casual: ‘Hey, John, how’s it going?’ You can’t just translate these cultural differences directly, and it’s important to account for them when you’re working across different markets.

Another important aspect is how AI can help with time zone challenges in global companies. For example, AI can summarise or catch you up on meetings or decisions made overnight, reducing the need for late-night or early-morning calls. While these basic functions – note-taking, recordings, transcripts, and summaries – might sound mundane, they’re incredibly impactful. If you can remove those repetitive tasks, it frees people up to focus on more innovative and strategic work.

Interestingly, the features people use the most are often the simplest ones because they save so much time and effort. I always say, when in doubt, start with the boring things. There’s a lot of them, and they often deliver the quickest wins.

We’re all so stretched thin, especially since COVID. We once had boundaries, like commuting time, to separate work and personal life. Now, with remote work, we start earlier, finish later, and the workload has only increased.

AI can help lighten that load. For most employees, that’s the key: reducing the overwhelming workload so they can focus on what they enjoy and the work that made them choose their career in the first place.