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and on, and will continue to grow as applying AI to work becomes a new business imperative. So where to start? The first and most important step is to pick your spots. This will vary by business of course, but there are some common themes you should look for. (If you want a shortcut, look for a cubicle farm where lots of people are doing the same thing over and over and over again.) Software robots don’t get bored, so start with work that is highly repetitive and done by lots of people (because you want a meaningful impact). Robots are great at calculations, so tasks that rely heavily on a prescribed process and large amounts of data are far better targets for automation. Avoid trying to automate complex, ambiguous work that requires intuition, empathy, curiosity, insight or judgment. In spite of all the sci-fi talk, it’s best to leave that work to us humans. The key here is to pick your spots. Apply AI systems to automate specific (often mundane) tasks, and use that freed-up time and energy to do higher-value work. This higher-value work is often what we much prefer doing to completing forms (again), expense reports (always behind), scheduling (just a mess), and managing your endless soul-crushing daily tsunami of emails.

Make everything a code generator

You know something serious is happening when it shows up in bad jokes. “What will my refrigerator say to my sprinkler system?” “My alarm system got hacked and is demanding a Bitcoin to turn off.” Not funny, and the point is serious business. Every thing can now be a code generator. Watches, appliances, shirts, lightbulbs, cars and medical devices are now instrumented with sensors. More importantly, businesses are creating new products and services based on this data. Where to start instrumenting? The better question is where should you not? Selling or servicing mortgages? Instrument the home to help prevent predictable damage. Retail? Use sensors to better track and manage foot traffic and product placement. Healthcare? Instrument your ICU to help improve health outcomes. Manufacturing? Every machine should now be sending out data to improve productivity and uptime. With the cost of chips and sensors continuing to drop, every physical thing should be generating valuable data that your business or agency can use. The only limit is your creativity and willingness to innovate. Once you have data coming off a lightbulb, shirt, windmill or pet collar, what do you do with it? What are the correlations hidden in the data? How can you monetize the data from your table or car? It’s not really just specific physical items either. Your logistics process, call centre or

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claims process should also be generating data. Many leaders get a bit paralyzed here, because they don’t recognize fully that data is a means, not an end. It’s a raw material, not a business outcome. This is why you need support from your analytics team to help unlock value from your new raw material. Data science and algorithm building is not alchemy or a black art, it’s merely a set of tools that curious people can use to draw meaning and insight from data. If you don’t have these tools wielders next to you, and many leaders may not, then one of the most important things you can do to get fit fast is build a network of partners who can help you make meaning – and business value – from your data. Then it’s up to your business team to build new business models, consumer experiences and products based on your insights.

A postcard from your future

It’s a cliché but also true: this is all easier said than done. But similar to healthy eating and fitness habits, becoming digital should be the new practice for everyone hoping to succeed in the coming quarters and years. In five years’ time, the business landscape will be very different. New players will emerge. Venerable companies will fall. Unimagined technologies will exist. Anyone with too much certainty about how the future will play out is selling something. But we know enough to get started, and we must get started. We also know the clock is ticking. (Digital clocks don’t actually tick, but you get the idea.) If you act soon, this is going to be great. But if you don’t, your advantage will wither. After years of meeting people building new AI-fuelled systems of intelligence, one common thing that struck us was their sense of optimism. These weren’t Pollyannas or naive children, they were successful business people working hard to do the right things for their customers and their companies. They were probably like you. And that’s the point. Winners of the fourth industrial revolution will not sleep through the starting gun. They will recognize that waiting is the highest risk, decide that they had the agency to take an action, refuse to be stonewalled by perceived roadblocks, and then take the first steps described here to get fit for the future of work. — Paul Roehrig is a cofounder and chief strategy officer of Cognizant Digital Business. He is a coauthor – along with Malcolm Frank and Ben Pring – of What To Do When Machines Do Everything

The key here is to pick your spots. Apply AI systems to automate specific (often mundane) tasks, and use that freed-up time and energy to do higher-value work Q4 2017 Dialogue

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Dialogue Q4 2017