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    14.12.2017 — 3 min read

    Design your operating model to withstand the winds of digital change

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    As the end of the year approaches, budgeting and goal setting are on the to-do lists of many companies. Much energy is spent on visualizing the future. Remember how much easier this all was 20 years ago?

    When I consider working life over the past 20 years, one of the biggest changes has been our declining ability to forecast the future. Two decades ago, we could create reasonably accurate three- or even five-year business plans by predicting trends and changes in consumer and corporate life.

    In the 1990s, for example, we created different five-year scenarios about the development of e‑commerce. Who among us nowadays would dare try the same or even dream of getting things right?

    What’s next? Robots playing ice hockey?

    Today the pace of change is so rapid that it sometimes even seems difficult to predict what will happen in the next hour.

    Were you, too, surprised to see a video of a Boston Dynamics robot effortlessly landing a backflip? Although advances in robotics are constantly present in my day-to-day work, this one threw me for a loop. What do you think will be next? Will this same robot be playing ice hockey one month from now?

    Similar developments in this field include apple-picking robots, self-driving cars and Hyperloop trains.

    When surprising innovations emerge from every direction, instead of wasting energy trying to predict, companies should focus on developing an operating model designed to withstand change and agilely react to it. It is not useful to try and envision the future in too much detail: nothing is permanent except change.

    AI takes charge of data masses

    There is help available to deal with the challenges of forecasting – thanks to Artificial Intelligence (AI), of course.

    Apart from getting human-like robots to perform backflips, AI can also process masses of data to provide us with refined forecasts and recommendations for resource planning and workflow design. Perhaps soon it will predict its own actions.

    One noteworthy example of such solutions is IBM’s AI Watson that understands information generated by humans and, thereby, learns to make decisions. In Finland, Watson has been used in areas such as health care and for product recommendations on e-commerce sites.

    In the midst of these winds of change, it is encouraging to see many companies invest in digitalization by producing products and services that better meet the needs of their target groups, both by radically modifying their processes and by expanding their operations into new industries.

    Artificial Intelligence, business solutions, Digitalisation, Robotics