Artificial Intelligence
Taking AI Beyond the Experimental Stage? It Starts with Focus.
Taking AI Beyond the Experimental Stage? It Starts with Focus.
There are countless successful applications of AI across various sectors—from detecting cancer on CT scans to combating financial crime, and from adaptive learning to predicting bankruptcies. AI therefore ranks high on the priority lists of top executives.
Beyond the Experiment
Organizations often start by appointing key AI leaders and recruiting talent, and the first successful experiments soon follow. The initial successes are celebrated, and AI becomes a tangible reality for everyone involved. Yet the real challenge is only just beginning: how do we scale up these experiments and ensure sustainable results with AI?
To achieve this, AI must be given a permanent role in day-to-day operations. The organization must be willing to change its existing working methods and adapt its processes. Nothing is more frustrating than contributing to a successful experiment only to see it fizzle out. It’s a waste of time, money, and talent. So it’s important to move quickly to implement changes in day-to-day operations in order to use AI to achieve sustainable results.
What is Artificial Intelligence?
Focus
But how do you get the organization on board with this change? The best way to do this is with a compelling vision for AI. Don’t paint vague pictures of the distant future—be specific. How will AI help the organization move forward? In what specific areas? That focus must be part of the vision. Focus is vital for accelerating scaling and controlling costs. Focus also generates attention and motivates talent.
Focus accelerates the development of domain expertise and relevant data collections. After all, not all of an organization’s data is equally relevant to the focus area. Therefore, not “all” of an organization’s data needs to be subject to the same strict data governance regime. Research shows that this approach can reduce the time data teams spend on these tasks by up to 50%. This allows you to implement AI applications faster and at a lower cost.
Roundtable Discussion on Thursday, November 19
Growth Path
Once results become evident in day-to-day operations, it’s time to broaden our perspective again—to develop new expertise and expand our data sets. In this way, a clear vision for the application of AI, with the right focus, ensures a solid, iterative growth path.
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