Finding AI’s low-hanging fruit

Delivering AI solutions from the test bed to production environments will probably be the key focus for the enterprise throughout the next year or longer. But organizations should be cautious not to push AI too far too fast, despite the pressure to keep up with the competition.

This often leads to two key problems. First, it pushes inadequate solutions into environments where they are quickly overwhelmed and this leads to failure, disillusionment and mistrust from the user base that ultimately inhibits adoption. The AI industry is not helping anything with its stream of promises that their solutions offer complete digital autonomy and transformative experiences.

Small victories are still victories

In some circles, the idea of going smaller with AI is catching on. Instead of a complete forklift upgrade across the entire business process, it’s better to do the easy stuff first. That is, put AI to work in limited, non-critical areas and see how it performs before promoting it to bigger and better things. In this way, successes are more frequent, trust is more easily earned and AI can learn how to integrate with the world as it is before trying to improve it.

For many organizations, however, the question is where to find this low-hanging fruit.

According to Joe Bush, editor of The Manufacturer, it’s all around us. Resource consumption, for one, can be monitored far more easily and effectively with an intelligent platform than with teams of operators. While he speaks to an industrial audience, the same need to minimize the use of electricity, water and other basic commodities exists in the enterprise. With the right sensor-driven data, AI can also assess workloads across the digital environment and even shift it around to ensure the work-machine balance remains optimal. And AI can also react to changing circumstances far quicker than manual operators and can streamline key processes like reporting, maintenance scheduling and supply.

Of course, it doesn’t hurt to have a plan in mind when deploying AI into production environments, since it is far more valuable working in tandem than isolation. Accenture’s Bhaskar Ghosh, Rajendra Prasad and Gayathri Pallail argued recently in The Harvard Business Review that instead of aiming for quick victories or grand strategic transformations, the wisest course right now is to concentrate on building capabilities that address problems that will recur in the future. This will require careful analysis of current capabilities and identification of any gaps that are creating failures. Then you can create a step-by-step approach to deploying AI so it achieves the small victories that will ultimately lead to the grand transformation.[…]