The strategic value of operationalizing AI is recognized by IT leaders and business executives worldwide, but shockingly only a few have progressed beyond experimentation. A recent Capgemini survey found that only 13 percent of businesses have progressed beyond concept proof (POC) to scaling AI across the organization.

Since it reflects wasted time and money and unrealized potential, the fight to operationalize AI is painful. Reports circulate full of ideas, proposals, and manifestos exchanged to close the gap between the idea of AI and the distribution of businesses (including one proposal to abolish the POC entirely). All of these are knowledgeable and are worthwhile. However, something we have seldom seen discussed in any of them is the farfetched advice to invest in people.

We’ve seen several organizations build data science teams with the sole intention of operationalizing AI, and we’ve achieved many accomplishments. What we’ve found is that it also needs an increase in human interaction to operationalize AI. Let us see how to bring AI into play in organizations by investing in humans.

#1 Expand the skill sets of your team

Data experts spend a small part of their time designing models of AI. They spend much of their time understanding the business issue, gathering and cleaning data, analyzing business background information, exploring stakeholder theories, and more. While it is difficult, one of the most straightforward steps is to construct a model — a forecast or recommendation engine.

Specialize for efficacy, generalize for effectiveness. Develop the correct practice community and the required trade-offs between productivity and performance.

#2 Emphasize on Evolving team competencies

Progress needs to keep humans in the loop. Far from foolproof, emphasize on the most well-designed models. It can easily exacerbate undetected errors. To track your AI systems and respond to problems when they occur, you’ll need knowledgeable people. Besides, to continually evolve their competencies, you will need your team.

To exploit advancements in AI technology and algorithms, the teams need to have the requisite abilities.

#3 Translate for your business

For any operationalized approach, the business insight feature is crucial. It’s always solving a business problem, no matter how automated a process is. The performance of AI needs to report back to the organization on what worked and why. At ONPASSIVE AI, these questions play an important role in ensuring the correct implementation of AI technology.

For instance, if an AI model is an engine of recommendation, how well does it serve its purpose? What was working and what wasn’t? Who has behaved according to the recommendations? Can we redesign the product or the deal based on what we’ve learned so far? It is vital to pose, address and communicate these and many other business-oriented questions,

Incorporate your AI teams into your business.

Organizations require detailed visions. They need to create full teams, but they also need a roadmap to incorporate the broader organization with AI and their data science teams. There are a few avenues for this to be done.

It is worth aiming for Operationalized AI, but it is a strenuous journey. To enable the rapid deployment of new AI pilots, you need to have an infrastructure in place. Humans are the infrastructure. Invest in their growth, cultivate their communication and cooperation, and integrate them into the organization. It calls for some time to reach there, but it will be worth continuing as the rewards are numerous.