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Four Best Practices To Get The Most Out Of Your AI Tools 

Four Best Practices To Get The Most Out Of Your AI Tools 

Most Artificial Intelligence systems aren’t plug-and-play. AI tools require systemic changes to fully unlock their value.

Before companies can benefit from the efficiencies and cost reductions that AI tools promise, they need to demonstrate certain capabilities, such as a deep knowledge of data science and a strong IT backbone. AI tools need to harness a company’s data and integrate with its IT infrastructure in order to make accurate predictions. We discussed these capabilities in detail inthe first part of this article.

Once an organisation meets the prerequisites to integrate AI systems into their business processes, it’s time to outline a few critical practices that will help extract the most value out of their AI and enterprise cognitive computing (ECC) applications. 

Four crucial practices to deliver on the promise of AI tools

Research conducted by MIT Sloan identified four practices that will help companies deliver on the promise of AI tools. 

  1. Develop a clear, realistic use case. A use case will delineate what an enterprise cognitive computing (ECC) application will do, how it will augment certain business processes and how work will be divided between AI tools and users. This will clarify the necessary process changes, training, cost and application benefits. Domain experts and data scientists will have to work together to outline how organizational outcomes will improve and what data will be needed to create them. Enterprise architects need to point out how structures, roles and systems will change due to the ECC app. IT experts will need to identify what other IT support the tool will require.
  2. Manage ECC application learning. Since business conditions and demands are dynamic and ever-changing, AI algorithms need to learn and keep up with those changes continually or the algorithm will have “drifted” from its original purpose. Companies will need to rely on IT backbone capabilities, data science competence and domain proficiency to manage the drift and retrain and relaunch the AI app. Domain experts and data scientists need to work in unison to identify the new sources of data to improve the accuracy of the algorithm.
  3. Co-create throughout the AI application life cycle. To successfully get the most out of AI tools, people from disparate disciplines within the organization need to come together – not just domain experts and data scientists. Co-creation is significant because business experts aren’t yet aware of what an ECC app can or can’t do. But working with a diverse team from across the organization and sharing expertise can help mitigate this problem. Even after implementation, maintaining and sustaining the AI tool will be highly interdependent on the collaboration of a diverse group of experts
  4. Champion the use of AI tools. Companies that manage to create a positive buzz around their AI tools and champion them throughout the organization have been most successful in their implementations. This encourages employees to not only use the current tool but to generate new ideas for ECC apps that can improve their work. Employee response to ECC can vary from it being a detriment to having exaggerated expectations about it. It’s up to the organization to calibrate the responses and provide channels for realistic conversations.
AI tools can expand the capabilities of employees and business leaders. Using AI tools will strengthen the power of the tools themselves, but managing the organizational changes surrounding AI tools will be a key factor in seeing these benefits. Learn how to get the most out of AI systems in our MIT AI in Leadership course. Download the free brochure.
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