Artificial Intelligence (AI) strategy: 3 tips for crafting yours

Artificial Intelligence (AI) strategy: 3 tips for crafting yours

Shaping and implementing a successful artificial intelligence (AI) strategy requires thoughtful analysis of business problems, care with data, and a strong organizational culture

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There is no doubt – artificial intelligence (AI) is gaining momentum. We’re already seeing its impact in our daily lives and on the business landscape. Today, a growing priority for business leaders is determining how to best utilize AI to drive maximal value for their organizations.

However, it’s important to note that implementing AI is not just a matter of tacking on another solution to existing business processes. Instead, business leaders need to work on identifying the most strategic ways to implement AI and make sure buy-in is felt across the organization.

AI strategy: 3 essential tips

With this in mind, here are three tips to help you craft your AI strategy effectively.

1. Invest time in identifying the right use cases and how you will measure value

There is a tendency among leaders to consider a use case and ask, “Can I apply AI to this?” The problem with this thinking is that it’s not the right question to ask: If you can fully digitize a process, you can technically apply AI to it. A more productive question to ask is, “What’s the value proposition of applying AI to this use case?”

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

AI can drive value only if it is applied to a well-defined business problem, and you’ll only know if you’ve hit the mark if you precisely define what success looks like. Depending on the business objective, AI will commonly target profitability, customer experience, or efficiency. Automation from AI can yield cost savings or costs that are redirected to other uses.

You'll only know if you’ve hit the mark if you precisely define what AI success looks like.

For example, AI can enable a business analyst to spend less time on decisions that are highly predictable and more time doing analytical work that better utilizes their knowledge and experience.

Whatever the KPIs are, these success metrics will drive a learning loop, which will enable the AI system to adjust and improve its performance. It is critical to define KPIs at the beginning of any AI venture and to monitor the KPIs over time so that you can quickly iterate on the solution if needed. That’s what makes the difference between AI as a science lab experiment versus a science-based system that drives real, ongoing business value.

2. Cultivate your data and data-related processes to support the AI initiative

Treat your data as a treasured asset. While data quality and merging disparate data sources are common challenges, one of the biggest challenges in data integration initiatives is streamlining, if not automating, the process of turning data into actionable insights.

To understand why this can be a challenge, consider these questions:

  • Do you have the right data to address your business case?
  • Are you able to quickly adapt to frequent data changes?
  • Can you access data-driven insights at the time they are needed?

Make sure data integration initiatives are a team effort across the entire organization and not just left up to IT or data management teams. There needs to be strategic alignment across the business on the importance of the data, what purposes it will be used for, and how it will be maintained over time.

3. Put the right people in place and foster a culture that supports AI initiatives

If you are looking to develop AI capabilities in-house, keep in mind that AI teams can benefit from having a balance of skillsets. For example, deep expertise in modeling is critical for thorough research and solution development. Data engineering skills are essential in order to execute the solution.

Your AI teams also need leaders who understand the technology, at least enough to know what is and is not possible. In running an AI team, it is important to create an environment that fosters creativity but provides structure. Keep the AI team connected to business leaders in the organization to ensure that AI is being applied to high-priority, high-value use cases that are properly framed.

In running an AI team, it is important to create an environment that fosters creativity but provides structure.

Even if you are outsourcing to a vendor rather than developing AI in-house, having a people-focused company culture can go a long way in driving the success of AI initiatives. Retention of employees who have the skillset and experience working with AI systems can yield positive returns.

As organizations are increasingly making decisions based on data, AI is becoming more entwined with business processes. Be selective about which use cases are worth the investment needed to implement AI.

In order to see sustained value from AI in the long term, take care up front to target well-defined use cases with clear KPIs that are tied to the AI system. Data, data, data – treat it like the treasure that it is and look for ways to streamline data-related processes while growing this highly valuable asset.

Finally, consider how restructuring your organization could enable better support for your AI initiatives. With the right people and processes in place, AI can enable powerful capabilities that drive substantial value for your business.

[ Want lessons learned from CIOs applying AI? Get the full HBR Analytic Services report, An Executive’s Guide to Real-World AI. ]

Justin Silver, PhD is a manager of data science and AI strategist at PROS. He specializes in the application of data science to enable pricing and sales excellence. Dr. Silver’s innovative contributions to the PROS solutions suite have helped customers to achieve substantial ROI through a scientific approach to commerce. Dr.

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