Evaluate AI Use Cases Strategically to Achieve the Best Outcomes

With so many potential applications of artificial intelligence, figuring out which avenues to explore and where to invest resources can be daunting. At CDW, we’re pursuing three distinct pathways, each with its own technology and business objectives. We deploy off-the-shelf solutions to enhance coworkers’ productivity, customize solutions for specific workflows and build our own large language and machine learning models for innovative initiatives. Throughout this journey, we have been intentional and strategic in prioritizing AI use cases.

Ideas for new AI applications come from peers, industry events, partners and competitors. With the rise of generative AI, this technology seems to be everywhere, which can create a sense of urgency to keep up. That makes it easy to fall into the trap of viewing every project as a nail and AI as the all-purpose hammer. However, we often encourage customers to pause and assess whether AI is genuinely the best solution for a particular problem.

This might seem counterintuitive in an environment where AI is advancing rapidly and organizations are wary of falling behind. But the reality is that AI can be costly, experimentation requires resources, and every deployment needs well-planned change management. By carefully evaluating where to invest AI resources, organizations are more likely to achieve meaningful results.

Here are some key questions to ask when considering AI initiatives.

The first step is to ask, do I have a viable use case for AI? Have I done everything else before considering it? Organizations need to fully understand and rationalize the business process in question. Often, one of the biggest challenges is failing to comprehend current processes, which then aren’t incorporated effectively into the AI initiative. To make the investment worthwhile, leaders need to ensure that AI can meaningfully improve a process.

Assessing readiness is also essential. Is the necessary technology available? Is the organization prepared to adopt it? This involves assessing both technical and change management capabilities. Besides piloting the tool and gathering feedback, teams may need to adjust workflows, facilitate adoption and provide training.

Data readiness is another critical factor. The organization needs the right data assets, as well as processes to keep the data clean and updated. Data can be overwhelming, but focusing on a specific use case narrows it down. Once a potential application is identified, work backward to determine which data elements are crucial and whether that data is complete, accessible, accurate and comprehensive.

Some organizations start with data before defining a use case, which often results in setbacks. For instance, without a standardized, documented process, it’s difficult to automate with AI effectively because there isn’t a playbook on which to model the new systems.

In our experience, organizations can avoid many potential pitfalls and achieve better AI outcomes by following a specific order of operations. Amid the great promise of AI, it takes discipline to recognize that out of hundreds of ideas, only a small percentage are genuinely ready for AI deployment.

Generating ideas is rarely the issue; most organizations will have plenty of ideas for AI use. But without a defined problem, technology alone won’t improve things. Once the problem and process are defined, leaders can assess whether AI is the right tool or if an alternative approach might be more effective.

Skipping these steps can lead an organization to invest its resources unwisely and create disillusionment with AI that hampers future innovation. When AI is hyped as a magic solution and doesn’t deliver due to lack of strategic planning, skepticism can grow. Instead of blaming a lack of process definition, users may end up blaming the technology itself.

AI can indeed be transformative. At CDW, we see this every day. But the best results come when organizations do their due diligence before implementing an AI use case.