How to Identify and Prioritize High-Value AI Opportunities
AI is no longer a "nice-to-have" in organizations—it's a must. Seventy-three percent of U.S. companies integrated AI into at least one aspect of their operations this year, and 85% of business leaders plan to augment their AI investments. Despite widespread adoption, many organizations are still experimenting, seeing small pockets of success with little to no impact.
With greater investment comes greater responsibility. The question is no longer whether or not to invest in AI. It's how to identify AI opportunities that drive the most value for your organization—and set them up to scale. In this post, we'll explore proven practices to help you identify and prioritize the initiatives that will deliver the most value for your organization, building the foundation for sustainable and transformational AI applications.
What makes an AI opportunity high-value?
A high-value AI opportunity isn't limited to one that adds to your bottom line. The technology is new. More applications—and value—are unlocked each day. A potential AI opportunity is valuable if it applies today's technology to solve your unique business needs. Put simply, strategic alignment drives value. Consider how AI can help you achieve your strategic objectives and how it can alleviate your most pressing pain points. By engaging in this reflection, AI will be a core business accelerant rather than a disjointed cost center.
Conduct a gap analysis
A great place to start identifying investment-worthy AI initiatives is to examine gaps and needs across the organization. No one team will have the complete picture or technical know-how to determine feasibility, so it's crucial to be open to potential gaps across functional areas and invite diverse perspectives.
Go wide, then narrow. When identifying AI opportunities, aim for a large subset that you can filter down. Look at specific pain points that—if resolved—could drive significant improvement. Also, consider new areas of business growth and identify how AI could enable and accelerate them. It's essential to begin with a plethora of AI use cases as talent gaps, limits in technical infrastructure, and lack of alignment will naturally shrink the initial pool.
Assess business value at scale. Evaluate the potential benefits—both quantitative and qualitative—of addressing these challenges. Compile all necessary information to make a case for how each solution will generate value. Remember that you'll likely not see ROI until you deploy your solution and it's fully adopted—which can be in the third year or beyond. Focus on future value, not just what you can achieve this year.
Observe your competitors
Benchmarking can be an illuminating practice for identifying high-value AI opportunities. Compare your organization against industry standards and your competitors. Are there areas where comparable organizations are applying AI to gain a competitive edge? For example, if your competitors use AI for predictive maintenance—and you're not—you may be looking at a valuable opportunity.
Stay informed about industry-specific AI trends. Routinely monitor how AI is being used in your industry to stay ahead of emerging trends. Plan to attend conferences held by industry associations—or browse the event schedule—for AI-related topics. This practice will keep you abreast of your industry's latest AI applications and best practices.
Conduct a competitive analysis. Study your competitors' AI strategies to identify gaps and areas where your organization can differentiate and improve. Aside from the typical advice of browsing competitive websites and social feeds, you can also review the 10K filings and Investor Day presentations of publicly traded competitors for information about technology investments and AI strategies.
Conduct innovation-focused workshops
We glean the most valuable insights when people with diverse perspectives put their heads together to tackle challenges and explore new opportunities. You can facilitate idea generation by facilitating cross-functional brainstorming workshops to gather potential AI use cases. In these forums, the most innovative ideas surface—ideas that often don't emerge through traditional top-down planning.
Foster cross-functional collaboration. AI is improving organizations from the bottom up. You can workshop ideas generated from leadership, but the most valuable applications will likely come from front-line associates. Cross-functional collaboration is most effective when each functional leader first gathers ideas within their own areas. Then, bring cross-functional team members together for diverse perspectives and insights. This order of operations makes for a more efficient use of time and sets the stage for widespread adoption.
Promote open innovation. At this stage, the goal isn't to limit yourself to a few AI use cases—it's to generate as many as possible. Encourage out-of-the-box thinking. Allow participants to propose bold, unconventional ideas. Be sure you're asking the right questions and modeling an open mind in your narrative. If someone says, "We can't do X because of Y," reframe it by saying, "If we want to do X, what else needs to happen to make it possible?"
Create a prioritization matrix
Once you've identified a vast pool of potential AI use cases, it's time to prioritize them based on impact, feasibility, and strategic alignment. A prioritization matrix is a framework that allows you to visualize and more easily prioritize AI initiatives—directing your investments to those that deliver the most significant returns.
When creating your prioritization matrix, map AI opportunities across several criteria. Though there may be unique criteria for your organization, the most common criteria include the following:
Business Value: Projects that align with long-term strategic goals deliver the most significant value. Improving customer experience and operational efficiency are common business objectives that AI can accelerate.
Feasibility: Evaluate the technical readiness, data availability, and organizational capacity to implement and scale the project. AI projects require not only the right technology but also an organization that's ready to embrace change.
Scalability: Consider the ease or difficulty in expanding a given AI initiative across different departments or regions. Scalable projects allow you to realize more long-term value.
Risk Management: AI initiatives carry risks, particularly around data, security, and compliance. Governance frameworks help mitigate them. Weigh the risk and cost of mitigation for each initiative.
Financial Commitment: Discuss how you'll allocate the initial—and ongoing—budget for your initiative’s success up front. Ensure your organization can commit these resources in the short and long term.
Portfolio Mix: Balance quick wins and large-scale initiatives in your AI roadmap. Some AI initiatives, such as improving data analytics, may deliver value quickly. Others, like integrating AI into supply chain operations, may take longer but offer more long-term value.
Scoring your AI initiatives in the matrix enables productive conversations about what to prioritize. The logical answer may seem to focus on high-value and feasible near-term projects, but that might not be the case, depending on your organization's goals and portfolio mix. Invite a range of voices to participate in the prioritization discussion—including external AI experts—so you can select the best opportunities for your organization's needs.
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AI is essential to growth and innovation; mere investment isn't enough. The success of your AI efforts hinges on your ability to identify and prioritize projects that drive value. When you consider AI's potential in all areas of your organization, invite in different perspectives, and use a systematic framework to prioritize projects, you ensure the successful integration of AI into your business operations. With thoughtful consideration toward strategic alignment, AI becomes more than innovation—it becomes a driver of core business growth.
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