Authored by Paul McNamara, Co-founder and Senior Partner ADAPTOVATE.
Paul McNamara leads our Agile practice, helping organizations to be more focused on customer and business value, deliver better outcomes and achieve faster time-to-market.?
AI is Redefining Work Across All Industries
AI is revolutionizing the way we work across industries. With ideas flowing from all corners of the business, and teams passionate about AI use cases, there’s no shortage of enthusiasm or ideas. In fact, AI adoption is growing exponentially, with a McKinsey’s Global AI Survey showing that 91% of respondents are regularly using GenAI for work (McKinsey).
Balancing Enthusiasm with Strategic Investment
This enthusiasm can lead to an overwhelming number of projects that may not all have the same level of impact. The key challenge for leaders is to harness the passion while guiding investments towards initiatives that will drive significant, measurable value, without sinking time and money into a myriad of uncoordinated projects.
- Knowing what to do: With AI advancing at exponential speed, companies face the unique challenge of maintaining a coherent AI strategy that remains relevant. It’s tempting to greenlight every promising AI concept, but doing so risks diluting resources and focusing on projects that may not deliver meaningful business outcomes. This risk is exacerbated by the rapid pace of AI development; a strategy developed today can quickly become outdated, limiting the long-term value of current initiatives. In companies that are experimenting with AI, only 17% say their gen AI strategies align greatly with their business strategies (McKinsey).
- Doing it cost effectively: Without a consistent framework, organizations may fail to differentiate between projects that require centralized oversight and those that can be executed independently by various teams. Lacking this balance can lead to fragmented efforts, duplication of work, and misalignment with business goals. According to RAND, by some estimates more than 80% of AI projects stall before reaching production due to these very issues (RAND).
Establishing a Strategic AI Decision-Making Framework
To address these challenges, companies must create a decision-making framework that enables rapid, yet strategically aligned, investment in AI. This framework should help identify “needle-moving” AI initiatives—those that will significantly increase returns, reduce costs, or reshape core processes.
Key Components of a Robust AI Decision Framework
- Steer the ‘Needle-Movers’: AI initiatives that significantly increase returns, require large technology investment, or demand extensive change management are the ones that need central oversight. These are the projects that transform how the business operates—impacting not just costs but also workforce dynamics and regulatory compliance. For example, an initiative to automate compliance processes across multiple departments might warrant centralized oversight given its impact on costs and regulatory compliance.
- Redefine Business Cases with a Future Lens: Traditional ROI metrics may not capture the full value of AI investments, especially in a rapidly changing business environment. AI’s impact will be felt most in the future, 5+ years from now, not on today’s workflows. Companies should develop AI business cases that anticipate future states of the workforce and business environment. This approach might include setting a “do-nothing” baseline to measure the incremental value of AI, adjusting for projected business growth, workforce changes, and industry shifts.
- Balance Centralized and Decentralized Innovation: Continue to allow business units and individual teams to pursue smaller-scale AI initiatives independently, enabling quicker experimentation and implementation. Empowering teams to test and iterate independently drives innovation while reducing bottlenecks at the organizational level. However, this approach rarely finds the ‘needle-movers’, as these ideas usually come from a greater diversity of thought, through cross-functional ideation and by taking people out of their comfort zone to challenge the ‘status-quo’. To achieve this, typically organizations require a centralized approach to this type of innovation, with repeatable processes and removing the traditional silos while empowering the innovation teams.
By applying these principles, companies can create a balanced AI portfolio, making it easier to allocate resources to projects with transformative potential while fostering innovation across all levels. As the AI landscape continues to evolve, companies will benefit from a dynamic framework that allows them to pivot their AI strategy in real-time, aligning investments with both current goals and future vision.
This article first appeared on www.adaptovate.com
