AI strategies are kind of destined to fail

"As a consultancy owner, I've been experimenting heavily with the headline AI applications for the better part of two years now. Our teams have tested it across dozens of products and use cases. Some experiments worked immediately. Others failed at first but succeeded six months later when the models improved. Some we're still figuring out. The results keep evolving. A lot of leaders are obsessing over AI strategies right now."

"But here's the issue. Technology is moving way faster than traditional planning cycles can handle. What seemed impossible in January becomes a commodity by June. GPT-4 launched in March 2023. By year-end, teams were already building multimodal AI and voice interfaces that didn't exist when they started planning. So, we've developed a posture instead of just a strategy. WHAT DOES "POSTURE" MEAN? A posture is a consistent way of thinking about when, why, and how to experiment as things evolve."

"For us, that starts with a simple filter. Before we experiment with AI on any problem, we ask: Does this fit our criteria? We built a framework called SPARK to help us decide: Scale: High volume or time-intensive tasks Pattern: Repeatable structures or behaviors Ambiguity: Needs perspective or ideation Redundancy: Been done before, will be done again Knots: Bottlenecks that slow people down If a potential concept hits at least two of these markers, we move forward with an experiment."

Practical AI adoption requires a posture that emphasizes continuous experimentation over fixed long-term plans. Rapid technological change often outpaces traditional planning cycles, turning once-impossible ideas into commodities within months. Establish a consistent decision framework to decide when, why, and how to experiment in real time. Use a simple screening filter — SPARK (Scale, Pattern, Ambiguity, Redundancy, Knots) — and only run experiments that meet at least two markers. Screening focuses resources on high-value opportunities, reduces wasted effort, and accelerates institutional learning and capacity to recognize valuable use cases over time.

Read at Fast Company