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. Detailed roadmaps, implementation plans, and resource allocation. I get it. Leadership wants clarity, stakeholders want commitments, and everyone wants to know the plan.
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. It’s the framework you use to make decisions in real-time when conditions keep changing.
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. If not, we wait. Screening helps us focus on high-value opportunities instead of throwing spaghetti at the wall to see what sticks.
WHY THIS COMPOUNDS OVER TIME
Here’s what happens when you develop a clear posture: You get faster at recognizing valuable opportunities. You build institutional knowledge about what works in your specific context. You learn when to push forward and when to wait for technology to mature.
One team we work with started experimenting with AI for customer support triage in early 2023. The initial results were mixed. AI frequently misrouted tickets and gave generic responses.
Six months later, we came back to it. Better models, better prompting techniques, and a better understanding of what the AI could handle. This time it worked. They now process 60% of tier-one support interactions with AI, freeing their human team to focus on complex customer issues.
The difference wasn’t a better strategy. It was having a posture that included “when to come back to something we already tested.”
DEFINE YOUR OWN POSTURES
You don’t need to copy our framework. Build something that fits your business context, risk tolerance, and team’s capabilities. But it may be helpful to think through these questions:
- What types of problems are we willing to experiment with?
- What results would make an experiment worth scaling?
- How do we balance speed with responsibility?
- What triggers a decision to invest more deeply or move on?
- How do we capture and share learnings across experiments?
Having clear answers matters more than having perfect answers.
THE LONG VIEW
AI capabilities will only continue to evolve, and new use cases will emerge. Some of today’s cutting-edge applications will become commodities. Others will reach dead ends.
I believe that the companies who will thrive will be the ones who can consistently evaluate new opportunities, learn from results, and adjust as conditions change. They’ll have trusted experts who know where to experiment and when to scale. That’s what I mean when I say our AI point of view isn’t a snapshot. It’s a posture.
TL;DR The technology keeps moving. Our posture helps us move with it.
George Brooks is the CEO and founder of Crema.