For teams that know AI matters to their future but need a clear-eyed assessment of what to build, how to build it, and what to skip.
Every company I talk to is somewhere in the same cycle. Leadership has decided that AI is strategically important. The board is asking about it. Competitors are announcing AI-powered features. There's pressure to move, and it usually manifests as one of two patterns: either the team spins up a series of experiments that never converge into a product, or they commit to a major initiative before understanding whether it's the right one.
Both patterns share a root cause: the absence of a structured product thesis grounded in technical reality. And that gap is understandable. Most product leaders are not equipped to evaluate the technical feasibility and operational complexity of AI systems. Most engineering leaders are not practiced at framing technical capability in terms of product value and market positioning. The result is a strategy conversation where neither side has full information.
The AI landscape in 2026 is extraordinarily noisy. Foundation models, fine-tuning, RAG architectures, autonomous agents, multimodal systems, edge inference. The taxonomy alone is a full-time research job. For a product team trying to figure out where AI fits into their roadmap, the signal-to-noise ratio is brutal. I see teams waste months exploring capabilities that are technically impressive but commercially irrelevant to their customers. The first job of any AI strategy engagement is to filter ruthlessly: what matters to your users, what's technically feasible at your scale, and what creates durable competitive advantage rather than a feature that every competitor will have in six months.
This is the most common failure mode I encounter. A team builds a compelling demo: an internal tool, a proof of concept, a hackathon project that genuinely excites the company. But the path from that demo to a production-grade product is vastly more complex than it appears. The demo works on curated data. The production system needs to handle edge cases, adversarial inputs, drift, latency requirements, cost constraints, and user experience patterns that don't exist yet. I've watched teams discover this gap six months into development, after significant investment in the wrong architecture. The strategy sprint exists to surface these realities before the big commitments are made.
The build-vs-buy decision in AI is genuinely harder than in traditional software. The landscape shifts monthly. Capabilities that required custom model training a year ago are now available through API calls. Conversely, some things that look like commodity API features actually require deep customization to deliver real value. I've seen teams spend months building custom solutions for problems that are well-served by existing platforms, and I've seen teams lock themselves into vendor APIs for capabilities that should be proprietary differentiators. Getting this decision right requires both deep technical understanding and clear product thinking. Understanding not just what's possible, but what creates lasting value for your specific business.
The strategy sprint is designed to cut through all of this. Not with a generic framework or a deck full of quadrant charts, but with hands-on analysis of your specific technical landscape, your competitive position, and your team's real capabilities.
The strategy sprint is a structured engagement, typically four to six weeks, designed to produce a clear, actionable roadmap. Not a strategy document that sits on a shelf, but a working plan your team can execute against.
A rigorous evaluation of the AI opportunities available to your business. I look at your existing data assets, your product surface area, your competitive landscape, and your customer needs to identify where AI creates genuine value rather than novelty. This isn't a survey of what's technically possible. It's an analysis of what's commercially meaningful for your specific situation.
A clear articulation of what you're building, why it matters to your customers, and how it fits into your broader product strategy. The thesis connects technical capability to customer value and market positioning. It gives your team a decision-making framework for the hundreds of smaller choices that follow.
An honest assessment of what your team can build, what infrastructure you need, and where the real technical risks are. I draw on twenty-plus years of building production AI systems to evaluate feasibility in practice, not in theory, given your team's skills, your data quality, and your operational constraints.
A phased plan with concrete milestones, clear decision points, and explicit build-vs-buy-vs-integrate recommendations for each component. The roadmap is designed to de-risk the initiative by starting with the highest-uncertainty elements so you learn fast and can adjust before committing fully.
For each component of the roadmap, a clear recommendation on whether to build custom, buy off-the-shelf, or integrate existing services. Each recommendation includes the reasoning, the trade-offs, and the conditions under which you'd revisit the decision.
The sprint typically runs four to six weeks. It involves deep engagement with your product, engineering, and leadership teams. I'm not observing from the outside. I'm embedded in the work, reviewing code and architecture, sitting in on planning sessions, talking to customers if appropriate. The output is a plan your team believes in because they helped shape it.
Most AI strategy consultants come from a software-only background. They can talk about LLMs and cloud architectures, but their frame of reference is narrow. My background spans AI systems that interact with the physical world: sensors, robotics, mixed reality, edge computing, IoT, geospatial data. That means I think about AI product strategy differently than someone who's only built SaaS features.
More importantly, this isn't a slide deck engagement. I don't produce a strategy document and walk away. The sprint produces a working roadmap grounded in your team's real capabilities, your actual data, and the technical constraints that will determine whether this initiative succeeds. The output is something your engineering team can start executing against, not something your leadership team presents and then shelves.
I bring over twenty years of building production systems across AI, cloud, IoT, robotics, and spatial computing. That depth matters because the hardest strategy decisions in AI are fundamentally technical decisions, and getting them wrong is expensive.
If you're evaluating AI opportunities and want a structured, technically grounded approach, let's talk about a strategy sprint.
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