AI readiness strategy grounded in operational reality.
You know AI matters to your business. But you don’t know which pain points it can actually relieve, where it is likely to slow you down, what to tackle first, and how to separate real opportunities from expensive distractions in a landscape full of hype.
I help clients think through those questions from a whole-system perspective, because your business context is upstream of any technology decisions. It is the integration of AI with your company’s process, people, requirements, and review structure that will determine success or failure. Grounding strategy in the realities of your business helps ensure that an AI initiative is useful, usable, and worth the investment.
How I help
Opportunity assessment
The AI landscape is bewildering; the industry is trying everything to see what sticks.
I help identify where AI can create real value in your organization, where it is likely to disappoint, and which opportunities are worth pursuing first.
Readiness planning
AI can only be helpful if it fits your company’s processes, requirements, inputs, and review structure.
I work with you to determine what needs to be in place to turn a promising idea into a viable initiative – identifying gaps, constraints, and prerequisites before rollout begins.
Implementation strategy
An effective AI rollout is planned and organized around clearly articulated goals and carefully calibrated ambition.
We collaborate to design an AI adoption strategy for your business priorities, whether that is a proof-of-concept, pilot project, or full-scale phased deployment.
Adoption facilitation
People embrace AI when it makes their jobs easier, not harder. A good rollout pairs training on the tool’s capabilities and limitations with workflows that help people do their work.
When AI makes practical sense to users, it builds trust and enthusiasm instead of confusion, resentment, or checkbox compliance.
A whole-system perspective on AI adoption
Most AI conversations focus on the tool, but the harder and more important questions sit around it: workflow, requirements, information quality, review structure, trust, and accountability. Identifying the most acute pain points helps us design an AI strategy that fits the way your business actually operates.
That is where I work. I am not an AI booster, an automation builder, or a product promoter. My role is to help organizations understand AI in the context of their actual needs, so they can make better upstream decisions before committing significant time, money, or trust.
Technology adoption in the real world
Large language models are a frontier technology, but the fundamental challenge they present is familiar.
Pursuing sustainability through hard-tech innovation has meant living outside the comfort zone of established practice: evaluating novel technologies, asking what they actually require, and thinking through how they will perform in the real world.
The same questions apply to AI adoption: What problem is actually being solved? What does the technology require from the organization around it? How will it interact with real workflows, constraints, and incentives? And what happens when it reaches the field? That broader perspective helps clients make clearer decisions about where AI belongs, how to introduce it, and where not to force it.
Selected writing and talks
A few public-facing pieces that reflect how I think about AI, technology adoption, and the broader questions around innovation in the real world.
Featured conversation
Systems Thinking in the Built Environment
A conversation with Advisor AI Solutions about AI, work, trust, and the practical realities of adoption beyond the hype.
Featured roundtable
The AI and Energy Scenario Exercise
A 2-part roundtable game hosted by DSR’s Siliconsciousness, exploring potential energy policy in the current and hypothetical next Administration in the context of the AI boom.
Each participant represents a political, economic or social interest or entity. I speak for the public interest in the environmental sector.
I start at ~39:57 in Part 1
I start at ~32:12 in Part 2
More to come
Additional essays and conversations will be added here over time.
