Strategic guidance for AI transformation. Rank, sequence, and revisit your AI bets using evidence you can defend.
The Problem
Most organizations under pressure to use AI are not short on ideas. They have experiments running in several teams, vendor recommendations, promising demonstrations, and a long list of possible use cases.
What they lack is a shared way to decide which opportunities matter, which are ready, and which should stop. Pressure to “do AI” rewards activity, so pilots accumulate faster than decisions, and one early approval quietly becomes permission for everything that follows.
- You have competing AI opportunities and no defensible way to rank them.
- Pilots keep accumulating with no path to acceptance.
- Dependencies are being spent past before anyone has sequenced them.
- Teams move at different speeds with unclear ownership of the consequences.
Activity expands while accountability blurs, and an unready idea becomes an implied commitment before anyone decides it should.
The Solution
Fewer, clearer decisions
A useful AI strategy is a short set of reviewable decisions: proceed, narrow, hold, stop, or investigate further.
Each decision names the evidence behind it, the business decision or workflow it affects, the accountable owner, and the next gate. That gives leadership a defensible sequence instead of an unexplained score or a pile of unrelated pilots.
An actionable AI strategy is a set of reviewable decisions about where AI belongs, where it does not, in what order initiatives should proceed, under what operating model, and with which measures and stop conditions.
When AI Strategy fits
You have competing AI opportunities and no defensible way to rank them.
A plausible capability does not establish business value. A polished prototype does not resolve ownership, review capacity, system readiness, or the consequences of a wrong answer.
Initiatives have dependencies that must be sequenced before spending expands.
We examine the business decision or workflow each opportunity affects, who is involved, and what happens when an answer is wrong.
Evidence, readiness, ownership, or approval questions remain unresolved.
You can bring the pilots, signals, or data behind each opportunity.
We weigh value mechanism, evidence, reversibility, readiness, dependencies, and ownership to create a sequence leaders can revise.
You need the option to hold or stop an initiative on the record—not only to proceed.
A productive hold records what is missing, who owns the gap, and what evidence would justify reconsideration. It prevents an unready premise from becoming an implied commitment.
How We Work
Opportunity and workflow inventory
Identify candidate decisions and workflows as they operate today, including the people affected, existing evidence, systems involved, and consequences of error.
Prioritization and sequencing
Determine which opportunities should proceed first, which depend on other work, and which should be deferred or stopped.
Evidence and risk requirements
Define what must be known or demonstrated before a decision advances.
Ownership and approval design
Make clear who recommends, who approves, who owns the consequences, and where another decision is required.
Operating-model recommendations
Define how AI work should be assigned, reviewed, accepted, and handed into implementation.
Explicit holds and next-step routing
Record unresolved conditions and route approved work into research, a bounded pilot, implementation, governance design, or ongoing operations.
Results.
You need a partner who's built production AI systems — not just prototypes.
A Defensible Sequence of AI Decisions
After the engagement, leadership holds a small, inspectable set of decisions rather than a backlog of unrelated pilots.
The practical change is straightforward: AI investment follows evidence in a sequence leaders can challenge and revisit, instead of following demonstration appeal or vendor momentum.
View All Work
Pricing
AI Strategy is generally shaped as a fixed-scope advisory project. Current indicative planning ranges run from approximately $15,000 for a focused, single-decision review to $60,000 for a broader portfolio spanning several teams.
The number of opportunities and workflows, availability of supporting material, stakeholder and reviewer count, and depth of operating-model recommendations drive scope. We scope based on your specific requirements.
Common Questions
We already have AI experiments. Is it too late for strategy?
No. Existing experiments provide useful material. Strategy determines which should become commitments, which need more investigation or controls, and which should stop.
How should we prioritize competing AI use cases?
Start with the decision each use case affects, then compare its value mechanism, supporting evidence, reversibility, readiness, dependencies, and ownership.
How do you decide where AI should not be used?
We look for inadequate support, missing authority, hard-to-reverse consequences, insufficient review capacity, or an existing tool that already meets the need.
What is the difference between AI strategy and implementation?
Strategy produces decisions and sequence. Implementation adds a bounded capability or builds a complete system after the relevant decision has been made.
How should an AI pilot be measured?
Choose measures tied to the decision the pilot is meant to inform, and define them—along with stop conditions—before the pilot begins.
Can we start with one function or workflow?
Yes. One decision area provides a concrete place to examine readiness, ownership, and sequencing without assuming an organization-wide program.
Ready to start?
Let's Discuss the Next Decision
Bring one area where AI activity or pressure already exists: what is in motion, what supporting material is available, where authority sits, and which decision leadership is considering.
Mercury can help determine whether that decision should proceed, narrow, hold, stop, or move into further investigation. Contact Mercury to start with one decision.