Find · Build · Scale
Most companies do not need more AI ideas. They need a disciplined way to choose the right opportunities, implement them, and scale what works.
The problem
Your company probably already has Copilot, ChatGPT, Claude, or some combination. People are experimenting. A few have become power users. Others barely touch them. Departments are starting their own initiatives. Leadership sees the potential but still has a harder question:
Where should we actually use AI?
Two ways to start
Build executive capability. Apply AI to the work that matters.
AI Lab · Focused working session · Adoption Program · Twelve weeks
Help executives use AI on real work and build habits that last. Begin with a focused working session or continue through a twelve-week adoption program that turns initial momentum into daily use.
Shaped around the business problem
Find the few workflows where AI can create meaningful value, then turn the best one into a working capability your team uses.
The method
These are stages of the work, not four separate products.
Build practical understanding around the real work.
Identify the few opportunities worth pursuing.
Turn the best opportunity into a working capability.
Extend what works with the right standards and support.
The approach
The work starts with your organization, not a vendor or a technology. Before anything gets built, we look at how work actually happens and where it breaks down.
Inside the Applied AI Program · Find
We examine how work happens across functions, identify where AI can create meaningful leverage, and score opportunities against value, feasibility, risk, complexity, and time to value.
You leave with an AI Opportunity Map, a short list of recommended pilots, and a 90-day roadmap.
Interview leaders and selected employees. Review strategic priorities, workflows, and pain points.
Document high-value workflows and potential AI opportunities across functions.
Evaluate business value, feasibility, implementation complexity, risk, data readiness, and time to value.
Select the small number of initiatives worth pursuing first.
Create a practical implementation roadmap with owners, sequence, and decision points.
How opportunities are scored
Scoring is not a formality. It is what turns a long list of plausible ideas into a short list of defensible decisions.
Time recovered, quality improved, decisions accelerated, or expertise extended.
Whether current tools, data, and systems can support it now.
Integration surface, number of teams involved, and change required.
Privacy, security, regulatory exposure, and the cost of being wrong.
How quickly the workflow could realistically improve.
Illustrative example. Real maps are built from your workflows and cover the full set of scored opportunities.
Scan deliverables
Every deliverable is built to be used in a decision, not filed after a readout.
Typical engagements run four to eight weeks depending on company and functional scope, and close with an executive readout and decision session.
Inside the Applied AI Program · Build
The Sprint moves one selected opportunity from concept to something your team actually uses. The objective is not custom software unless custom software is necessary. The objective is to solve the workflow.
Usually four to eight weeks, one clearly bounded workflow, a small joint team, and a weekly working cadence.
Current workflow, users, inputs, outputs, decisions, and the baseline we will measure against.
The simplest viable architecture. Configuration before integration, integration before custom software.
The skill, agent, workflow, configuration, integration, or prototype the workflow actually needs.
Against real historical work and known outcomes, not synthetic examples.
With real users, where security and systems permit.
Adoption, quality, time saved, and business impact against the baseline.
Stop, improve, expand, or scale. Including the honest answer when it is stop.
Where sprints usually start
Variance analysis, management reporting, board preparation, document processing, financial research.
Contract review, NDA analysis, redline identification, policy analysis, knowledge retrieval.
Employee support, talent review preparation, recruiting workflows, policy access, job architecture.
Weekly reporting, issue detection, root-cause analysis, process documentation, operational knowledge.
Account research, proposal preparation, pipeline intelligence, meeting preparation, competitive research.
Research, content workflows, campaign analysis, customer insight, competitive intelligence.
Inside the Applied AI Program · Scale
Once pilots work, demand increases. Departments build independently. Vendors multiply. Teams recreate the same capabilities. Governance gets harder.
At that point AI adoption needs an operating system. This is the ongoing advisory and implementation relationship that builds it.
A single pipeline for new opportunities, so good ideas do not depend on who happens to ask.
Experiments, pilots, and production capabilities tracked in one place with real status.
Shared patterns for skills, agents, prompts, workflows, and organizational context.
Human review, security, privacy, and risk controls that people can actually follow.
Adoption, quality, time saved, and business value, baselined and tracked.
Patterns that move across functions instead of being rebuilt six times.
Vendor independence
The question is never “what can we build with this tool?” It is “what workflow is worth changing?”
Then we choose the simplest credible solution. That protects you from unnecessary custom development and from lock-in to a platform chosen before anyone understood the problem.
Questions
Typically four to eight weeks, depending on how many functions are in scope and how quickly interviews can be scheduled.
A single-function scan sits at the short end. A broad scan across finance, legal, HR, and operations sits at the long end.
Access to the people who do the work, an executive sponsor who can make prioritization decisions, and enough context on strategic priorities to judge what actually matters.
The heaviest lift on your side is interview time. We keep it bounded and scheduled up front.
Only if that is genuinely the right answer, and it usually is not. The default is to solve the workflow with the simplest credible option, which is often configuration of tools you already own.
Software development beyond a bounded sprint is scoped separately, so it is a deliberate decision rather than a drift.
As a partner, not a replacement. The practice is vendor-independent and has no product to sell you, which tends to make these conversations easier rather than harder.
In most engagements the internal technology team is involved from the Scan onward, because they hold the constraints that determine what is actually feasible.
We say so, and we say why. A sprint that ends in a clear stop recommendation, with evidence, is a useful outcome. It costs far less than discovering the same thing after a company-wide rollout.
We baseline before building. That means agreeing up front what the current workflow costs in time, quality, or delay, and how we will know whether it improved.
Without a baseline, every AI project succeeds. That is not useful to anybody.
Start somewhere useful
Tell us where you think the opportunity sits, or tell us you have no idea. Both are a fine place to start.