AI strategy consulting is measured by what runs, and most mandates never reach that point. A leadership team receives a mandate to put AI to work, and the roadmap that follows describes capability without producing a workflow. The desk that watches the Federal Register, triages the shared inbox, and produces first drafts is still doing it by hand. An internal champion has a personal automation account nobody sanctioned, security has no policy to enforce, and the board is asking what the AI budget produced. The gap is not ambition or spend. It is the absence of a readiness assessment that ends in a ranked list, a sanctioned platform, a written check, and a named owner for the first workflow.
Our AI services practice runs strategy the way it runs builds: as revenue infrastructure with a person at the point of decision. The engagement opens with an AI readiness assessment across data, systems, people, and policy, and closes with a ranked workflow portfolio, a sanctioned platform, a written AI policy, a named owner per workflow, and the first workflow running inside the systems your desk already uses. Three rules govern every recommendation: sanction the platform, keep confidential data inside the firewall, and every agent stops for a person. Every build runs on the platforms you already run and sanction, and you own the result. Agents feed your CRM, ETRM, scheduling, and portal systems as the intake clerk feeds the ledger. The fractional chief AI officer retainer carries the plan forward with quarterly reviews and build capacity.
A written assessment of your data, systems, people, and policy: where the sources live, which systems of record hold the truth, who on the desk carries the monitoring and drafting load, and what governance exists now. It closes with a ranked list of candidate workflows and a plain statement of what has to change before the first one is built.
Every candidate workflow scored against the selection rule and ranked. Each entry names the trigger, the sources, the one thinking step, the defined action, the stop point where a person decides, and the owner. Three workflows are marked first: a monitoring brief, a first-pass document workflow, and one repeatable operational task.
The three rules written as policy your security and compliance teams can enforce: sanction the platform, keep confidential data inside the firewall, every agent stops for a person. The document covers approved sources, data classification by workflow, the written check and named owner for each workflow, audit log requirements, and the review cadence a regulated business expects.
A written recommendation on one or two platforms, with the reasoning: Microsoft Copilot Studio where the tenant is Microsoft 365, Make.com or n8n where the workflows cross many systems, Zapier where simplicity wins, and Google Cloud Run only for a step that outgrows the workflow platform. It includes the enterprise account setup and who holds permission to build.
Five moves sequenced to the first workflow, with owners and the definition of done for each: fundamentals learned on the free courses, friction audited, three workflows picked, the check written before deployment, one owner named per workflow. The plan ends with the first workflow built and running on the sanctioned platform, not with a slide.
AI leadership on retainer after the roadmap: a quarterly review of what ran and what stalled, a refreshed workflow portfolio, governance updates as platforms and regulations move, and build capacity from our practice for the next workflows on the list. Your team keeps ownership of every workflow; our practice keeps the plan honest.
Our team interviews the desk, inventories the sources it watches and the systems it feeds, reviews existing policy and platform accounts, and maps where the hours go. The output is the written assessment report with a first ranked list of candidate workflows. Nothing is bought and nothing is built yet.
We score every candidate against the selection rule, pick the three workflows to build first, and decide the platform with IT and security in the room. The enterprise account is opened, permission to build is granted, and the data policy for each workflow is agreed before a single module is placed.
We write the AI policy with your compliance and security leads: the three rules, approved sources, data classification, the written check and named owner for each of the three workflows, and the audit log each one keeps. The policy is signed by security before the build starts, so the first workflow launches already compliant.
Our practice builds the first workflow on the sanctioned platform, runs it against the test log, and hands it to the named owner with a runbook and a train-the-owner session. The owner can read, edit, and switch it off. The other two workflows are scoped for the program or the retainer that follows.
At the close of the roadmap we review what ran, what the owner changed, what the audit log shows, and what the desk wants next. The portfolio is refreshed and the governance updated. From here the engagement either closes with a client-owned workflow or continues as a fractional chief AI officer retainer with quarterly reviews.
AI strategy consulting is the work of deciding which workflows an organization should hand to AI first, on which platform, under what policy, and who is accountable, then proving the decision with a running workflow. Our AI services practice opens with an AI readiness assessment across data, systems, people, and policy, ranks the workflow portfolio by one selection rule, sanctions the platform with IT and security in the room, writes the governance a regulated business can enforce, and closes with the first workflow running inside the systems your desk already uses. The artifact at the end is a workflow with an owner and an audit log, not a maturity model.
Every build runs on the platforms you already run and sanction, and you own the result. Agents feed your CRM, ETRM, scheduling, and portal systems as the intake clerk feeds the ledger. We run the same stack on our own intake and regulatory monitoring.
Our founder has taken part in more than 100 energy and utility industry events, was named the 2025 FMEA Associate Member of the Year, and taught this material on the 2026 LDC Gas Forums technology and AI panels (Southeast, Northeast, Rockies and West, Mid-Continent), so the sources and vocabulary in your portfolio are the right ones. AI adoption consulting only counts when something runs.
What our team inspects before recommending anything
An AI readiness assessment inspects four areas: the data a workflow would read, the systems of record it would feed, the people who would own it, and the policy that governs it. The output is a written report with a ranked list of candidate workflows and a plain statement of what has to change before the first build.
Data means the sources. For a gas supply desk that is the Federal Register, FERC dockets, pipeline informational postings, and state commission dockets. For a proposal team it is RFP portals and the shared inbox. For marketing it is the review feeds and the CRM. We record which sources are public, which are approved, and which carry confidential material that must stay inside the firewall. Systems means the record of truth: HubSpot or Salesforce, the ETRM, the scheduling portal, Microsoft 365 or Google Workspace. Every candidate workflow is mapped to the system it feeds, because an agent that cannot write to the ledger is not yet a workflow.
People means the desk. We interview the coordinator, analyst, or manager who watches the sources and produces the first drafts, and we time where the hours go. We also look for the internal champion with a personal automation account, because that person is usually the first owner. Policy means what exists now: platform accounts, data classification, any AI use guidance, and the audit expectations of your regulator or customers. The common pattern is strong systems, a capable desk, no sanctioned platform, and no policy. The report says so plainly, ranks the candidate workflows, and states what has to change in each area before the first build starts.
Example: Our own assessment produced two first workflows. The intake pipeline routes every call and form through Claude scoring into HubSpot with a person alerted in seconds, and the FERC Gas Notice Watch reads every FERC natural gas notice from the Federal Register API and stops for a person on the ones that matter.
The selection rule behind our agentic AI consulting, and why three beats thirty
We rank workflows by one rule: the work has language or pattern shape, and it has a natural point where a human already checks it. Reading a notice, triaging an inbox, and drafting a first pass qualify. Pricing a deal or approving a filing does not. The rule produces three workflows to build first, not thirty.
Language shape means the input is text a model can read: a Federal Register notice, an RFP, a customer email, a review, a transcript. Pattern shape means the input repeats in a form a model can recognize: a nomination confirmation, an invoice, an interconnection queue update. A natural human check means someone already reviews this work before it moves downstream, so the stop point exists and the agent inherits it. Work that fails either test stays with people. A portfolio that skips this rule produces a long list of agents that never ship, because the first one picked needs a developer, a new data source, or a decision nobody is willing to hand over.
The three we mark first follow a shape that works across industries. One monitoring brief: an agent that watches a source and delivers a ranked summary each morning, with the items that matter flagged for a person. One first-pass document workflow: an RFP, a tariff filing, or an inbound proposal read and summarized against your checklist before anyone opens it. One repeatable operational task: the inbox triage, the review reply draft, the nomination confirmation logged to the system of record. The friction audit comes before any of this, and before anything is bought. We time the manual work, count the sources, and rank by hours and risk, then decide what to buy.
Example: The FERC Gas Notice Watch passed the rule on both counts. A Federal Register notice is language a model can read, and the regulatory desk already reviewed every notice by hand, so the stop point existed. On the record run, 10 entries were pulled, 4 combined-filings notices were skipped, 6 were read, 5 were filed ROUTINE, and 1 was held for a person.
Governance that security can sign and a regulator can audit
AI governance consulting turns the three rules into a policy security can enforce: sanction the platform, keep confidential data inside the firewall, and every agent stops for a person. The written AI policy for companies in regulated industries adds approved sources, data classification, a written check and a named owner per workflow, and audit logs.
Sources come first because they are the easiest place to leak. The policy names which sources are public (the Federal Register, FERC and state commission dockets, ISO notices, published tariffs), which are approved internal sources (the CRM, the shared inbox, the document library), and which are confidential and stay inside the firewall entirely (counterparty pricing, customer records, anything under a nondisclosure agreement). Data classification then follows each workflow: what it reads, what it writes, and where the model runs. A workflow that reads only public notices and writes a ranked summary to an inbox sits in a different class from one that reads customer records, and the policy says which platform and which model each class is permitted to use.
The written check and the named owner are the clauses a regulator reads. For every workflow the policy states, in a literal checklist, what the person confirms before an output moves downstream, and it names the person. In our FERC watch the check is four lines and the review email ends with the words the agent stops here, followed by a request to reply RELEASE or HOLD. The audit log records every run, every decision, and who made it, in a store the owner and the auditor can both read. Governance written this way is not a brake on the program. It is the reason security signs and the reason the first workflow launches already compliant.
Example: The FERC Gas Notice Watch keeps its governance in the workflow itself. HIGH notices trigger a review email that ends with the agent stopping and asking for RELEASE or HOLD. A second workflow polls the reply every 15 minutes, logs the decision, and hands off to the regulatory desk with a four-line checklist.
One or two platforms, an enterprise account, and permission to build
Sanctioning the platform means choosing one or two platforms your IT and security teams stand behind, starting with the ones you already run, opening the enterprise account, and granting the desk permission to build. Microsoft Copilot Studio, Make.com, n8n, and Zapier cover nearly every first workflow. Google Cloud Run enters only when a step outgrows them.
The choice follows the tenant and the workflow map from the assessment. Copilot Studio fits an organization that lives in Microsoft 365 and wants agents inside Teams and SharePoint under the identity and data controls it already administers. Make.com and n8n fit workflows that cross many systems, with visual scenarios the owner can read and a module for Claude, OpenAI, or Google Gemini as the one thinking step. n8n can be self-hosted when data residency requires it. Zapier fits simple handoffs where the team already knows it. We recommend one or two, never five, because the platform sprawl of a personal account per champion is the shadow IT the policy exists to end.
Code belongs at the edge, not the start. The first workflow belongs on a low-code or no-code platform the owner can read, and if it needs a developer to start, it is the wrong first workflow. When a step outgrows that platform, a scoring model with many fields, a document parser, a transcript pipeline, our practice builds that step as a small Google Cloud Run service the scenario calls, and the client owns the repository. Our own intake pipeline runs this way: Make.com scenarios post each call and form to a Cloud Run service where Claude scores ICP tier, urgency, and a follow-up recommendation, and HubSpot receives the result. The visual workflow stays readable by the owner. The code stays small, documented, and inside your account.
Example: The FERC Gas Notice Watch was built in one evening on Make.com, a low-code or no-code build with 10 modules and zero lines of code, using Claude Haiku 4.5 through Make's Claude module with no API key required. That is what a sanctioned platform and permission to build look like in practice.
Five moves, one running workflow, then leadership on retainer
The AI roadmap is five moves in order: learn the fundamentals free first, audit the friction, pick three workflows, write the check before you deploy, and name one owner per workflow. Then build. The fractional chief AI officer retainer carries the plan past the first workflow with quarterly reviews, portfolio refreshes, governance updates, and build capacity.
Move one costs nothing. Before any platform is bought, the desk completes the free fundamentals: Anthropic Academy AI Fluency, Google Prompting Essentials, Oracle Agentic AI Foundations 2026, Harvard CS50 AI, and MIT 6.S191. The education is free and the first workflow the desk builds itself is low-code or no-code, so there is no budget excuse left. Move two is the friction audit from the assessment. Move three picks the three workflows by the selection rule. Move four writes the check before deployment, as a literal checklist, because the same facts and the same model produced three different answers under three prompts in our own FERC build, and the triage was wrong until the deciding test came first. Move five names one owner per workflow.
Then build. The first workflow goes live on the sanctioned platform with a runbook, a test log, and a trained owner. That artifact is the point of the roadmap, and it separates a plan that ends in something running from a mandate that ends in a deck. Past the handoff, the fractional chief AI officer retainer keeps the program moving without a full-time hire. Each quarter our practice reviews what ran, what stalled, and what the audit logs show; refreshes the ranked portfolio with the desk; updates governance as platforms, models, and regulations move; and brings build capacity for the next workflows on the list. Leadership gets an AI strategy consultant who reports quarterly. Fractional marketing leadership, a fractional CMO, is offered on the same retainer model at /services/fractional-cmo.
Tip: Write the check before the build, as a literal checklist the reviewer reads top to bottom. In our FERC build the triage was wrong under two prompts and right under the third, and the only change was moving the deciding test to the top. Knowing what a wrong answer looks like in your market is the scarce skill.
Agency inbound intake (our own build). Our practice applied its own readiness pattern before selling it. Inbound leads arrived by voice and by web form, scoring was manual, and HubSpot lagged the conversation. A voice agent on the main line and a form handler feed Make.com scenarios that post each lead to a Google Cloud Run service where Claude scores ICP tier A to D, urgency, and a follow-up recommendation. HubSpot receives the contact, the deal with AI fields, and the call engagement with the recording link; GA4 receives the event; the team receives an alert with the transcript. A spam wall and a dedupe store sit in front of everything. The pipeline has run live since spring 2026 and feeds the system of record the practice already used, with a person alerted in seconds and a review before any follow-up goes out. 12 operations, about 27 seconds per real call, end to end
Natural gas regulatory desk (our own build). The first workflow a roadmap produces has to pass the selection rule and stop for a person. Our regulatory watch is the pattern: a public source, a desk that read every notice by hand, and a natural check before anything moved. Built in one evening on Make.com with 10 modules and zero lines of code, with Claude Haiku 4.5 as the one thinking step. The agent watches the Federal Register API for FERC natural gas notices, skips combined-filings notices, dedupes against a data store, reads each notice, and ranks it HIGH or ROUTINE by pipeline footprint against the desk's own list. HIGH notices trigger a review email that ends with a request to reply RELEASE or HOLD; a second workflow polls the reply every 15 minutes, logs the decision, and hands off with a four-line checklist. It runs daily at 7 AM Eastern. On the record run it pulled 10 entries, skipped 4 combined notices, read 6, filed 5 ROUTINE, and held 1 for a person. 33 operations, 26 seconds, 1 of 6 notices held
Energy & Utilities
Manufacturing
Data Centers
Logistics
Readiness Assessment (Set in the scope)
Portfolio Ranking, Platform Sanction, and Policy (Set in the scope)
First Workflow Build and Handoff (Runs until the check passes)
Roadmap Review and Retainer (Begins at handoff; quarterly under the retainer)
Factors that shape outcomes: Whether a named owner exists on the desk before the assessment begins; How quickly IT and security can sanction a platform and open the enterprise account; Whether the sources are public and approved or require confidential data to stay inside the firewall; Leadership's willingness to pick three workflows and defer the impressive thirty.
AI strategy consulting is judged by what runs. Our AI services practice opens with an AI readiness assessment across data, systems, people, and policy, ranks the workflow portfolio by one rule, sanctions the platform with IT and security in the room, writes an AI policy a regulated business can enforce, and closes with the first workflow running inside your system of record, with a written check and a named owner. Every agent stops for a person.
Every workflow feeds your CRM, ETRM, scheduling, or portal system on the platforms you already run and sanction, and you own the result. The fractional chief AI officer retainer carries the plan forward after handoff with quarterly reviews, portfolio refreshes, governance updates, and build capacity. We run this stack on our own intake and regulatory monitoring, and the proof is available on request rather than on a slide.
If your team has a mandate, a desk spending its hours on monitoring and first drafts, and no sanctioned platform, the first workflow can be named in the scoping call. Request a scope.
An AI readiness assessment inspects four things: the data your workflows would read, the systems they would feed, the people who would own them, and the policy that governs them. Ours ends in a written report with a ranked list of candidate workflows and a plain statement of what has to change before the first one is built. It opens every AI strategy consulting engagement.
AI governance consulting produces a policy your security and compliance teams can enforce. Ours writes the three rules as policy (sanction the platform, keep confidential data inside the firewall, every agent stops for a person), defines approved and public sources, classifies data by workflow, assigns a written check and a named owner to each workflow, and specifies the audit log every workflow keeps.
A fractional chief AI officer is a senior AI leader on retainer rather than on payroll. In our practice the role runs the quarterly review, refreshes the workflow portfolio, updates governance as platforms and regulations move, and brings build capacity for the next workflows on the list. It suits organizations that need an AI strategy consultant reporting to leadership without a full-time hire.
One rule ranks the portfolio: the work has language or pattern shape, and it has a natural point where a human already checks it. Monitoring a docket, triaging an inbox, and drafting a first pass all qualify. We pick three, not thirty: a monitoring brief, a first-pass document workflow, and one repeatable operational task. If it needs a developer to start, it is the wrong first workflow.
We choose one or two platforms your IT and security teams will stand behind, starting with the platforms you already run, then open the enterprise account and grant permission to build. Microsoft Copilot Studio fits a Microsoft 365 tenant, Make.com and n8n fit workflows that cross many systems, and Zapier fits simple handoffs. Google Cloud Run enters only when a step outgrows the workflow platform, and you own the result.
An AI policy for a regulated company names the sanctioned platforms, states that confidential data stays inside the firewall, and requires that every agent stops for a person before an output reaches a regulator, customer, or counterparty. It lists approved and public sources, classifies data by workflow, assigns a written check and a named owner to each workflow, and requires an audit log a reviewer can read.
The scope sets the timing, and three things determine it: access to the sources and systems the desk uses, how quickly IT and security can sanction a platform and open the enterprise account, and whether an owner with release authority is named before the interviews begin. The scope states the sequence: assessment, ranking and sanction, policy, first build, handoff.
After the roadmap you own a running workflow, a signed policy, and a ranked list of what comes next. From there an engagement continues in one of two ways: an agent program that builds the remaining workflows on the list, or the fractional chief AI officer retainer with quarterly reviews, portfolio refreshes, governance updates, and build capacity. Either way, your team keeps ownership of every workflow.
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