Commercial AI for energy and industrial companies
Michael S.U. Hudson designs and builds commercial AI systems: marketing and sales frameworks, content engines, and analysis and optimisation engines that run autonomously or with a human in the loop. He leads this work as Chief AI Officer of Project 54, which serves energy and industrial companies, and as Head AI Content & Analytics Engine Architect at Beeyawn, which builds content, ads, analysis and optimisation engines.
01
Marketing and sales frameworks
A marketing and sales framework sets the targets, audiences, channels and rhythm before anything is produced. Michael writes these frameworks for energy companies with sales cycles of one to two years: costed strategy options, forecast scenarios, a lead qualification model and a 90 day launch plan, each tied to commercial priorities.
02
Content engines
Content and ads engines that run fully autonomously or with a human in the loop. Each engine researches, drafts and publishes on a fixed rhythm, and every claim is traced to a named source. For one industrial client, an engine produced 129 posts, 12 long form articles and 60 infographic briefs between April and August 2026.
03
Analysis and optimisation engines
An analysis engine measures what the content and sales systems produce and feeds the result back into them. It covers analytics event design, campaign tracking, lead qualification and a monthly test of visibility in AI search answers. For one client, it exposed 948 false analytics leads in February 2026.
What does a Chief AI Officer do in an energy marketing firm?
A Chief AI Officer in an energy marketing firm sets the AI strategy and designs the systems the client work runs on. At Project 54 that means content engines, sales and pipeline automation, and the operating stack behind them. Michael chooses the models and the architecture, writes the operating method the delivery team works to and directs that team. He owns delivery quality and client reporting.
The role is governance as much as technology. AI never handles passwords, keys or secrets. Automations that read client mailboxes are read only. Every AI drafted claim is checked against a named source before a client approves it, and an evidence label shows how well each claim is supported.
Energy raises the stakes: technical buyers, sales cycles of one to two years and little tolerance for error. The job is to make AI output accurate enough for that audience and steady enough to publish every week.
When should a content engine run without a human?
A content engine can run without a human when every rule it must follow can be checked by a machine and a mistake is cheap to reverse. Publishing approved evergreen posts on a schedule meets that test. Deciding what a brand claims about its own products does not.
The line is drawn per client and per channel. Google treats pages produced at scale to manipulate rankings as spam. Gmail requires bulk senders to keep their spam rate below 0.3 per cent. LinkedIn bans automated likes, comments and connection requests. An engine may publish on its own inside those limits; it may not engage on its own.
For Geepas, people approved every start frame and every final edit, because the product had to be exact in every shot. For an industrial client, a written rule lets evergreen content publish when approval is delayed, while every new claim still waits for the chief executive.
Engagements
Market Entry Readiness Audit
Problem
A company entering a new energy market lacks a verified view of the regulatory path, the decision makers and the live tenders.
Delivered
Regulatory entry path, stakeholder map, tender landscape and an entry plan.
Duration
Two weeks, fixed scope.
Sponsor
Chief executive, managing director or head of business development.
Intelligence Brief
Problem
Leadership teams need current, verified market intelligence without building a research function.
Delivered
A monthly brief on the target market: tenders, stakeholders, regulation and competitor moves.
Duration
Monthly subscription.
Sponsor
Head of business development or strategy.
Growth Systems
Problem
An energy company has strong technical credibility but weak international visibility and pipeline, and no capacity to build either in house.
Delivered
An AI run marketing and sales system installed and operated for the client: content engine, distribution, outbound pipeline, CRM and reporting. Three scope levels.
Duration
Six months minimum.
Sponsor
Chief executive or commercial director, with the marketing director as day to day owner.
Selected case notes
Contracting
Engagements are contracted by Project 54. Fees on request.