AI consulting is becoming a practical escape route for experienced white-collar workers, but the opportunity comes with a quality problem. Domain expertise can open the door, technical discipline still decides whether clients get value.
Kristina Martinelli did not leave corporate America with the usual founder myth. There was no dorm room, no venture deck, no long runway. She was laid off from a banking job at 55 and, within a day, started building coaigence, an AI consultancy aimed at helping professionals, companies and communities use artificial intelligence without losing sight of the human work around it.
That is what makes the story worth watching. The most interesting AI businesses right now are not all being built by twenty-something engineers in San Francisco. Some are being built by people who spent decades inside banks, insurers, defense contractors and corporate technology teams, then realized that AI had created a new market for translating institutional experience into practical services.
According to Business Insider's profile, Martinelli is now 56 and describes the layoff as the trigger that pushed her into entrepreneurship. Her background was not casual exposure to technology. Her own company materials describe more than 20 years in software development life cycle work, mergers and acquisitions, digital transformation and large-scale corporate change across insurance, banking and defense. That matters because AI consulting is not only about knowing which chatbot to open. It is about understanding how organizations actually make decisions, where workflows break down and why employees resist tools that executives want them to adopt quickly.
Martinelli's model is modest in the way many service businesses are modest at the start, but it is also revealing. coaigence sells guidance for careers, corporate adoption and community education. Its website points to self-paced modules, live workshops and one-on-one coaching. A local Brookfield, Connecticut recreation listing shows Martinelli teaching an eight-week beginner-friendly AI course covering tools such as ChatGPT and prompt engineering for a $140 fee. That is not a billion-dollar software platform. It is something more immediate: a domain expert packaging knowledge into paid, repeatable services.
For displaced white-collar workers, that route is becoming more plausible. A former banking technology executive can turn risk reviews, portfolio management, business-case discipline and change management into AI strategy sessions for small businesses that do not have internal AI teams. A laid-off marketing director can build workflow audits around content operations. A former HR leader can advise on AI literacy and hiring processes. The common thread is not pure technical invention. It is translating AI into the language of a buyer who has a budget, a problem and limited patience for hype.
Martinelli's workflow also shows how low the operating barrier has fallen. She uses mainstream tools including ChatGPT, Claude, Copilot, Gemini, Grok and Perplexity, and has built custom GPTs, including an AI assistant called Raivyn. She has described an 80/20 approach, with most of the thinking still coming from the human and AI filling in as support. That framing is important because it turns the consultant into a systems designer for her own work. The service is not just advice about AI. It is proof that one person can use AI to research, draft, organize, teach and sell with far less infrastructure than a traditional consulting firm once needed.
There is a strong small-business signal here. Many owners do not need a machine-learning team. They need someone to help choose tools, write better prompts, clean up repetitive administrative work, understand where automation is safe and decide what should remain human. They also need reassurance from someone who has sat inside complex organizations and knows that technology adoption is rarely a straight line.
The buyer risk
The same forces that make this market attractive also make it risky. When AI tools are easy to access, the line between useful advisor and newly minted expert gets blurry. A consultant can build polished demos with off-the-shelf models, but that does not mean they understand data governance, security, model limitations, procurement constraints or the operational cost of maintaining an AI workflow after the first workshop ends.
That risk is especially sharp for small companies. A bakery, local clinic, contractor or professional services firm may not have a chief technology officer to evaluate advice. If a consultant recommends feeding sensitive customer data into the wrong tool, the buyer may not discover the problem until later. If an automation quietly produces inaccurate output, employees may spend more time fixing it than they saved. If a company buys a long subscription stack too early, the monthly cost can creep up before the use case is proven.
Martinelli herself appears aware of that concern. The Business Insider profile notes her warning against locking into long-term AI subscriptions because the technology is changing quickly and hidden costs can appear, including token limits. That is the right instinct. In this market, restraint is a competitive advantage. A good AI consultant should sometimes tell a client to buy less, test more and keep humans in the approval loop until the workflow has earned trust.
The stronger consultancies will be the ones that combine enthusiasm with operational discipline. They will document assumptions, protect client data, measure before-and-after performance and admit when a problem requires deeper technical help. They will also avoid selling AI transformation as a personality product. The buyer is not paying for the consultant to seem futuristic. The buyer is paying for fewer manual steps, better decisions, faster service or clearer career positioning.
Martinelli's story is not just a reinvention narrative. It is a preview of a labor market where experienced professionals use AI to turn knowledge into smaller, more flexible businesses after corporate careers become less predictable. Some will build durable practices. Some will chase a trend and disappear. The difference will come down to whether they can move beyond tool fluency and prove that their judgment makes the technology safer, clearer and more useful for the people who actually have to live with it.
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