For most of my career, the fundamental value proposition of consulting has been relatively straightforward. A client encounters a problem that management either cannot solve internally or believes would benefit from outside expertise. A consultant is brought in to understand the situation, diagnose the underlying issues, recommend a course of action, and help the client achieve a better result. While methodologies and specialties vary considerably, that basic model has supported the consulting profession for decades.
Artificial intelligence is beginning to challenge some of the assumptions underlying that model. Clients now have direct access to technology that can research an issue, analyze information, identify alternatives, apply established business frameworks, and develop reasonably sophisticated recommendations in a matter of minutes. The quality varies, and experienced consultants can readily identify the limitations, but that may miss the more important point. Clients don't need AI to completely replace a consultant for it to change how they perceive the value of consulting.
If a business owner can ask an LLM how to improve profitability, build a strategic plan, redesign a sales process, reduce expenses, develop KPIs, or increase the value of a business, some of the knowledge that consultants historically monetized becomes much easier to obtain. The client may still need help, but the question inevitably becomes different: What exactly am I paying the consultant for?
I believe every consultant should take that question seriously.
The answer cannot simply be that we have more information than the client. AI is making information abundant. Nor can our defense be that we have access to better AI tools. Our clients increasingly have access to many of the same tools. The more durable source of value is our ability to understand what is actually happening inside a business, determine which problems matter, recognize relationships that aren't immediately apparent, exercise judgment when there isn't a black-and-white answer, and help management turn an answer into a measurable result.
That distinction has been occupying a great deal of my thinking, and it is one of the reasons I have been developing STAR.
STAR began with a problem I have encountered repeatedly throughout my career. Businesses rarely have isolated problems, even though that is often how consulting engagements are defined. A client believes it has a sales problem, an expense problem, a people problem, a strategy problem, or an owner-dependence problem, and the natural response is to focus the engagement on solving that particular issue.
Experience teaches us that the stated problem is frequently only part of the story. A company may indeed need more sales, but increasing revenue could make matters worse if the organization lacks the capacity, processes, people, or working capital to support the growth. What appears to be a people problem may actually be the result of poorly designed processes or an operating model that makes it difficult for good people to succeed. Weak profitability could be attacked through expense reduction when the much larger opportunities reside in pricing, customer mix, recurring revenue, utilization, management capacity, or the underlying business model.
The more complicated the business becomes, the more important these relationships become. Strategy influences the capabilities a company needs. Existing capabilities determine which strategies can actually be executed. Constraints prevent otherwise attractive opportunities from producing results. Organizational change ultimately depends upon individuals changing their behavior. Those changes affect the company's operating and strategic assets, which in turn influence financial performance, risk, competitive advantage, cash flow, and enterprise value.
We understand many of these relationships individually. The difficulty has always been evaluating them together.
That is the problem STAR is being designed to address.
STAR is an AI-enabled advisory system that connects strategy, capabilities, constraints, individual change, organizational change, strategic and operating assets, value drivers, and enterprise value within a common framework. Instead of approaching each of these areas as a separate consulting discipline, the objective is to understand how a decision or problem in one part of the business affects the others.
This is an important point because STAR is not intended to be an AI consultant. I have very little interest in creating another application where a business owner answers a handful of questions and receives a professionally formatted report telling them what to do. AI can already generate impressive-looking reports. Generating more recommendations is not the problem I am trying to solve.
STAR is being built around the experienced consultant.
A good consultant brings context and judgment that are difficult to capture in a prompt. We understand the personalities involved, the history of the business, the client's tolerance for risk, the organization's ability to change, and the difference between something that works conceptually and something that will actually work for this particular client. We also recognize something that becomes increasingly important as AI makes answers easier to obtain: the question the client asks is not necessarily the question that needs to be answered.
AI brings a different set of capabilities. It can process large amounts of information, consistently apply structured methodologies, examine relationships across disciplines, challenge assumptions, maintain institutional knowledge, and evaluate possibilities at a scale that would be extremely difficult for an individual consultant to replicate manually. STAR is intended to bring those capabilities together with the judgment of the consultant rather than attempting to substitute one for the other.
After working on the individual components for some time, STAR has now reached an important point in its development. It is real. The frameworks operate together as a system rather than as a collection of separate tools and methodologies.
That development has also caused me to reconsider the economics of the traditional consulting model. Many independent consultants still operate largely on a project basis. A client has a problem, the consultant gets the engagement, completes the work, sends the invoice, and eventually begins looking for the next project. The consultant may have accumulated decades of valuable experience, but much of that knowledge remains in the consultant's head and has to be reapplied manually to every new situation.
This model creates another problem that almost every experienced independent consultant understands. A surprising amount of the day can disappear into email, client questions, unexpected fire drills, and small pieces of work that are valuable to the client but difficult to bill. By late afternoon, the consultant has been working continuously but may feel as though very little has actually been accomplished. The business development that should have happened gets pushed aside, while the question of where the next project will come from never entirely disappears.
AI doesn't automatically solve that problem. A structured advisory system potentially can.
If we can capture more of our methodology, apply it consistently, connect information that previously existed in separate silos, identify the issues creating the greatest economic impact, and maintain continuity between diagnosis and implementation, we begin to change the nature of the consulting relationship. Instead of waiting for the client to identify a problem and call us, we can help the client continuously determine which problems and opportunities deserve attention.
That is a fundamentally different value proposition from selling another project.
It also points to why I am increasingly optimistic about the future of consulting despite the disruption AI will undoubtedly create. AI will reduce the value of some work consultants have traditionally performed. Research, summarization, routine analysis, framework development, report preparation, and generic recommendations will become faster and less expensive. Pretending otherwise won't protect us.
However, reducing the value of certain consulting activities is not the same as eliminating the value of the consultant. As information becomes abundant, judgment becomes more important. As analysis becomes inexpensive, knowing what deserves analysis becomes more important. As recommendations become easier to generate, understanding which recommendation fits the client's circumstances becomes more important. As technology allows us to examine more variables simultaneously, the ability to recognize connections between those variables becomes more valuable.
There is also an uncomfortable implication for those of us who advise other businesses. We cannot spend the next several years telling clients that AI requires them to change while assuming that our own consulting practices can remain largely unchanged. We cannot tell clients to challenge established business models while protecting ours. We cannot encourage them to develop new capabilities while limiting our own AI education to occasional conversations with ChatGPT or Copilot.
There is a natural temptation for consultants to stay at the level of general discussion. It is relatively easy to talk intelligently about how AI will transform an industry. It is considerably harder to determine precisely which processes should change, which capabilities need to be developed, how the economics will improve, what risks must be controlled, how people will need to work differently, and how the resulting improvements will translate into business value.
That is where I believe the opportunity lies.
STAR represents my attempt to build for that future rather than simply talk about it. There is still much to develop, test, challenge, and refine, but it has moved beyond the conceptual stage. The components are together, the relationships between them are clearer, and the system connects disciplines that have traditionally been treated independently.
I don't believe the consultant of the future wins by trying to compete with AI on access to information. That is a contest we are unlikely to win, and it isn't where our greatest value resides anyway. The opportunity is to use AI to expand what an experienced consultant can see, connect, evaluate, and ultimately help a client accomplish.
For decades, clients have paid consultants because we had answers they didn't have. In the years ahead, answers will be everywhere. Our value will increasingly come from knowing which answers matter, how they connect, and what the client should actually do next.
That is the future of consulting I am building STAR for. DM me to schedule a 30-minute demo.