For decades, businesses have relied on agencies and professional service firms to handle work they could not efficiently manage internally. Companies hired marketing agencies to create campaigns, advertising firms to generate leads, accounting firms to manage books and reports, and lawyers to review contracts, conduct research and prepare documents. The traditional model was straightforward: hire people with specialized expertise, pay them for their time and receive a service.
Artificial intelligence is beginning to challenge that model.
The next major phase of AI may not simply be about giving employees better tools. It is about building AI systems that can take responsibility for entire workflows and deliver a measurable business result. Instead of paying an agency to produce 20 social media posts, a business could increasingly pay an AI-powered system to generate qualified leads. Instead of paying an accountant simply to enter transactions, a company could use AI to maintain its books, identify irregularities, prepare reports and flag important financial issues. Instead of paying lawyers for hours of research, businesses could increasingly use specialized legal AI to review contracts, identify risks and prepare work for human approval.
This is the rise of outcome-based AI: technology that is increasingly judged not by how much work it performs, but by what result it produces.
Recent developments suggest the shift is becoming more serious. Thomson Reuters’ 2026 research into professional services found that generative AI adoption among surveyed professionals had risen substantially, while 53% said their organizations were planning or considering agentic AI. The report also found that 77% expect agentic AI to become central to their workflows by 2030. (Thomson Reuters)
That distinction matters because traditional software generally waits for instructions. An employee opens a program, enters information and completes a task. An AI agent, by contrast, can increasingly be designed to understand an objective, determine the steps required, use multiple tools and systems, and continue working until the assigned task reaches a defined stage.
In other words, the software begins to look less like a tool and more like a digital worker.
From Selling Hours to Selling Results
The agency industry has historically operated around human labour. A marketing agency might have strategists, copywriters, designers, media buyers, account managers and analysts. The client pays the agency because coordinating all these specialists internally would be expensive and complicated.
But AI can combine many of these functions into interconnected workflows.
A business owner could provide an objective such as: “Generate 100 qualified leads for this product this month.”
An AI system could potentially research the target audience, analyse competitors, develop campaign concepts, create advertisements, prepare landing pages, launch campaigns, monitor performance, adjust targeting and produce reports. Human professionals may still supervise the system, but the amount of manual coordination required could fall dramatically.
This is already beginning to reshape marketing. Forrester reported in June 2026 that nine out of ten US marketing agencies were using generative AI and half were using agentic AI for marketing execution. The research also warned that agencies must avoid allowing efficiency gains to come at the expense of creativity and differentiation. (Forrester)
The bigger question, therefore, is not whether agencies will use AI. It is whether some agencies will eventually become something very different because of it.
McKinsey has also highlighted how AI could encourage advertisers to bypass traditional intermediaries as discovery, buying and measurement become increasingly integrated. Its 2026 research found that three-quarters of surveyed advertisers expected AI to increase total media spending, while some spending was expected to move away from traditional search and open-web advertising toward platforms with stronger data, measurement and transaction capabilities. (McKinsey & Company)
The agency may therefore evolve from being the workforce that performs every task into the organisation that designs, supervises and guarantees an AI-powered system.
Legal Services Are Entering the Same Transition
The legal industry provides an even clearer example of where outcome-based AI could go.
Legal work contains numerous repetitive and information-heavy processes: reviewing contracts, researching precedents, comparing documents, summarising cases, conducting due diligence and identifying potential risks.
Specialized AI platforms are already moving beyond simple question-and-answer systems.
Harvey, one of the most prominent legal AI companies, recently introduced new capabilities designed around persistent memory and legal workflows. Its platform is increasingly intended to understand how individual lawyers work and retain context instead of treating every request as an isolated prompt. (The Wall Street Journal)
That development illustrates the difference between a chatbot and an AI agent.
A chatbot answers a question.
An agent can potentially take an objective and work through the process required to achieve it.
For a company, that could mean asking an AI system to review hundreds of supplier contracts, identify clauses that create unusual risks, classify the agreements and prepare a report for a lawyer. The human expert remains important, particularly where legal judgment and accountability are required, but the amount of routine work surrounding the decision can be substantially reduced.
This does not mean lawyers disappear. Instead, the economics of legal work could change. Clients may become less interested in paying for hundreds of hours of routine research and more interested in paying for judgment, strategy, accountability and successful outcomes.
Accounting Could Become an Automated Operating System
Accounting is another sector where the agency model is vulnerable to automation because so much of the work follows structured processes.
Bookkeeping, transaction categorisation, reconciliation, financial reporting, expense analysis and compliance preparation all involve large volumes of information moving through predictable workflows.
Wolters Kluwer argues that agentic AI could allow accounting systems to plan and execute multi-step workflows rather than simply respond to individual prompts. It also notes that firms will need to redesign workflows around AI and human expertise rather than simply adding AI to existing processes. (Wolters Kluwer)
Thomson Reuters similarly reported in August 2026 that accounting firms are moving toward AI systems capable of handling more complex, multi-step work, although strong review processes and trusted data remain essential. (Thomson Reuters Tax)
This could eventually create a new type of accounting business.
Instead of advertising “professional bookkeeping services,” a company could sell “real-time financial control for small businesses.” The technology could monitor transactions, reconcile accounts, prepare management reports, detect anomalies and alert business owners when something requires attention.
The customer would not necessarily care which AI model performed each step.
They would care whether the books are accurate, reports arrive on time and financial problems are detected early.
That is the fundamental change.
The Agency of the Future May Be Much Smaller
If AI can perform large portions of agency workflows, businesses may no longer need huge teams to deliver certain services.
A small company could potentially operate with a handful of specialists overseeing dozens of AI agents. One person could supervise content production, another could monitor campaigns, another could handle client relationships, while AI systems execute much of the underlying operational work.
PwC has described an “agentic-first” approach to global business services in which AI agents work alongside human oversight and focus on business outcomes rather than simply reducing labour costs. The firm estimates that AI agents can already automate 25% to 40% of typical global business service tasks in some contexts. (PwC)
This has enormous implications for entrepreneurs.
The traditional barrier to starting a service business has often been the need to hire people before revenue becomes predictable. AI can reduce some of that barrier by allowing a small founder-led company to coordinate a much larger volume of work.
A Nigerian entrepreneur, for example, could build a lean digital marketing company serving clients in Nigeria, the United Kingdom or the United States while relying heavily on AI for research, content production, reporting, customer communication and campaign analysis.
The competitive advantage would no longer simply be having more employees.
It would be having better systems.
But AI Will Not Automatically Replace Agencies
There is an important limitation to this trend.
AI can automate workflows, but automation does not automatically create business value.
A badly designed AI system can produce large amounts of poor content, make incorrect assumptions, misinterpret financial information or create legal risks. Professional services also involve trust, accountability, confidentiality and judgment—areas where human oversight remains critical.
KPMG has argued that enterprises need to move from “agent sprawl” toward measurable outcomes, with governed AI systems that can coordinate work across multiple systems while maintaining visibility and accountability. (KPMG)
This means the future is unlikely to be simply “AI versus humans.”
It is more likely to be AI-managed workflows plus human accountability.
The professionals who understand how to design, supervise and improve those workflows may become more valuable, while professionals whose primary value comes from repetitive execution could face increasing pressure.
What This Means for Entrepreneurs
For business owners, the lesson is straightforward: do not ask only what AI can do. Ask what result you can sell with AI.
There is a major difference between selling “AI content creation” and selling “a complete customer acquisition system.”
There is a difference between selling “AI bookkeeping” and selling “automated financial reporting and cash-flow monitoring.”
There is a difference between selling “AI legal research” and selling “contract risk analysis under professional supervision.”
The second versions are more powerful because customers ultimately buy outcomes, not technology.
This is why the next generation of AI businesses may look less like software companies and more like automated service companies.
DDM News believes this could become one of the most important shifts in the service economy over the coming years. The winners may not necessarily be the businesses with the most sophisticated AI models, but those capable of connecting AI to a clear commercial outcome.
The traditional agency sold labour.
The modern agency increasingly sells expertise.
The emerging AI-native agency may sell results.
That does not mean every marketing agency, law firm or accounting practice will disappear. Instead, the definition of an agency is likely to change. Teams may become smaller, workflows more automated and pricing increasingly tied to measurable value rather than hours worked.
For entrepreneurs, this creates both a threat and an opportunity. Businesses that continue selling tasks while competitors sell outcomes may struggle to justify their prices. But businesses that learn to combine AI with industry knowledge, human judgment, customer relationships and accountability could build highly scalable service models.
The real AI revolution, therefore, may not be about replacing one employee with one machine.
It may be about replacing an entire chain of fragmented tasks with one intelligent system designed around a specific result.
And as AI becomes better at planning, executing, monitoring and improving those workflows, the most valuable question for any service business may become remarkably simple: What outcome can you guarantee, and how much of the work required to deliver it can AI now handle?
That is where the agency model of the future is likely to be decided.
DDM News will continue to track how AI is changing the way businesses operate, compete and create new sources of income as the global economy moves from software that assists workers to intelligent systems capable of executing entire business processes.
