Access to capable AI is becoming cheaper and more widely reported, but that does not mean every organization has the same tools, talent, data or results. The strategic advantage is shifting toward how well a business builds AI into repeatable work: with the right context, redesigned workflows, accountable decisions and measures that track real outcomes.
What “everyone owns the same intelligence” gets right—and what it overstates
Terence Kwok’s August 25, 2026 essay in The AI Journal makes a strategic argument: when competitors can access similar AI capabilities, advantage comes less from procuring a model and more from turning its capabilities into a reliable way of working. Kwok writes, “Converting raw capability into a repeatable operating habit is considerably harder than procuring it, and considerably harder for a rival to copy.” That is a thesis, not a universal law established by the available figures.
As an Amazon Associate I earn from qualifying purchases.
“Everyone owns the same intelligence” is rhetorical, not literal. Organizations vary in what models they can access, what they cost, how well they perform on a given task, and whether a company has the infrastructure, expertise and usable data to deploy them. The evidence points to convergence in some measures—not equal access or interchangeable results.
Why access to AI is becoming less of a differentiator
Some model costs have fallen sharply
Stanford HAI’s 2025 AI Index Report found that the inference cost for performance equivalent to GPT-3.5 fell more than 280-fold from November 2022 to October 2024. This is a comparison for a particular performance level over a specified period, not evidence that every AI service became cheaper by the same amount or that barriers to adoption disappeared.
#1 Best Overall
Benchmark gaps narrowed, but task fit still matters
In the same report, the score gap between the top- and tenth-ranked models narrowed from 11.9% to 5.4% in one year, while the top two models were separated by 0.7%. Those are benchmark comparisons in the report, not proof that models perform equally across business tasks. Rankings and test conditions change, and a small difference on one benchmark does not settle which model is more useful in a particular workflow.
The competitive frontier also continues to move. Stanford HAI’s 2026 AI Index Report says that, as of March 2026, Anthropic’s top model led the nearest Chinese model by 2.7%. That dated comparison is a reminder that convergence in some reported measures does not mean the frontier has stopped shifting.
Why widespread use has not meant widespread enterprise impact
McKinsey’s 2026 State of AI survey captures a gap between adoption and reported results. Among 1,719 respondents from 97 countries surveyed from May 4 to June 8, 2026, 88% said their organizations regularly used AI in at least one business function, while 44% reported scaling AI across the enterprise.
Free tools Windows power users keep installed
One-click scans. No signup required.
In the same survey, 37% said AI had contributed positively to organizational EBIT. Approximately 6% met McKinsey’s high-performer definition: respondents attributed at least 5% EBIT impact to AI and reported significant value. These are survey responses and McKinsey-defined categories, not independently verified rates for all companies. The results show why “we use AI” and “AI has materially changed our business” should not be treated as equivalent claims.
Individual productivity gains can be real without translating directly into enterprise value. To do that, organizations need to connect a task-level improvement to the rest of the process: who receives the output, what happens next, whether quality holds, and how the change affects cost, customer experience, revenue or risk.
Why workflow redesign is associated with stronger results
McKinsey reports that nearly three-quarters of high performers in its 2026 survey said they had fundamentally redesigned workflows, compared with one-quarter of other respondents. This is an association in survey responses, not proof that workflow redesign alone caused stronger financial results. It does, however, support a practical distinction: adding AI to an existing task is not the same as reconsidering how the work should flow from end to end.
A workflow approach starts with the outcome and the process, not the tool. For example, if a team wants faster customer support, automating the drafting of replies may help one step. Redesigning the workflow also asks how a request is classified, what account context the system can use, which cases need a human decision, how exceptions are routed, and how resolution quality is measured.
What to examine before choosing an AI workflow
There is no universally best workflow to automate. McKinsey’s Rewiring for AI: From ambition to advantage highlights capabilities including business priorities, end-to-end workflows, talent, operating model, technology and data, adoption, and scaling. Treat these as useful dimensions to assess, not a checklist proven to fit every organization.
- Business outcome: Define what should improve—such as quality, cycle time, customer impact, cost or innovation—and establish how you will measure it.
- Workflow scope: Decide whether AI is assisting a standalone task or changing an end-to-end process. Identify handoffs and exceptions that may determine whether a local time saving becomes a broader gain.
- Model and task fit: Compare capability, cost and suitability for the specific work. A benchmark lead does not automatically make a model the best choice for a particular task.
- Context and data: Check whether the system can access accurate, relevant business information and whether that information can be used appropriately.
- People and operating model: Identify who owns the process, who can change it, what skills are needed, and how the affected teams will adopt the new way of working.
- Risk and accountability: Decide which outputs need human review, who is responsible for actions taken, and how errors or unusual cases are handled.
- Scale: Establish whether the workflow can operate reliably beyond a small trial, including the data, technology and support needed to maintain it.
The evidence does not rank these factors universally. Their importance depends on the task, the organization and the consequences of an error.
How to tell whether AI is changing the business
Measure more than usage. A high number of users or generated outputs can show adoption, but not whether the process became better. Track the measures tied to the workflow’s intended outcome, such as accuracy, turnaround time, customer impact, cost or financial contribution. Pair those results with operational checks, including how often people correct outputs, how exceptions are handled and whether the new process is being used consistently.
Separate individual and organizational effects. If employees finish a task faster, examine whether the saved time improves service, reduces a bottleneck, supports more work or simply shifts effort elsewhere. A productivity gain matters, but its business value depends on what the organization does with it.
Finally, avoid treating a positive early result as proof of durable advantage. Models, prices and benchmark rankings change. A repeatable workflow can be more difficult to copy than a procurement decision, but only if the organization keeps its process, data, skills, controls and measures aligned as the technology changes.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
