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Becoming an AI company is not primarily a matter of installing a model, buying more software or counting employee prompts. It is an operating-model transformation: leaders connect AI to a business objective, simplify work before automating it, redesign workflows around human and machine strengths, and measure the business capability created.
That is the central argument in a sponsored October 15, 2026 article for The New Stack by Walid Abu-Hadba, chief executive officer of Precisely. The practical test is not how many AI tools an organization deploys, but what it can now accomplish faster, better or at greater scale than before.
1. Start with the business objective
AI should follow strategy, not substitute for it. Before selecting a model or launching an internal assistant, leadership needs to define the outcome the investment is meant to produce. Possible objectives include profitable growth, faster innovation, stronger customer relationships, better products or improved margins.
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Abu-Hadba describes AI as an operating model that changes how quickly and effectively a business pursues its strategy, rather than as the strategy itself. That distinction prevents a common failure mode: deploying a general-purpose tool without changing a priority workflow, then declaring success because employees used it.
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Turn the objective into a change agenda
- State the business outcome in plain language.
- Identify the workflows that constrain that outcome.
- Assign an accountable business leader, not only a technology owner.
- Define the baseline and the result that would justify expansion.
2. Simplify before accelerating
Automation magnifies the process it touches. If the process is wasteful, AI can make waste move faster. The sequence recommended in the article is: eliminate work that adds no value, simplify work that does, and only then use AI to accelerate it.
This is a process-design exercise before it is an AI exercise. Teams should map the actual path a request, decision or product takes; remove duplicate approvals, unnecessary handoffs and reports that nobody uses; and standardize the valuable steps that remain. Only after that should they decide where an AI system can draft, classify, retrieve, route or execute.
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As Abu-Hadba puts it, “Apply AI to a process that no longer adds value, and you’ve simply made a bad process run faster.” The warning applies equally to internal operations and customer-facing automation. A chatbot placed on top of unclear policies may increase contacts, rework and escalation rather than reduce them.
A practical sequence
- Eliminate: Remove tasks, checks and outputs that do not contribute to the desired result.
- Simplify: Reduce variation and make the valuable path easier to understand and govern.
- Accelerate: Apply AI where speed, scale or consistency will improve without weakening the outcome.
3. Redesign how people work
Giving employees access to an AI tool does not, by itself, create an AI-enabled organization. Leaders must decide where AI belongs in each workflow, what it can safely take on, and where human judgment remains essential.
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That redesign changes the baseline of what employees can accomplish. A professional may spend less time producing a first draft and more time checking evidence, making exceptions, explaining trade-offs or working directly with customers. The value is not simply faster completion of the old job description; it is a different division of labor.
Role definitions, skills and development programs therefore have to evolve with the workflow. Employees need clarity about when an AI system may act autonomously, when a person must approve an output, what information may be used, and how errors are reported. Managers need new ways to review quality when part of the work is generated or triaged by a system.
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“Giving employees access to AI and measuring usage will only take an organization so far,” Abu-Hadba writes. “Leaders need to examine how the work itself should change.” That means involving the people who perform the work: they know where exceptions occur, which controls are essential and which steps exist only because an older process was never reconsidered.
Preserve judgment deliberately
- Keep human approval for decisions involving material risk, ambiguity or accountability.
- Define escalation paths for uncertain, low-confidence or out-of-policy outputs.
- Train employees to verify results rather than accept fluent answers automatically.
- Update job expectations and learning plans as repetitive tasks are reassigned.
4. Measure the results that matter
Adoption counts, the number of agents deployed and token volume are activity measures. They can show that a system is being used, but they do not prove that the company is creating value. “Adoption is an input, not an outcome,” Abu-Hadba says.
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Measurement should connect the AI-enabled workflow to the business objective established at the start. Depending on the use case, useful indicators may include capacity created, speed to market, time employees spend on expert work, customer experience, growth and broader business performance.
Capacity is especially important. If an AI system handles routine preparation, the relevant question is what people do with the time released: serve more customers, investigate harder problems, improve a product or reduce delays. A usage dashboard cannot answer that question; operational and financial measures can.
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| Measurement area | What to examine |
|---|---|
| Capacity | Work completed with the existing team and the expert time released from routine tasks. |
| Speed | Time from idea to delivery, decision or customer response. |
| Quality and experience | Accuracy, rework, escalations and customer outcomes, not just response volume. |
| Business performance | Growth, margin, retention or other measures tied to the stated objective. |
| Usage activity | Adoption, agent counts and token volume, treated as inputs rather than proof of value. |
The operating-model shift behind the four lessons
Taken together, the lessons describe a loop rather than a one-time deployment. A business sets an outcome, removes unnecessary complexity, redesigns work around human and AI strengths, measures the resulting capability, and uses what it learns to choose the next workflow. The loop also makes governance practical: controls are attached to real decisions and risks instead of being written as a separate technology policy.
This view is consistent with the chapter “Becoming an AI Company” in Competing in the Age of AI, which presents a data-centric operating architecture and an agile organization as foundations for becoming a different kind of company. The chapter says its principles draw on work involving Microsoft and research on hundreds of companies; readers should verify the edition and availability before purchasing.
What leaders should ask before calling the transformation successful
- Which specific business objective does this AI work advance?
- What did the team eliminate or simplify before automating?
- How did responsibilities change between people and AI systems?
- Where is human judgment mandatory, and how is escalation handled?
- What can the organization do now that it could not do before?
- Which business result improved, and over what comparison period?
The hardest part is organizational. As Abu-Hadba states, “The hardest parts of becoming an AI company have relatively little to do with the technology.” Tools matter, but leadership choices about priorities, processes, roles and evidence determine whether those tools change performance.
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