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Becoming an AI company is not primarily a software-installation project. It is a leadership and operating-model change: the organization chooses a business outcome, removes unnecessary work, redesigns jobs around human and AI strengths, and measures whether the business can now perform better. Those are the four lessons outlined by Walid Abu-Hadba, CEO of Precisely, in a sponsored article published by The New Stack on October 15, 2026.
What an AI company actually is
An AI company uses artificial intelligence as part of how it executes strategy, not as a separate innovation program. The technology may generate code, summarize information, forecast demand or operate parts of a workflow, but those capabilities matter only when they improve a defined business result.
That distinction prevents a common failure mode: treating the number of deployed tools, employees using chatbots or software agents running as evidence of transformation. Abu-Hadba’s central argument is that “the hardest parts of becoming an AI company have relatively little to do with the technology.” The difficult decisions concern priorities, process design, accountability and the role of human judgment.
Lesson 1: Start with the business objective
Begin by naming the outcome the company needs. Possible objectives include faster growth, shorter development cycles, stronger customer relationships, better products or improved margins. The objective should determine where the organization invests and which processes it changes.
Turn strategy into an AI target
A useful objective is specific enough to guide choices. “Use more AI” cannot tell a team which workflow to change. “Reduce the time required to release a tested product feature” can. Leaders can then identify the work that constrains that result, decide where AI might help and assign an owner for the business outcome.
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AI is an operating model, not the strategy
AI changes how quickly and effectively a company pursues its strategy; it does not replace the strategy. A customer-service organization still has to decide which customers it serves and what experience it promises. AI can alter staffing, response workflows and knowledge access, but those choices should follow the customer and financial objectives.
Lesson 2: Simplify before accelerating
Before automating a workflow, examine whether each step creates value. Abu-Hadba’s sequence is: “Eliminate what doesn’t add value → Simplify what does → Then use AI to accelerate it.”
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Remove work that should disappear
List the approvals, duplicate data entry, handoffs and reports involved in the target process. Stop steps that exist only because of habit, outdated controls or unclear ownership. Removing a step is often safer and cheaper than automating it.
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Simplify the valuable work
For the steps that remain, standardize inputs, clarify decisions and reduce unnecessary variations. Simplification improves quality before any model is introduced and makes later monitoring easier.
Automate only after the redesign
AI can accelerate a sound process, but it can also scale a defective one. 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.” Teams should document the intended process, its controls and its success criteria before deployment.
Lesson 3: Redesign how people work
AI changes the baseline of what employees can accomplish. Giving people access to a model is not the same as redesigning work around it. Leaders must decide where AI belongs in a workflow, what it may take on independently and where human judgment remains essential.
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Map responsibilities between people and systems
For each task, specify whether AI drafts, recommends, executes or monitors. Define the information it may use, the actions requiring approval and the evidence a person must review. High-consequence decisions, ambiguous exceptions and relationship-sensitive work generally need explicit human ownership.
Redefine roles and skills
As routine production moves to AI, employees may spend more time on problem framing, validation, exception handling, domain expertise and customer communication. Job descriptions, training and career development should reflect those responsibilities rather than simply adding a new tool to an old role.
Design for trust and accountability
Employees need clear escalation paths when an output is wrong, incomplete or unsafe. Managers should make it easy to report failures and improve the workflow instead of rewarding silent workarounds. “Giving employees access to AI and measuring usage will only take an organization so far,” Abu-Hadba writes; leaders must examine how the work itself should change.
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Lesson 4: Measure the results that matter
Adoption counts, agent counts 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.
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Measure capacity and speed
Track capacity created for expert work, cycle time, time to market and the time employees spend on high-value judgment. An illustrative example in the source says an engineer might generate code four times faster, but it is not presented as an independently measured statistic and should not be treated as a general productivity claim.
Measure customers and the business
Depending on the objective, meaningful measures may include customer experience, retention, revenue growth, margin, defect rates or the time required to resolve an issue. Establish a baseline before changing the workflow, then compare results against that baseline and review unintended effects such as rework or increased risk.
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Use adoption as a diagnostic
Low usage may indicate poor training, a badly designed workflow or a tool that does not solve the intended problem. High usage with no business improvement is a signal to revisit the process and objective. “Adoption is an input, not an outcome,” Abu-Hadba says. The goal is expanding what the organization can accomplish with the people and expertise it already has.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical four-step operating loop
- Choose one business outcome. Name the result, owner, baseline and time horizon.
- Redesign the process. Eliminate non-value work, simplify the remaining steps and document controls.
- Assign human and AI responsibilities. Define permissions, review points, escalation and the skills employees need.
- Evaluate business performance. Compare capacity, speed, customer and financial measures with the baseline, then adjust the workflow.
This loop can be repeated across functions. It also provides a decision framework for comparing transformation proposals: prefer the proposal tied to a defined outcome, with substantial simplification before automation, explicit role redesign, preserved human judgment and business-result measurement.
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The chapter “Becoming an AI Company” in Competing in the Age of AI describes a data-centric operating architecture and an agile organization. Its principles draw on Microsoft and research involving hundreds of companies. Check the current edition and availability before purchasing.
Frequently Asked Questions
Is buying AI software enough to become an AI company?
No. The transformation requires a business objective, process simplification, redesigned roles and measures tied to business performance. Software is an enabler within that operating model.
What should leaders measure first?
Start with a baseline tied to the chosen objective, such as cycle time, customer experience, capacity for expert work, growth, margin or quality. Usage metrics can be monitored as diagnostics but are not the outcome.
Where should human judgment remain?
Keep explicit human ownership for ambiguous exceptions, high-consequence decisions, relationship-sensitive work and any action whose evidence or context the AI cannot reliably evaluate.
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