Becoming an AI company is a company-wide redesign, not a feature launch. AI must become central to what the software sells, how customers achieve results, and how the company builds, operates, prices, sells and governs its products. A few generative-AI features added to an otherwise unchanged application do not establish that transformation.
The practical test is whether AI changes the firm’s value proposition and operating model at the same time. That means redesigning workflows around agents or intelligent automation, funding the infrastructure and controls they require, changing roles and processes, and measuring business outcomes rather than demo quality or pilot productivity alone.
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What an AI-company transformation actually changes
McKinsey’s October 2025 analysis describes an AI-centric software company across seven connected areas: product, business model, go-to-market, development, internal functions, infrastructure and organization. These areas reinforce one another. An agent that performs useful work changes the product experience; that can alter pricing, support requirements, sales conversations, data architecture, staffing and risk controls.
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This is why “AI company” is not a formal legal category or a single maturity level. It is a strategic description of where intelligence sits in the business. In a transformed firm, AI is part of the core customer promise and the machinery used to deliver that promise.
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- Customer value: the product helps users complete outcomes, not merely navigate screens or store records.
- Operating model: discovery, engineering, sales, support and back-office work are redesigned around people and software agents working together.
- Economics: pricing and margins account for variable inference and implementation costs.
- Control: data access, security, evaluation, explainability, intellectual property and human accountability are designed into the system.
More than 60 percent of top software-industry executives identified McKinsey’s seven shifts as a top strategic priority. That figure indicates the breadth of the agenda, not proof that every company has completed it.
Three product paths for becoming AI-centric
A software vendor generally chooses among three architectural directions. They can coexist, but each has different development effort, integration demands and sources of differentiation.
| Path | How it works | Main advantage | Key trade-off |
|---|---|---|---|
| Agent added to an existing application | An assistant or task agent works inside the current product and uses its existing data and APIs. | Usually the quickest route for an incumbent with an installed customer base. | Capabilities are constrained by the host system’s APIs, data model and product roadmap. |
| Agent-centric orchestration | The agent becomes the primary interface, coordinating work across several tools or systems. | Can deliver an end-to-end workflow rather than a single feature. | Requires more development, monitoring, permissions and ongoing maintenance. |
| Specialist domain product | A focused agent performs a high-value workflow for a particular industry or function. | Deep domain context can produce differentiated outcomes. | Customer-specific integration and implementation can be substantial. |
McKinsey argues that defensibility may move beyond visible features toward privileged access to high-quality domain data, workflow context and the ability to produce useful outcomes. Data ownership alone is not a guaranteed moat: its value depends on accuracy, permission to use it, freshness, integration and the company’s ability to turn it into reliable actions.
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Traditional software asks a person to locate a screen, enter fields and move work through a sequence. AI-centric software can let a user state an objective, supply context and review a proposed plan or result. The product still needs conventional controls for inspection, correction and auditability, especially when an agent can change records, send communications or trigger transactions.
The most valuable redesigns remove coordination work between systems. An agent might gather information, draft an action, request approval, execute the approved step and record what happened. That is materially different from placing a chatbot beside an unchanged workflow.
How AI changes the business model and unit economics
AI introduces costs that conventional seat-based software often hides: model inference, retrieval, tool calls, storage, monitoring and customer-specific implementation. If an agent performs work that formerly required several human seats, a per-seat metric can become a weaker proxy for both customer value and vendor cost. That does not make seat pricing obsolete; it means vendors should test whether the unit they charge tracks the value delivered and the resources consumed.
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Possible pricing units include:
- Usage: requests, tokens, minutes or compute consumed.
- Actions: completed transactions or workflow steps.
- Outputs: documents, analyses, cases or other deliverables produced.
- Outcomes: a business result such as a resolved case or approved claim, where it can be measured and contracted fairly.
- Hybrid plans: a platform fee combined with usage or outcome allowances.
McKinsey reports that 63 percent of surveyed software leaders expected AI to fundamentally change their business model within three to five years. It also reports that the number of consumption-based software companies more than doubled between 2015 and 2024. These are market observations and executive expectations, not a universal prescription.
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Finance teams should model gross margin at realistic usage levels. A highly active customer may generate more revenue under consumption pricing, but inference and support costs can rise faster. Contracts also need safeguards for sudden usage spikes, model-price changes and workloads that require human review.
Go-to-market becomes outcome-led
AI is harder to sell through a feature checklist because customers must understand which workflow improves, what data is needed, how much supervision remains and how the result will be measured. Sales teams therefore need to connect the product to a business outcome and a deployment plan.
McKinsey describes more verticalized selling, greater engagement with senior business and technology leaders, and forward-deployed technical roles that help customers integrate and optimize AI. The ecosystem can include software vendors, systems integrators and AI-native firms. That creates implementation opportunities, but the sources do not establish a specific partner program or universally suitable provider.
Nearly 70 percent of software executives ranked go-to-market transformation as a top investment priority for the next few years, according to McKinsey. A practical sales process should define the customer’s baseline, data and permission requirements, approval points, expected time to value, and the evidence that will determine whether a rollout expands.
Implementation is part of the product
An agent that must read several systems, follow a company policy and take controlled actions cannot be deployed like a standalone screen. Teams may need connectors, identity and access controls, data mapping, prompt or policy configuration, evaluation datasets, observability and change management. Forward-deployed engineers or solution specialists can be necessary even when the long-term product is standardized.
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This implementation work should be treated as a repeatable capability rather than an unlimited consulting promise. Templates, documented interfaces and clear boundaries help a vendor deliver customer-specific value without turning every installation into a bespoke project.
Engineering and internal operations are redesigned end to end
An AI-centric engineering organization applies AI across discovery, requirements, design, coding, testing, release, monitoring and operations. Simply adding a coding assistant to an old process may improve individual speed while leaving bottlenecks, handoffs and quality problems untouched. McKinsey says companies may need more consistent stages, artifacts, tools and roles, with fewer unnecessary handoffs.
AI can also support sales research, customer service, marketing, professional services and back-office work. McKinsey reports that 93 percent of software leaders cited automation of internal operations as a high-priority area. The business case should count the full workflow: time saved, review effort, error rates, rework, customer impact and any new monitoring cost.
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Infrastructure, organization and governance requirements
AI products and agents can require real-time workloads, model routing, retrieval systems, evaluation pipelines, logging, cost controls and observability. The exact stack depends on the product, but the organization must be able to answer which model or tool produced an output, what data it accessed, what action it took and whether a person approved consequential steps.
Microsoft’s January 13, 2025 announcement provides one example of structural change. It said its CoreAI group would bring together developer, AI-platform and Office of the CTO teams to build an end-to-end Copilot and AI stack for first- and third-party customers. This is Microsoft’s own organizational choice, not a template that every software company must copy.
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In a July 24, 2025 corporate blog, Microsoft chairman and CEO Satya Nadella wrote: “Our differentiation will come from how we bring these layers together to deliver end-to-end experiences and products, with the core ethos of a platform company that fosters ecosystem opportunity broadly.” He also described an ambition to move “from a software factory to an intelligence engine empowering every person and organization to build whatever they need to achieve.” Those statements express a strategic direction; they are not evidence that the same structure or results will work elsewhere.
Controls that must be explicit
The sources identify several risk areas that a responsible transformation must address:
- Data quality: inaccurate, incomplete or stale records can produce confident but wrong outputs.
- Bias and explainability: users may need to understand why a recommendation was made and test it for unfair patterns.
- Intellectual property: training data, customer content, generated code and outputs require clear rights and handling rules.
- Security: agents need least-privilege access, protection against prompt or tool abuse, and monitoring for data leakage.
- Human oversight: the company must specify which actions require review, approval or a second check.
- Accountability: named owners need to investigate failures, reverse harmful actions and communicate incidents.
Governance is not a final compliance document. It is part of product design, supported by test cases, audit logs, access policies, escalation paths and ongoing evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.People and work evolve in stages
Jared Spataro, Microsoft’s CMO of AI at Work, describes a three-phase workplace model: people using AI assistants; human-led teams working with agents; and humans setting direction while agents operate workflows. Microsoft presents this as a conceptual model, not a universal maturity scale. It is useful because each phase changes management questions.
Phase one: individual assistance
Employees use AI for drafting, search, summarization or analysis while existing roles and approvals remain. The main needs are training, approved tools, data-handling rules and measurement of quality and time saved.
Phase two: human-led teams with agents
Agents take on repeatable portions of a team’s workflow. Managers define responsibilities, queues, escalation rules and review standards. Process redesign matters more than simply granting everyone access to an assistant.
Phase three: human direction, agent execution
People set goals, constraints and priorities while agents coordinate multi-step work. This phase requires stronger identity, permissions, observability, evaluation and incident response because the system can act at greater scale.
In a Microsoft WorkLab account, one sales team of nearly 700 people showed 9.4 percent higher revenue per person and 20 percent more deals closed than peers with lower Copilot usage. Those are company-reported comparisons, not controlled causal evidence that the tool alone produced the differences. Microsoft Chief AI Transformation Officer Pam Maynard also said, “The core of what makes a good seller hasn’t changed. They deeply understand the customer’s context, priorities, and pain points, and then map our solutions to those needs.” The example reinforces that domain judgment and customer understanding remain important even as tools change.
A practical decision framework for software leaders
Before declaring an AI transformation, leadership can evaluate the choices below and document the assumptions behind each one.
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- Select the product path. Decide whether an embedded agent, an orchestration layer or a specialist domain product best fits the data, APIs and customer context.
- Map the workflow. Mark where data enters, where the agent reasons or retrieves information, which tools it can call and where a person must approve.
- Model economics. Include inference, storage, implementation, support and review costs. Test seat, usage, action, output and outcome units against customer value and margin.
- Build the delivery capability. Provide connectors, identity controls, evaluation data, observability and repeatable implementation patterns.
- Redesign roles and processes. Remove unnecessary handoffs and define how product, engineering, sales, support and operations work with agents.
- Set governance before scale. Establish data permissions, security controls, quality thresholds, escalation and ownership for failures.
- Measure realized impact. Compare baseline and post-deployment outcomes, not only model accuracy, usage or pilot productivity.
Common mistakes that do not amount to transformation
- Adding a chatbot while leaving the underlying workflow, data model and approval process unchanged.
- Advertising a large productivity percentage from a pilot as if it were a guaranteed company-wide financial result.
- Assuming seat pricing must disappear instead of testing which unit reflects value and variable cost.
- Giving an agent broad system access before defining permissions, review points and rollback procedures.
- Copying another company’s organization chart or reported sales comparison without matching its data, process and adoption conditions.
- Measuring model or developer activity while ignoring customer retention, revenue, margin, quality and risk.
McKinsey reports that 40 percent of surveyed software leaders expected AI to unlock more than 20 percent revenue growth beyond their current trajectory, while 11 percent expected gains above 50 percent. It also cites 20 to 40 percent operating-cost reductions and 12 to 14 percentage-point EBITDA-margin increases reported by AI-centric organizations. These figures describe expectations or reported experiences among surveyed or identified organizations; they are not typical outcomes or guarantees.
What success looks like
A software company has become meaningfully AI-centric when customers buy a measurable outcome enabled by intelligent workflows, the product can operate across the necessary systems with controlled autonomy, and the company’s own teams use redesigned processes to deliver it efficiently. Pricing reflects both value and inference cost. Engineering and operations continuously evaluate quality, security and reliability. Leaders can show which business metrics improved and which assumptions failed.
The transition is therefore less about attaching an AI label and more about changing the company’s center of gravity: from shipping isolated features to delivering governed, outcome-oriented intelligence throughout the product and the organization.
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