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A software company becomes an AI company when artificial intelligence is central to both what it sells and how it operates. That means redesigning products around AI-enabled outcomes, adapting pricing to new usage economics, rebuilding development and internal workflows, investing in suitable infrastructure and talent, and governing data, security and automated decisions. Adding a chatbot or a few generative features is useful, but it is not a company-wide transformation by itself.
AI company is a business model and operating-model change
There is no formal certification that turns a software vendor into an AI company. The practical test is whether AI affects the company’s value proposition and its end-to-end system for creating and delivering value.
McKinsey’s October 2025 analysis describes an AI-centric software company as one that changes product strategy, business model, go-to-market, development, internal functions, infrastructure and organization together. A firm can begin with one product, but the destination is broader than an isolated feature. Leaders should be able to explain which customer outcome AI improves, how that outcome is measured, what it costs to deliver, and which controls keep the system dependable.
This distinction matters because a local productivity experiment can look impressive without changing revenue, margins or customer retention. The transformation is real only when the company converts technical capability into repeatable customer value and sustainable economics.
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Three product paths to becoming AI-centric
Most software companies choose among three architectural directions. They can use more than one, but each path has different integration, investment and defensibility requirements.
| Path | What the customer gets | Advantages | Trade-offs |
|---|---|---|---|
| AI added to an existing application | An assistant or agent inside the current product, such as drafting, search, recommendations or task automation. | Fastest route for an incumbent; familiar user experience and existing distribution. | Capabilities are constrained by the host system’s APIs, data model and product roadmap. A feature may be easy to copy. |
| Agent-centered orchestration | An interface that plans and coordinates work across several tools or systems. | Can address a complete workflow instead of one screen and may create a simpler customer experience. | Requires more development, integration, monitoring and maintenance. Reliability must be managed across every connected system. |
| Specialist domain product | An AI system focused on a regulated or technically specific workflow, such as a vertical process. | Domain data, terminology and workflow expertise can support strong differentiation. | Customer-specific integration and implementation can be extensive, especially where systems and policies differ. |
The right choice depends on the customer problem, access to relevant data, integration burden and the company’s ability to support the resulting system. There is no universal “best” architecture.
Where defensibility may move
In a feature-led market, user interface and functionality often provide the visible advantage. AI can shift competition toward privileged access to high-quality domain data, workflow context, distribution and the ability to deliver a useful outcome reliably. Data ownership alone is not a guaranteed moat: data may be incomplete, difficult to use lawfully, or too noisy to improve decisions. The advantage comes from turning permitted data and context into a dependable workflow that customers cannot easily reproduce.
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Pricing and unit economics change with AI usage
Traditional per-seat pricing assumes that each human user consumes a relatively predictable amount of software. An agent can perform work that previously required several users, while also generating variable inference and infrastructure costs. That creates pressure on both the revenue metric and the cost base.
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| Pricing basis | What is charged | Question to test |
|---|---|---|
| Seat | Access for a named or concurrent user. | Does the number of users still track the value delivered when agents do more of the work? |
| Usage or tokens | Requests, processing volume or model tokens consumed. | Can customers predict bills, and can gross margin withstand heavy use? |
| Action or output | Completed actions, generated documents, transactions or other measurable outputs. | Is the output clearly defined and valuable enough to justify a variable bill? |
| Outcome | A business result, such as a resolved case or completed workflow. | Can the vendor measure its contribution and agree on attribution with the customer? |
| Hybrid | A base subscription plus usage, output or outcome charges. | Does the base fee cover platform availability while variable charges reflect incremental value and cost? |
These are options to test, not automatic replacements for subscriptions. A company must model model-inference cost, storage, monitoring, support and implementation alongside revenue. Pricing that grows with customer value but grows faster in cost can damage margins.
McKinsey reported in 2025 that 63 percent of surveyed software leaders expected AI to fundamentally change their business model within three to five years. The figure is an expectation, not a measured forecast of what happened. The same analysis said the number of consumption-based software companies more than doubled between 2015 and 2024, indicating a market direction rather than proof that every vendor should adopt consumption pricing.
Go-to-market becomes outcome selling
AI products are harder to sell with a feature checklist alone. Buyers want to know which workflow changes, how quickly value appears, what data is required, who remains accountable and how the system behaves when it is wrong. Sales teams therefore need enough technical and domain knowledge to connect the product to a customer’s operating metrics.
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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. A focused ecosystem can include software vendors, systems integrators and AI-native firms. That observation supports a category-level implementation opportunity; it does not establish a particular partner program or guarantee that an external provider is suitable.
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Implementation may include data mapping, identity and access configuration, retrieval or tool integration, workflow redesign, evaluation setup, user training and production monitoring. If these activities are necessary for value, the company must decide whether to provide them directly, certify partners or build a product that minimizes the service burden.
Nearly 70 percent of software executives in McKinsey’s 2025 survey ranked go-to-market transformation as a top investment priority for the next few years. This is a reported priority, not evidence that a specific sales structure will work for every market.
Development and internal operations must be redesigned
Becoming AI-centric is not simply inserting a coding assistant into the old software process. AI can support discovery, requirements, design, coding, testing, release, monitoring and incident response, but the company needs consistent artifacts, tools, review points and ownership across those stages.
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- Define evaluation sets before release, including normal, ambiguous and adversarial inputs.
- Track quality, latency, cost and failure modes alongside conventional defect metrics.
- Version prompts, models, retrieval sources and tool permissions so a change can be reproduced.
- Keep human review where an incorrect action could cause financial, legal, safety or reputational harm.
McKinsey reports that companies cite 30 to 50 percent developer-productivity improvement in pilots. It also says those gains rarely translate into equivalent top- or bottom-line impact. Faster code generation can increase review, testing, infrastructure or support work, so leadership should measure delivered product value rather than pilot activity alone.
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Business functions
AI can automate or augment sales research, marketing operations, customer support, professional services and back-office work. The useful unit is an end-to-end workflow, not an isolated task. For example, a support workflow might classify a request, retrieve approved information, draft a response, obtain authorization for a refund and record the decision. Each handoff, exception and approval needs an owner.
McKinsey reported that 93 percent of software leaders identified automation of internal operations as a high-priority area. That statistic indicates where executives are focusing attention; it does not establish a universal productivity gain.
Infrastructure, organization and governance
AI workloads can require real-time inference, data pipelines, model gateways, evaluation systems, observability and cost controls in addition to the existing application stack. Reliability targets should cover response time, availability, groundedness, safe refusal and recovery when a model or connected service fails.
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Microsoft offers one example, not a template. On January 13, 2025, it said its CoreAI group would combine 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. In a July 24, 2025 corporate blog, chairman and CEO Satya Nadella said the company intended to reimagine each technology-stack layer, from infrastructure through applications and agents, while emphasizing security and quality. A smaller vendor may need only a focused platform capability rather than a similar corporate structure.
Controls a responsible transformation needs
- Data quality and provenance: know what data enters a model, whether it is permitted for that use and how stale or incomplete records affect results.
- Access and security: restrict tools and records by identity, tenant and task; test prompt-injection, data-exfiltration and unauthorized-action paths.
- Bias and performance: evaluate relevant user groups and failure cases instead of relying on an average score.
- Explainability: provide sources, reasoning summaries or decision records where customers need to review an output.
- Intellectual property: define rights and retention for customer data, generated material, training inputs and third-party components.
- Human oversight: specify which actions require approval, what happens when confidence is low and who owns a harmful outcome.
The sources identify these risk areas but do not prescribe one complete control framework. Controls should be proportionate to the domain and the consequences of failure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.AI maturity is a progression, not a label
Jared Spataro of Microsoft describes a workplace progression: people use AI assistants; human-led teams work with agents; and humans set direction while agents operate workflows. Microsoft presents this as a conceptual model, not a universal maturity scale. It is useful because it distinguishes assistance from delegation.
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- Supervised agents: agents execute bounded steps, with people reviewing exceptions or approving consequential actions.
- Directed workflows: people set objectives and policies while agents coordinate routine work, with monitoring and escalation.
Progress should be earned through evidence of quality, safety and economics. A company should not delegate a workflow merely because a demo looks capable.
What the reported numbers do—and do not—prove
Several widely cited figures help describe ambition, but they should not be treated as guarantees:
| Reported figure | How to interpret it |
|---|---|
| More than 60 percent of top software-industry executives identified seven AI-centric shifts as a top strategic priority. | A survey measure of executive priority, not proof of implementation or success. |
| 40 percent of surveyed software leaders expected more than 20 percent revenue growth beyond their current trajectory; 11 percent expected gains above 50 percent. | Expectations reported by McKinsey in 2025, not realized returns. |
| AI-centric organizations reported 20 to 40 percent operating-cost reductions and 12 to 14 percentage-point EBITDA-margin increases. | Reported results from organizations described as AI-centric; not typical or guaranteed outcomes. |
| A Microsoft 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 use. | A company-reported comparison from Microsoft WorkLab in 2025, not a controlled causal estimate that Copilot alone produced the difference. |
McKinsey’s reviewed excerpts do not provide full survey methodology or denominators for every result. Attribute these figures to the named source and retain terms such as “surveyed,” “expected” and “reported.”
A practical transformation sequence
- Choose a measurable customer or business problem. Define the baseline cost, cycle time, quality level, revenue or retention metric before selecting a model.
- Map the workflow. Identify systems, data permissions, human decisions, exceptions and failure consequences from start to finish.
- Select the product path. Decide whether an in-product assistant, orchestration layer or specialist product best fits the problem and integration constraints.
- Build an evaluation and cost model. Test representative cases, unsafe inputs, latency, inference spend, support load and expected customer value.
- Run a controlled pilot. Compare against a baseline, record adoption and quality, and separate tool usage from business outcomes.
- Redesign roles and handoffs. Update engineering stages, sales responsibilities, customer implementation and approval points rather than layering AI onto unchanged work.
- Choose a sustainable commercial model. Test seat, usage, output, action or hybrid pricing against value realization and margin sensitivity.
- Scale with governance. Add monitoring, incident response, access reviews, model and prompt versioning, and a retirement process for poor-performing capabilities.
Common mistakes and how to avoid them
- Calling a feature launch a transformation: require changes in customer outcomes, operations and accountability, not just a new interface.
- Assuming seats are obsolete: model alternatives while preserving a subscription where it still matches value and cost.
- Extrapolating pilot productivity: track shipped outcomes, revenue, margin, quality and customer retention after deployment.
- Copying a large technology company’s structure: adopt the capabilities you need instead of reproducing another firm’s departments.
- Ignoring implementation: budget for integration, data preparation, training and ongoing optimization.
- Delegating without controls: define tool permissions, human approvals, evaluation thresholds and ownership before agents act.
- Treating data possession as defensibility: verify data rights, quality, workflow relevance and the ability to turn context into reliable results.
The bottom-line test
A software company has genuinely become AI-centric when AI changes the customer promise and the machinery that delivers it: products coordinate or perform meaningful work, pricing reflects new value and costs, teams redesign workflows, infrastructure supports dependable operation, and governance makes failures visible and manageable. The label is less important than evidence that customers receive a better outcome and the company can deliver it repeatedly, safely and profitably.
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