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The best 2025 AI bet was not simply buying the largest model or distributing an assistant to every employee. CIOs should have funded a small portfolio of measurable, workflow-level deployments first—then invested in the data, integration, security, governance and workforce capability needed to operate them safely. More experimental spending belonged in bounded agents, AI-enabled products and end-to-end process redesign, with milestone-based funding rather than open-ended pilots.
The right question is not “Which AI should we buy?”
“Place an AI bet” can mean several different things: buying assistant licenses, funding an internal product team, purchasing model or cloud capacity, modernizing data platforms, building security controls, redesigning a business process, or testing an uncertain new operating model. These investments have different time horizons, risks and definitions of success.
A seat license should not be evaluated with the same formula as a data-platform program or an autonomous claims-processing system. The useful executive question is:
Which business workflows can we improve enough to justify the cost, risk and organizational change?
That question shifts attention from model brands to measurable outcomes. It also prevents a common mistake: confusing access to AI with business value.
The 2025 AI investment hierarchy
- Measurable workflow applications. Start with high-volume, information-intensive work that has a baseline and a business owner.
- Data, retrieval and integration foundations. Make authoritative, permission-aware information available inside the workflow.
- Security, governance and observability. Control access, monitor usage, test outputs and contain failures.
- Adoption and role-based skills. Train people on specific jobs and redesign the work around them.
- Bounded agents. Experiment where tasks are reversible, auditable and low consequence if wrong.
- Transformation and AI-enabled products. Fund fewer, larger bets that redesign an end-to-end process or create new revenue.
- Model and infrastructure optionality. Preserve the ability to change suppliers when quality, cost, latency, privacy or availability changes.
McKinsey’s 2025 research describes broad AI use and growing agent experimentation, but also says meaningful enterprise-wide bottom-line impact remains uncommon. Its reported figures—23% of respondents scaling an agentic system somewhere and another 39% experimenting—describe survey responses, not universal enterprise adoption or proven ROI. McKinsey’s survey also identifies IT, knowledge management and software engineering among prominent areas of use.
1. Fund workflow-level value first
The strongest early candidates share several characteristics:
- High transaction or task volume.
- Expensive human handling.
- Repetitive or information-heavy work.
- Digital inputs and outputs.
- A named business owner.
- Existing performance metrics.
- Low-to-moderate consequences when the system is wrong.
- Reversible actions and human escalation.
- Accessible, accurate and permissioned data.
- A realistic path into the existing system of record.
IT operations and service management
IT is often a practical starting point because the work is already digital and the organization usually controls much of the relevant data. Useful applications include ticket classification and routing, suggested resolutions, knowledge-base generation, incident summarization, root-cause assistance, employee self-service and change-risk analysis.
Measure ticket-resolution time, first-contact resolution, escalation, backlog, employee satisfaction and cost per resolved request. An assistant that produces attractive answers but does not reduce handling time, improve resolution or preserve service quality has not yet created a business case.
Software engineering
Potential uses include code generation and explanation, test creation, code-review assistance, vulnerability remediation, documentation, migration support, legacy-code analysis and developer knowledge retrieval.
Do not use lines of code or raw suggestion acceptance as the primary success metric. Measure completed and accepted delivery, review time, defect rates, security findings, rework, deployment frequency and developer experience. The relevant outcome is secure software delivered with less friction—not more generated text.
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Knowledge management and enterprise search
Internal research tools can help employees find policies, procedures, technical documents, sales material, legal information and compliance guidance. But retrieval quality is a product concern, not background plumbing.
A production knowledge assistant needs permission-aware retrieval, source citations, document-freshness controls, confidence or uncertainty signals and a clear distinction between retrieved facts and generated interpretation. If it cannot respect document permissions or identify contradictory source material, it may increase risk while appearing helpful.
Customer service and contact centers
Good initial applications include agent assistance, intent detection, conversation summaries, suggested replies, knowledge retrieval, low-risk self-service and tightly bounded automated resolution with escalation.
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Track average handle time, first-contact resolution, escalation, customer satisfaction, repeat contacts, error rates, remediation and cost per resolved interaction. A lower handle time is not a success if repeat contacts or customer complaints rise.
Sales and marketing
AI can support account research, proposals, campaign variations, lead prioritization, sales-call preparation, CRM summarization and product intelligence. Revenue attribution is harder here because territory, seasonality, pricing and campaign mix can change at the same time. Use controlled pilots, matched comparisons or randomized rollouts where practical instead of assigning every sales improvement to AI.
Finance and back-office work
Document processing, invoice intake, reconciliation assistance, close-process support, policy interpretation, procurement intake, forecasting assistance and audit-evidence preparation can be valuable targets.
AI should prepare, recommend or flag—not quietly bypass approval, segregation-of-duties or audit controls. Measure cycle time, exception rates, reconciliation accuracy, close duration, rework and the cost of manual review.
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Supply chain and operations
Demand-signal analysis, exception management, schedule recommendations, supplier-risk monitoring, maintenance support and logistics-document processing may offer meaningful gains. Physical-world processes require stronger validation than informational copilots: predictions must be tested against actual outcomes and operational constraints.
2. Invest in the scaling layer
Data access, integration and governance should not be treated as overhead separate from AI value. Without them, organizations produce demonstrations rather than durable operating improvements.
Data and retrieval foundations
- Searchable, permission-aware enterprise content.
- Metadata, lineage and data-quality controls.
- Identity-aware access to documents and systems.
- Retrieval-augmented generation where authoritative internal information matters.
- Evaluation datasets and feedback loops.
- Clear ownership for source data and document freshness.
A CIO should build the smallest reliable platform that supports the first production workflows. Not every organization needs its own model-training cluster, a complex multi-agent platform, multiple vector databases or a custom foundation model.
An AI control plane
Shared capabilities should cover model routing, prompt and configuration management, evaluation, guardrails, identity and authorization, logging, cost allocation, data residency, human approvals, incident management and model or vendor inventory.
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Security and governance
AI security belongs in the investment portfolio, not in a compliance appendix. Core controls include:
- Approved-use policies and data classification.
- Role-based identity and access controls.
- Sensitive-data detection and loss prevention.
- Prompt-injection, data-poisoning and retrieval-integrity testing.
- Model, application and vendor inventories.
- Output evaluation and regression testing after model changes.
- Audit logs, human approval gates and incident response.
- Retention, deletion and geographic controls.
- Third-party software and model-supply-chain reviews.
- Usage, latency and cost monitoring.
For agents, add least-privilege credentials, tool-level permissions, action and spending limits, rate limits, sandboxed execution, approval thresholds, kill switches and replayable audit logs. Deloitte’s 2025 technology-value research reported that only 25%–32% of surveyed respondents had invested in identity management, federated security or zero trust in the prior year—an indication that security investment may lag the expansion of AI workloads. Deloitte’s research also emphasizes enterprise-wide measurement rather than evaluating technology spending in isolation.
3. Treat adoption as part of the product
Generic “AI literacy” is not enough. Training should be attached to specific jobs and workflows. Managers need guidance on reviewing output, redesigning work, handling exceptions and measuring productivity.
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A practical operating model is federated: a central team owns standards, approved vendors, security patterns, evaluation methods, shared infrastructure and identity controls; business units own use-case selection, process redesign, domain evaluation, adoption and benefit realization.
4. Keep agentic AI as a controlled option
An agent should earn more autonomy gradually. A useful maturity ladder is:
- Assist.
- Recommend.
- Draft.
- Execute with approval.
- Execute within bounded policy.
- Fully autonomous operation.
Early agent candidates have a bounded objective, narrow tool set, low-cost failure, human review, clear completion criteria, a full audit trail, rollback capability, stable underlying systems and a reliable test environment.
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Do not begin with unsupervised production deployment, autonomous vendor payments, legal commitments, employee termination or hiring decisions, medical or safety-critical decisions, unrestricted security changes or high-volume external communication without review. The key question is not whether an agent completes a demo. It is whether the organization can detect, contain and recover from a wrong action.
Gartner’s 2025 survey recommended platform-agnostic agent governance and careful domain selection. Its findings should be read as survey evidence about IT application leaders, not as a universal forecast.
5. A practical portfolio allocation
The following is a proposed planning model, not a verified industry benchmark. CIOs should adjust it for maturity, regulation, existing infrastructure and strategic priorities.
| Portfolio | Suggested share | Purpose |
|---|---|---|
| Core operating value | 50%–60% | Production workflows in IT, engineering, knowledge management, customer service and document-heavy operations. |
| Foundations and controls | 20%–30% | Data access, retrieval, identity, evaluation, security, observability, integration, FinOps and training. |
| Transformation and product bets | 10%–20% | AI-enabled products, end-to-end process redesign and advanced decision support. |
| Exploratory options | 5%–10% | New models, multimodal applications, agent frameworks and novel operating models. |
Exploratory projects should have a hypothesis, time limit, evaluation method and explicit next decision. Transformation projects should receive milestone-based funding. Core initiatives should have quarterly value reviews and a clear path to scaling, redesign or termination.
6. Score every use case before funding it
| Criterion | Question |
|---|---|
| Economic value | What cost, revenue, capacity or risk improvement is plausible? |
| Frequency | How often does the task occur? |
| Baseline | Can current performance be measured? |
| Data readiness | Is the required information accurate, accessible and permissioned? |
| Workflow fit | Can output enter the existing process or system of record? |
| Error tolerance | What happens when the system is wrong? |
| Reversibility | Can a human undo the action? |
| Integration effort | How difficult is production deployment? |
| Adoption likelihood | Will users incorporate it into daily work? |
| Governance burden | What privacy, security, legal or regulatory controls apply? |
| Strategic differentiation | Is this table stakes or a source of advantage? |
| Vendor portability | Can the organization change models or suppliers later? |
Prioritize high-value, high-frequency work with strong data readiness, low-to-moderate risk, clear workflow integration and measurable outcomes. Defer projects with unclear ownership, no baseline, irreversible actions, sensitive data, high integration cost or benefits that cannot be separated from normal business variation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Measure outcomes, not AI activity
Weak metrics include prompts, invited users, tokens consumed, response speed, chatbot conversations, generated-content volume and accepted code suggestions. These measure activity or output, not necessarily value.
Stronger metrics include cost per completed transaction, time to resolution, cycle time, first-contact resolution, defect and rework rates, revenue per employee, conversion, customer retention or satisfaction, security findings remediated, forecast error and capacity returned to higher-value work.
For every project, calculate a value chain:
- Gross benefit: time, cost, revenue or risk improvement.
- Adoption adjustment: the proportion of intended users who actually use the system.
- Quality adjustment: output that requires correction or rework.
- Process adjustment: whether the workflow genuinely changes.
- Operating cost: licenses, inference, storage, integration, support and training.
- Risk reserve: expected error, incident or compliance-remediation cost.
- Net benefit: realized value minus total cost.
Before deployment, record cycle time, labor or vendor cost, quality, volume, customer or employee experience, errors and escalations. Then compare results with a control group, matched teams, a seasonally adjusted pre/post design or a randomized rollout where practical. Self-reported productivity is useful for generating hypotheses, but it is not the same as causal financial impact.
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8. Buy, build or use a hybrid
Buy
Buy when the workflow is common across companies, the organization already uses the vendor’s suite, integration and controls are mature, speed matters more than differentiation, or the process does not create unique advantage.
Build
Build when proprietary data or domain logic matters, the workflow is strategically differentiating, existing tools cannot meet requirements, or the company needs deep integration with proprietary systems.
Hybrid
For many CIOs, the default is hybrid: use a commercial model and managed platform, but retain control over data, retrieval, evaluation, workflow logic, identity, user experience and business metrics.
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For example, a Microsoft-heavy organization may evaluate Microsoft 365 Copilot and Microsoft Foundry. Microsoft lists Copilot Enterprise at $30 per user per month paid annually, alongside a qualifying Microsoft 365 license; agents and connected services may create separate Azure or platform charges. Microsoft’s official pricing page should be checked for current terms.
Microsoft says Foundry is free to explore, while deployed models, agents, tools and underlying Azure services have separate billing. Its product documentation is the appropriate source for current platform details.
Claude Enterprise was listed at $20 per seat per month billed annually, with a 20-seat minimum, while usage is billed separately at API rates. Anthropic’s enterprise page and its billing documentation explain the distinction between seat access and usage charges.
These prices were observed in August 2026 and are not investment recommendations. CIOs should model total cost per completed business outcome, including seats, tokens, agent execution, retrieval, storage, data transfer, cloud infrastructure, implementation, support and premium security or residency features.
9. Avoid the most common failure modes
- Pilot purgatory: a demonstration has no production owner, integration plan or success threshold.
- Seat-first procurement: licenses are purchased before representative roles, usage instrumentation and workflow metrics are defined.
- Unpermissioned retrieval: the assistant can access information the employee could not legitimately see.
- Agent sprawl: teams create overlapping agents without inventory, controls or cost monitoring.
- Shadow AI: employees use unapproved tools because sanctioned systems are too restrictive or unavailable.
- Poor data quality: the model is blamed for contradictory, stale or incomplete source information.
- Unmeasured productivity: time savings are claimed but do not change staffing, capacity, service levels or output.
- Vendor lock-in: prompts, data, identity and evaluation are tightly coupled to one supplier.
- Runaway inference costs: usage-based charges grow faster than completed business outcomes.
- No owner after launch: nobody maintains retrieval sources, evaluates model changes or improves adoption.
What CIOs should not fund first
A broad “AI for everyone” rollout may create awareness, but it should not consume the entire budget without evidence of changed work and measurable value. Start with representative roles, instrument usage and expand where results justify it.
Avoid unrestricted agent permissions over financial systems, production infrastructure, HR records, customer-account changes, sensitive legal or medical data, procurement or payment workflows. Avoid bespoke foundation-model training when a managed model meets quality requirements, the real problem is retrieval or data quality, or the workload is too small to amortize fixed costs.
Likewise, do not purchase GPUs or private infrastructure for prestige. Dedicated infrastructure may make sense for predictable, high-utilization, latency-sensitive or regulated workloads. It is a poor fit when demand is uncertain, models are changing quickly or utilization will be low.
Finally, do not approve an IT-led chatbot without a business owner. Every production use case needs an executive sponsor, process owner, product manager, measurable target and explicit scale-or-stop decision.
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The durable advantage is not access to one model. Models, prices and availability change. The advantage is the ability to repeatedly identify valuable workflows, deploy AI into them, measure outcomes, govern risk and redesign the surrounding work faster than competitors.
For most CIOs, the 2025 portfolio should therefore have been conservative about autonomy but ambitious about integration: fund the workflows where value can be measured, build the data and control layer that makes them reliable, train people around real jobs, and reserve a smaller pool for transformation and carefully bounded experiments.
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