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PwC’s three recommendations for IT leaders in 2025 were to address technology talent gaps, help the business manage technology complexity, and organize data so it can support new uses. They are practical foundations for turning AI and other emerging technologies into business value—not a call to buy more AI tools. The advice was reported by CIO.com on February 24, 2025, drawing in part on PwC’s June 2024 survey. It is historical guidance, not PwC’s latest technology-leader advice as of September 2026.
What were PwC’s three actions for IT leaders?
CIO.com reported the recommendations from Dallas Dolen, a principal in PwC’s Technology practice. The three actions were talent, complexity management and data organization. Cybersecurity supports all three, but it was not one of the three headline actions.
| Action | What it means in practice |
|---|---|
| Move quickly on technology talent | Build the skills needed for priority business outcomes through hiring, internal development, mobility and selective use of partners. |
| Help the business navigate complexity | Connect technology experiments to business value, risk controls, integration, operating ownership and the costs of scaling. |
| Organize data to unlock value | Establish ownership, quality, access, privacy and lineage so data can support AI, analytics and legitimate commercial uses. |
The rationale was a gap between ambition and readiness. In PwC’s June 2024 technology-leader survey, 79% of CIO respondents said they would use generative AI to help change their company’s business model, while 40% said IT was completely prepared to support a new business model. Those are survey responses, not evidence that AI had already delivered the intended changes. The survey context is described by PwC’s technology-leader findings.
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CIO’s State of the CIO 2025 research reported that 54% of CIO respondents said staff and skills shortages were diverting attention from strategic and innovative work. Respondents reported the greatest hiring difficulty for AI/ML roles (38%), cybersecurity (33%) and data science or analytics (21%). These figures describe that survey’s respondents; they are not a universal labor-market measure.
Choose a workforce mix around the work
- Inventory priority skills. Map needs in AI and machine learning, cybersecurity, data engineering and analytics, cloud, ERP, architecture, product management and change management to specific business priorities.
- Develop internal talent. Use training, apprenticeships and internal mobility, especially where employees already understand the company’s data, customers or processes. Measure whether training leads to deployed capability, not just course completion.
- Hire for hard-to-build expertise. Recruit where the organization needs enduring judgment or a distinct capability, and define the outcome each role is expected to enable.
- Use outside help selectively. Contractors, systems integrators, managed-service providers and cloud partners can add capacity for project-based needs. Set expectations for knowledge transfer and preserve internal ownership of strategic decisions.
- Retain technical and business translators. People who can connect a technical option to a real operating process can be as important as narrowly specialized engineers.
The right balance depends on whether the company is building proprietary models, integrating commercial services, developing data products or automating internal processes. A smaller organization may need approved tools, a managed provider and a few high-value use cases rather than a large AI or data-governance department. Moving quickly means forming the capability to deliver, not hiring indiscriminately.
2. Make IT the guide from experiment to responsible deployment
Business teams can start a proof of concept quickly and still discover too late that the data cannot be used, the tool is not approved, the project does not integrate with existing systems or the economics do not work at scale. PwC’s advice, as reported by CIO.com, was for IT leaders to help the business navigate those organizational, regulatory, financial and operational complications.
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Use a consistent review path
- Record the use case. Require a business problem, intended users, accountable owner, data involved, expected benefit and proposed measures of success.
- Classify the risk. Assess data sensitivity, regulatory exposure, decision impact, degree of automation and consequences for customers or employees.
- Review data, privacy and location. Establish what information enters the system, where it is processed, how long it is retained and whether a provider may use it to train models. Check applicable data-location restrictions.
- Review security and accountability. Assess identity and access, vendor security, data leakage, prompt injection, logging, incident response and who is responsible for errors or unauthorized output.
- Build the full business case. Account for licenses, model usage, integration, data preparation, monitoring, training, change management and support—not just the initial purchase.
- Set a baseline and pilot measures. Define current performance and how the trial will measure quality, cost, cycle time, adoption and risk before it starts.
- Make a scale decision. Expand only when results are repeatable, controls are adequate and an operating owner is accountable for the production service.
- Monitor after launch. Track usage, cost, quality, security and privacy incidents, policy violations and changes in user outcomes.
License for workflows and users who can benefit
CIO.com’s report gives the example of a company considering licenses for all 25,000 employees: blanket purchasing may be less efficient than targeting roles and workflows where measurable value is likely. A role-based rollout can avoid paying for unused seats, but it still needs access controls, support and a plan for managing expansion. Low usage, overlapping functionality, inadequate training, inaccessible data or an unchanged process can all undermine the case for a broad rollout.
Do not treat a successful demonstration as proof that a system is ready for production. Integration, human oversight, support responsibilities, adoption and monitoring all affect whether a pilot can scale safely and economically.
3. Organize data before promising AI value
Data readiness is more than installing a warehouse, lakehouse or catalog. Useful data needs accountable owners, consistent definitions and quality standards, appropriate permissions and enough lineage to establish where it came from and how it was changed.
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PwC reported that 46% of TMT executives viewed data monetization as a major transformation challenge, while about 80% had modernized or planned to modernize data within the following 12 months to take advantage of generative AI. These figures describe TMT respondents in PwC’s June 2024 survey, not all companies; see PwC’s sector findings. PwC’s technology-leader guidance also stresses addressing silos, governance, privacy and cybersecurity as part of modernization.
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Check whether data is usable and governed
- Can the business identify its most valuable data domains and name an owner for each?
- Are key terms and measures defined consistently across teams, and can a metric be traced to its source?
- Are sensitive fields classified, and can access be limited to the appropriate people and purposes?
- Are quality rules, retention and deletion requirements, and master or reference data practices clear?
- Can applications retrieve authoritative information rather than arbitrary or stale documents?
- Can teams trace data through transformations and explain the basis for an AI-generated answer?
- Are data-sharing rights clear for partners, licensing, acquisitions and other commercial uses?
Data organization can include cataloging, metadata, lineage, data contracts between teams, interoperability, backup and recovery, and monitoring of data use. A catalog alone is not governance: people need to own the decisions and processes the catalog is meant to support. Modern infrastructure also cannot resolve inconsistent business definitions or unclear rights to use information.
How to execute the three actions together
The recommendations reinforce one another. The business outcomes determine which skills and data matter; people provide the expertise to govern and improve those assets; and a review-and-measurement process helps prevent unsafe or uneconomic deployments. Security and privacy are cross-cutting controls, not separate boxes to check at the end.
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The following 90-day sequence is a practical planning approach, not a timetable stated by PwC.
- Days 1–30: establish the baseline. Inventory priority business workflows, current and proposed technology projects, critical skills, important data domains, systems and risks. Identify existing pilots without production owners.
- Days 31–60: choose and prepare. Rank use cases by expected value, feasibility and risk. Name business and technical owners, determine the skills and data required, set review controls, and establish baseline measures.
- Days 61–90: test and build. Launch a limited number of controlled pilots, begin targeted hiring or training, and address the most valuable data-quality or access gaps. Define how results will be reviewed before expanding.
- After 90 days: scale selectively. Expand work that demonstrates repeatable value with an accountable operating owner; revise or stop experiments that do not meet their measures.
What should IT leaders measure?
Seat counts and training completions are inputs, not proof of business impact. Use measures that show whether the organization can deliver useful technology safely and sustainably.
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- Talent: time to fill critical roles, internal-fill rate for priority skills, retention of key employees, and the share of training that leads to deployed capability.
- Execution: share of projects with named business owners, pilot-to-production conversion, time to approve higher-risk uses, and active-user rate rather than purchased-seat count.
- Economics and outcomes: cost per workflow or transaction, and measured changes in revenue, operating cost, cycle time or quality against a defined baseline.
- Data: share of critical data domains with owners and lineage, data-quality defect rates, and time needed to locate and authorize access to trusted data.
- Risk: security and privacy incidents, policy violations, and quality or performance degradation after launch.
Choose measures suited to the use case. A productivity tool, customer-facing model and internal analytics project do not have identical success criteria.
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How to read the 2025 advice now
The reported recommendations were anchored in PwC’s June 2024 Pulse Survey and CIO’s 2025 State of the CIO research. PwC’s general survey page describes a base of 673 respondents from public and private companies across several sectors; percentages in the underlying materials refer to different respondent groups and should not be combined as if they shared one sample. The PwC survey overview provides that context.
PwC published later technology-leader guidance in June 2025, with a changed emphasis that included employee development, AI-native ecosystems and future-proofing architecture. That later material is available in PwC’s technology-leader outlook. The 2025 actions remain useful as a practical lens, but survey findings are not forecasts, and TMT results need not generalize to every sector. In any year, “move fast” should mean removing avoidable delay—not bypassing security, privacy, compliance or sound investment decisions.
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