Tech Industry Signals: Five Operating Priorities for the AI-Enabled Enterprise

# The Decisions Around AI Are Becoming More Connected

Tech companies have moved quickly on AI across products and internal operations. The pressure now is to turn that activity into consistent value at scale while managing cost, risk, talent constraints, and the pace of product change.

As AI becomes more embedded across the business, decisions about autonomy, models, talent, knowledge, and delivery are increasingly connected. Greater autonomy changes accountability. Model decisions shape cost and flexibility. New ways of working alter roles and skills. Faster delivery exposes weaknesses in knowledge and operating systems.

For tech companies, progress depends on coordinating these decisions across functions. Advancing one area without adjusting the surrounding structures can create fragmentation, risk, and friction that limit value.  This brief highlights five signals that deserve leadership attention in the months ahead.

Five Signals to Watch

1. Autonomy: Define how people and agents share work, decisions, and accountability.
2. Model strategy: Balance enterprise standards with flexibility as model capabilities and economics change.
3. Workforce: Redesign roles and talent decisions around the work humans and AI will each perform.
4. Knowledge: Treat trusted organizational knowledge as infrastructure for AI-enabled operations.
5. Operating model: Connect strategy, funding, governance, and delivery so faster team-level work produces enterprise outcomes.

# Signal 1: Designing the Autonomous Enterprise

Agentic AI is moving enterprises from task support toward coordinated, multi-step execution. AI agents can now route service requests, resolve incidents, manage approvals, and complete workflows that span systems and functions. As autonomy increases, organizations will need to define where agents can act independently, where people must step in, and who is accountable for the outcome.

Why it matters

Tech industry organizations are well positioned to build and deploy agentic solutions quickly, but decentralized innovation can create organizational sprawl. Functions may automate similar workflows independently and establish inconsistent approaches to ownership, oversight, and measurement. Approximately 50% of CEOs report that rapid investment has already resulted in disconnected technology within their organizations.

These gaps become more consequential as agents gain access to enterprise systems and take actions that influence operational outcomes. Without clear decision rights, validation standards, and escalation pathways, accountability becomes difficult to trace, and risk accumulates.

75% of organizations  are piloting or deploying AI agents, but only 13% strongly agree they have the governance structures needed to manage them effectively.

Organizations leaning into a more autonomous future are redesigning how work is governed, assigned, and measured across the enterprise. They are creating visibility into use cases across functions, defining where agents can act independently, and clarifying human and agent responsibilities. 

Who should pay attention

• Chief Information Officer: Govern agent access, integration, and performance across enterprise systems while reducing risk.
• Chief Operating Officer: Redesign operational workflows, controls, and escalation pathways as agents take on more end-to-end work.
• Chief of Staff and VPs of Strategy and Operations: Align agentic AI investments to enterprise priorities and measure whether autonomy is improving business outcomes.

Where to focus

Organizations should expand agentic autonomy alongside the enterprise changes required to support it. Leaders need to prioritize use cases with clear enterprise value, revisit how ownership and decisions are structured, and align governance with the level of autonomy being introduced. Building these organizational foundations in parallel will allow agentic AI to scale with greater consistency, accountability, and business impact.

• Where can greater agent autonomy materially improve performance, and what needs to be true to scale it across the enterprise?
• Where should human review, approval, or intervention remain explicit as agent autonomy expands?
• How will accountability work when outcomes depend on people, agents, systems, and functions working together?

# Signal 2: Building an Adaptive AI Model Strategy

As AI models diverge in capability, cost, integration, and risk, organizations are deciding where to standardize and where to preserve flexibility. Most are moving toward a hybrid approach, using an anchor vendor for common enterprise needs while retaining model choice for specialized or rapidly evolving use cases. The leadership challenge is deciding where to standardize, where to preserve choice, and who has the authority to make, fund, and revisit those decisions as the market shifts.

Why it matters

Model strategy now influences architecture, procurement, investment priorities, data controls, and vendor dependency. Yet only 53% of organizations have established a formal process for approving AI projects, leaving many model and vendor decisions to develop through disconnected team-level choices.

Without a shared approach, teams may choose vendors with misaligned criteria, fund overlapping solutions, and prioritize critical use cases on platforms that are costly to change. Leaders then struggle to see what value they are delivering and where vendor dependency is growing.

75% of technology leaders say their organization must change its operating model within the next 12 to 18 months to generate greater value from AI.

Organizations preparing for continued market change are defining common assessment criteria, clarifying ownership, governing exceptions, and creating a repeatable mechanism for adding, replacing, or retiring models.

Who should pay attention

• Chief Information Officer: Set enterprise standards, manage vendor dependency, and govern model choices across the technology portfolio.
• Chief Technology Officer: Determine where model flexibility creates strategic value and where standardization reduces complexity and reinvention.
• Chief Data and AI Officer: Establish model evaluation criteria, portfolio visibility, and ongoing performance review across use cases.

Where to focus

AI model strategy requires a repeatable enterprise method for making and revisiting technology choices. Leaders should define decision ownership, shared evaluation criteria, procurement guardrails, and portfolio visibility before model decisions fragment further across the organization. This discipline allows the enterprise to scale with greater consistency while preserving the flexibility to adapt as vendors, capabilities, and business needs change.

• What AI model strategy best supports our business objectives, risk tolerance, and need for flexibility?
• Who owns vendor selection, exceptions, and decisions to switch or retire a model?
• Do we have enough visibility into value, cost, risk, and dependency across our model portfolio to know when our strategy should change?

# Signal 3: Aligning the Workforce to How Work Is Changing

Technology organizations are reassessing whether their workforce strategy matches how work is changing. AI is automating tasks, reshaping roles, and changing the skills people need as organizations look to get more from existing talent. Leaders are revisiting workforce plans, role design, talent development, and organizational structure to align their people and capabilities to where the business is headed.

Why it matters

AI is accelerating workforce changes that once unfolded over years. As more tasks become automated or AI-enabled, organizations need to understand how roles will change and where human skills will become more valuable. Yet only 23% of leaders say their workforce is fully ready for AI, down 6% from last year.

For leaders, this means looking beyond headcount to understand the work itself. Workforce planning increasingly requires a view of which tasks can be automated, which roles should be redesigned, and where capabilities should be built, hired, or redeployed.

49% say skills and talent gaps are a top barrier to AI success, second only to security concerns (52%).

Organizations leaning into this shift are connecting workforce planning, skills strategy, role design, and organizational structure around the capabilities they will need next.

Who should pay attention

• Chief Human Resources Officer: Translate changing work into new role, skill, hiring, development, and mobility strategies.
• Chief Operating Officer: Align workforce capacity and organizational structure as AI changes how work is delivered.
• Chief of Staff and VPs Strategy and Operations: Connect workforce choices to business priorities and future capability needs.

Where to focus

Workforce planning now needs to account for both human talent and AI-enabled work. Leaders should assess how work is changing, then use that insight to shape workforce plans, talent investments, and organization design. Connecting these decisions helps organizations build the capabilities they need while preparing the workforce to adapt as AI continues to reshape roles and tasks.

• How is AI changing the mix of work and capabilities we will need across the organization?
• Where should we redesign work and roles, develop or redeploy existing talent, and hire for new capabilities?
• How are we preparing today’s workforce for the capabilities required to deliver our future strategy?

# Signal 4: Modernizing Knowledge for the AI-Enabled Enterprise

As AI becomes embedded across the enterprise, organizational knowledge is taking on a new role. AI increasingly relies on internal processes, documentation, and expertise to answer questions and execute work. For technology organizations, fragmented or outdated knowledge can directly limit what AI can do. Leaders are rethinking how knowledge is captured, structured, and maintained so it can reliably support both people and AI.

Why it matters

AI can apply organizational knowledge at a scale that was previously difficult to achieve, making the quality of that knowledge more consequential. In a 2026 survey, 71% of organizations struggle with data accuracy, access, and management, making it the top barrier to AI adoption.

Processes, policies, and institutional expertise need enough context and structure for AI to use them reliably. As organizations move toward more AI-enabled operations, knowledge also needs a clear lifecycle, so content can be validated, updated, and retired without relying on manual effort alone.

56% of IT leaders say classifying and tagging unstructured data is the top challenge in preparing data for AI.

Organizations leaning into this shift are combining stronger ownership and governance with AI-enabled content creation and maintenance, allowing human expertise to focus where judgment and context matter most.

Who should pay attention

• Chief Information Officer: Turn knowledge across enterprise systems into trusted, reusable context for AI-enabled operations.
• Chief Data and AI Officer: Ensure AI can access organizational knowledge with the quality, context, permissions, and ownership needed to use it reliably.
• Chief Operating Officer: Reduce dependence on tribal knowledge as more operational work becomes AI-enabled.

Where to focus

AI readiness requires knowledge to be managed as an operational asset. Leaders should focus first on the knowledge that powers critical work, then establish the ownership, structure, and lifecycle needed to keep it useful. As AI supports more routine knowledge management, human expertise can focus on validation, improvement, and the knowledge that differentiates the organization.

• Where are gaps in our knowledge limiting how effectively we can scale AI capabilities, and what are we doing about them?
• How will we keep enterprise knowledge accurate, current, and trusted as both people and AI create and use it?
• How can we reduce reliance on individual expertise and make critical knowledge easier to reuse across teams and AI-enabled workflows?

# Signal 5: Evolving the Operating Model for Faster Execution

Technology operating models are under pressure as the pace and complexity of delivery increases. AI is accelerating development, while platform dependencies and cross-functional work make execution more complex and interconnected. Organizations are revisiting planning, funding, governance, decision rights, and cross-team workflows so their operating models can keep pace with how quickly teams build, make decisions, and deliver.

Why it matters

Organizations have evolved faster than many of the systems used to manage them. In our research, 42% report operating model changes and 46% report organizational restructuring over the past year. As Product, Design, Data, Security, and Platform Engineering teams become more interdependent, unclear priorities, misaligned planning cycles, and slow decisions can create friction across delivery.

71% of CEOs say their IT operating models are not fit for the age of AI, while only 24% of CIOs believe their models effectively adapt to business needs.

Leaders are responding with clearer ownership, more adaptive planning, funding tied to roadmap priorities, and governance that gives teams greater autonomy within shared guardrails. The opportunity is to connect strategy, investment, and delivery, so increased speed at the team level translates into stronger outcomes across the enterprise.

Who should pay attention

• Chief Technology Officer: Shape the technology operating model so investment, teams, and decision-making stay connected as delivery accelerates.
• Chief Product Officer: Connect priorities, funding, and ownership across product and platform teams to keep delivery focused on customer and business outcomes.
• VP of Engineering: Remove cross-team friction while giving engineering teams the clarity and autonomy to execute effectively.

Where to focus

Technology operating models need to evolve with the work they support. Leaders should connect planning, funding, governance, decision rights, and cross-team workflows rather than improving each in isolation. Getting these elements working together helps organizations turn greater team-level speed into more focused, consistent execution at scale.

• Where are planning cycles, funding models, governance, or cross-team dependencies creating the greatest friction between strategy and delivery?
• Which decisions should individual teams own, and where are shared guardrails required?
• How are we managing the parts of our operating model that may become the next constraints on speed and execution?

# Setting Priorities for the Months Ahead

These five signals will play out differently in every organization. The leadership task is to identify where progress in one area is being limited by another. An ambitious agent strategy may stall without trusted knowledge. Faster product delivery may expose slow funding or governance decisions. New tools may create little value if roles, skills, and workflows remain unchanged.

Three Moves to Make Now

1. Name the Constraint: Determine which signal is most likely to limit value or execution over the next 12 months.
2. Connect the Decisions: Bring the relevant technology, product, operations, data, and people leaders together around shared outcomes and decision rights.
3. Create a focused action agenda: Define the few changes to governance, ownership, investment, roles, or workflows that will make the greatest difference now.

# Continue the Conversation

Propeller helps tech industry leaders turn industry shifts into practical operating decisions and execution plans. Let’s discuss which signals are creating the greatest pressure for your organization and where to focus next.