AI is increasingly shaping how work is planned, prioritized, and executed. As organizations move beyond early experimentation, the strain often appears not in the technology itself, but in the operating model — the structures that shape how decisions are made and how work gets done.
An operating model defines how an organization executes its strategy and delivers value. It governs how priorities are set, how teams are organized, how work flows across functions, and how decisions are made and overseen. When that model is designed for slower, more linear ways of working, AI tends to introduce friction rather than momentum. Teams may move faster in isolated areas, while decisions, accountability, and execution remain constrained by existing structures.
As AI becomes more embedded in core operations, these misalignments grow more visible and more costly. Organizations that address them early are better positioned to integrate AI in ways that improve decision quality, execution speed, and organizational clarity.
# Don’t Insert AI Solutions. Integrate AI Across End-to-End Operations
Many organizations are approaching AI the same way they approached earlier waves of technology: by deploying tools to solve individual problems. This approach can deliver quick wins, but it also introduces new complexity. Different teams adopt different solutions, ownership becomes unclear, and workflows become harder to manage end-to-end.
Before adding AI to address specific points of operational friction, leaders should examine whether the underlying challenges are rooted in how work is planned, handed off, or governed. In many cases, AI doesn’t create these issues, but it exposes inefficiencies that already existed, such as unclear decision rights, slow approvals, or fragmented workflows across functions. This challenge becomes more pronounced as organizations attempt to move from experimentation to broader adoption, a pattern we see frequently when leaders begin operationalizing AI at enterprise scale.
Like operating models themselves, AI solutions require an end-to-end perspective. Organizations can layer new tools onto existing structures and risk reinforcing misalignment, or they can adapt their operating model to support AI across planning, execution, and governance. The latter approach reduces friction, clarifies accountability, and increases the likelihood that AI delivers sustained value rather than isolated gains.
# Four Operating Model Areas That Require Attention as AI Scales
As AI becomes more embedded in day-to-day work, organizations tend to encounter similar pressure points. These pressures surface first in four areas of the operating model:
- Planning cycles
- Roles and responsibilities
- Cross-functional workflows
- Governance frameworks
These areas shape how quickly insights turn into decisions, and how consistently decisions turn into action. Addressing them early helps organizations move beyond experimentation and integrate AI as a durable capability.
# AI Is Compressing Planning Cycles, Making Real-Time Strategy Non-Negotiable
AI is accelerating the pace at which organizations can analyze data, surface insights, and evaluate scenarios. As a result, planning approaches built around long, linear cycles are increasingly misaligned with how quickly conditions now change.
In many organizations, planning models still resemble waterfall approaches, with extended analysis, decision, and execution phases. AI-enabled tools introduce near-real-time signals on performance, demand, and risk, creating tension between faster insight generation and slower planning and approval cycles. Without changes to planning cadence, teams often continue executing against priorities that no longer reflect current conditions.
Leading organizations are already adapting. One global consumer brand uses AI to monitor social media trends, campaign performance, and real-time sales data. AI tools generate weekly forecasts and recommended adjustments, compressing months of manual analysis into a single planning cycle. This shift enables leaders to revisit priorities more frequently and reallocate resources with greater confidence.
# Roles and Responsibilities Are Shifting Alongside AI
By automating lower-value and repetitive tasks, AI is changing how work is distributed between humans and technology. In recruiting, for example, many organizations are implementing AI hiring assistants to screen candidates, rank resumes, and identify skill gaps. Recruiters can focus on candidate engagement and final interviews, while AI handles repetitive screening activities.
This shift in responsibilities is creating new expectations around how work gets done and introducing AI-related roles focused on orchestration, oversight, and prompt-driven delivery. To support this shift, organizations must redesign job descriptions and skill expectations to clarify expectations for how work is done.
Propeller recently supported an HR team seeking to automate case management. During discovery, we found that approximately 80% of incoming cases involved questions that could be addressed by an AI agent. While automation could reduce manual effort, the HR professionals needed clarity on how their roles and responsibilities would evolve as repetitive, routine work declined. Established change management practices and redefined job descriptions helped the team transition toward higher-value work while maintaining service quality.
Related Read: Why Treating AI as a Teammate (Not Just Technology) Unlocks Its Full Potential
# AI Exposes the Limits of a Siloed Approach
As AI influences multiple steps in an end-to-end workflow, it is increasingly highlighting inefficiencies created by siloed organizational structures. Capabilities that once sat within individual functions are becoming more interconnected, which puts pressure on models built around isolated teams and sequential handoffs.
In response, some organizations are reorganizing around workflows rather than functions. One software company, for example, is using AI to support cross-functional product operations pods. AI tracks feature requests, usage analytics, and customer feedback that teams incorporate into end-to-end delivery decisions. AI also supports backlog prioritization, sprint forecasting, and dependency tracking.
By reducing reliance on handoffs between separate teams, information and approvals move more easily from concept through launch. This structure allows teams to respond more quickly and maintain a tighter connection between strategy, execution, and outcomes.
# Governance Enables AI to Scale Safely
Before AI is embedded into enterprise workflows, organizations must consider governance. Without robust IT-led governance structures in place, enterprise AI initiatives can introduce operational errors, regulatory risk, and slow or inconsistent adoption across teams.
IT teams will need time to embed continuous, cross-functional governance into development, deployment, and operational workflows. This includes vetting AI tools, identifying areas of responsibility, and building security frameworks before new solutions are implemented.
Many organizations are establishing cross-functional AI governance frameworks to support secure scalability. A central AI governance board, for example, may define standards, approve models for deployment, and set enterprise-wide monitoring dashboards, while IT teams integrate automated checks for code quality and model drift. Business units review flagged outputs before they are used operationally, helping ensure AI is applied responsibly and consistently.
# Why the Next 12-24 Months Matter
Senior leaders have a limited window to evaluate and understand how AI will impact their function’s operating model. As AI capabilities mature and adoption accelerates, gaps in planning, roles, workflows, and governance become harder to address incrementally.
Because large transformations often take multiple years to implement, now is the time to develop an AI strategy that accounts for every phase of your operating model. Organizations that take a proactive approach can avoid costly rework and position themselves to scale AI with greater confidence.
A useful starting point is to examine where AI could accelerate value, where existing structures may slow adoption, and which elements of the operating model require redesign. Treating AI as a structural capability, rather than a bolt-on tool, helps ensure investments translate into sustained impact.
Related Read: Why Operating Models Fail And How to Get Yours Back on Track
# Can Your Operating Model Support AI Transformation?
As AI continues to reshape how organizations operate, the question is no longer whether it will affect operating models, but how prepared teams are to adapt. Planning cadence, role clarity, workflow design, and governance all influence whether AI accelerates execution or introduces friction.
Propeller works with leaders on operating model design and evolution to help organizations understand where existing structures are under strain and where change will have the greatest impact. By focusing on how decisions are made and how work is delivered, organizations can integrate AI in ways that strengthen execution and support long-term value creation.
If you are exploring how AI may affect your operating model, let’s discuss where you are seeing pressure and what a practical next step could look like.
