Like a rubber band stretched too far, enterprises can absorb change only for so long before they begin to snap. The signs are subtle: strategies that don’t land, decisions that stall, and teams stuck interpreting instead of executing. But the friction builds: plans expire faster than they can be delivered and technology scales faster than the organization can absorb. The harder you try to keep up, the further you get left behind. This isn’t failure. It’s a wake-up call.
For enterprise leaders—COOs, transformation chiefs, and shared services and technology heads—this is more than noise in the system. It’s a signal that the model itself is cracking.
What we’re seeing now isn’t the breakdown of enterprise capability; it’s the collapse of enterprise logic. The models we built to deliver performance were designed to control complexity, not live inside. They were designed for predictability, not perpetual change.
Every era hits a threshold—this one just got here faster
Every system has two fundamental thresholds—one where it begins to strain and the other that marks a complete transformation. Enterprise history is defined by the operating models we build and the moments when they stretch and eventually break. This occurred three times already (see Exhibit 1):
- The Industrial Age taught us to command and control, standardize the work, centralize decisions, and optimize for efficiency. This gave us scale but collapsed under the weight of information it couldn’t process.
- Digital Transformation let us automate and integrate. We centralized data, streamlined operations, and squeezed out inefficiencies. But the more we digitized, the more brittle and siloed we became.
- The Platform Era offered flexibility. APIs, agile teams, and cloud systems were supposed to make us adaptive. But instead of simplifying complexity, we just got better at absorbing it until the systems and the people inside them started to overload.
Each wave pushed us further, yet each hit the same wall: the illusion that control can keep up with complexity.
Exhibit 1: The evolution of business operating models—with each shift triggered when existing systems hit their complexity limits
Source: HFS Research, 2025
Now we’re watching the cracks appear again, but just faster this time. Today, four forces are exposing the fundamental flaw in this approach:
- Decision capacity has collapsed: The volume of decisions has surpassed human ability. Enterprises face billions of data points and thousands of interconnected choices each day. No dashboard or planning cycle can keep up with the demand for real-time action.
- Strategy expires before it executes: Planning cycles are no longer in sync with the pace of change. In April 2025, global markets dropped 1,700 points and rebounded 900 within hours. Leadership teams weren’t executing strategies. They were trying to understand what just happened.
- Intelligence is suffocating inside legacy systems: AI is being plugged into processes it was never meant to handle. A recent study found that only 12% of enterprises have successfully embedded AI into core decision-making and operations. The other 88 percent remain stuck in experimentation because their systems weren’t built for intelligence, autonomy, or real-time execution.
- Work no longer fits the model: It moves across boundaries, forms around outcomes, and adapts in real time. However, most organizations define work by roles, functions, and static ownership.
The cracks aren’t the ending—they’re an opportunity for a reset
You can’t retrofit your way out of a broken system. Enterprises don’t need more agility layered on old scaffolding. They need a different foundation altogether. At HFS, we call this the ‘Adaptive Operating Model.’
This isn’t about adding agility to a brittle structure. It’s about building a new core that can sense change, respond with intelligence, and continually realign execution with intent. It replaces control with orchestration, roles with outcomes, and centralization with autonomy.
A defining feature of this model is temporal agility—the ability to operate across multiple time horizons simultaneously. Adaptive enterprises act where it matters most in real time, stay coordinated in the near term, and execute strategically in the long term. Unlike traditional models that force strategy, planning, and execution into rigid cycles, organizations can move fluidly across tempos without internal friction.
The Adaptive Operating Model is grounded in four core principles that shift how work happens, how decisions are made, and how value is delivered:
- Intent over instruction: Work doesn’t need constant direction when there’s a shared purpose. Teams and agents align to outcomes without waiting for approvals.
- Intelligence in flow: Insight is embedded across the enterprise. Decisions are distributed, contextual, and made closer to where work happens, not escalated upward.
- Value over function: The organization flexes around value, not structure. Work forms around outcomes, not roles, departments, or reporting lines.
- Resilience by design: The system doesn’t resist volatility; it absorbs it. Variability becomes a signal, not a threat. The enterprise learns and evolves as it operates.
The model is anchored by six foundational layers (see Exhibit 2):
- Enterprise intent and adaptive culture: Adaptive enterprises align around a shared purpose, enabling teams to act autonomously without waiting for top-down direction. For example, a logistics company allows frontline teams to reroute shipments during disruptions without waiting for corporate approval because everyone is aligned on the same mission to deliver on time.
- Embedded governance and trust: Governance is not a gatekeeper but a built-in function operating continuously across workflows. Unlike traditional models that rely on manual checks and post-facto compliance, this model directly integrates risk, compliance, and ethics into the workflow. A healthcare provider, for instance, integrates automated HIPAA compliance into its patient intake process, ensuring privacy is upheld without slowing down care.
- Data and intelligence nervous system: Adaptive enterprises are powered by a real-time data and intelligence layer that enables continuous organizational sensing and decision-making. A retailer, for example, draws from live customer sentiment, inventory levels, and competitor pricing to dynamically adjust promotions and stocking decisions throughout the day.
- Value streams and capability mesh: Work is no longer managed by rigid departments but flows through dynamic, outcome-focused structures. Traditional hierarchies are replaced with flexible ecosystems that pull in the right talent, tools, and AI when and where needed. For example, an insurance firm forms rapid-response pods for natural disaster claims, pulling in experts and tools from a shared pool to resolve cases faster.
- Modular capability platforms: The capabilities are designed as reusable, composable services that can be deployed rapidly across different business needs, shifting away from traditional one-off solutions locked within silos. For instance, a bank uses a modular KYC platform for customer onboarding and across compliance and partner integrations, enabling consistency and scale without redundancy.
- Agentic work systems: Execution is driven by intelligent agents—human and machine—that sense, decide, and act autonomously in real time. Traditional models rely on manual coordination and human intervention at every step. A telecom company, for example, uses AI agents to monitor network health, predict outages, and automatically deploy fixes, freeing engineers to focus on higher-value innovation.
Exhibit 2: Pillars of the Adaptive Operating Model
Source: HFS Research, 2025
The Bottom Line: Traditional operating models are crumbling—not from execution failures but because their logic no longer fits how the world works.
Enterprises can’t afford to rely on rigid hierarchies, centralized decision-making, and single-speed execution. Complexity, velocity, and intelligence have outgrown the scaffolding.
The adaptive operating model offers a reset. It replaces control with orchestration, static roles with dynamic outcomes, and linear planning with continuous alignment. It is built to operate across multiple time horizons, powered by shared intent, embedded trust, and autonomous agents.
