Agentic AI--the next wave--are you ready?
How Agentic AI Is Redefining the Way Businesses Operate, Compete, and Win.
I. Executive Summary: Agentic AI Will Reshape How Businesses Operate
The world of business has entered a new era—one driven not just by predictions or pattern recognition, but by autonomous execution. Agentic AI systems aren’t merely tools—they’re teammates, empowered to make decisions and drive outcomes on their own. This seismic shift demands more than just upgrading technology; it requires a complete transformation of how businesses operate, adapt, and compete.
Just as the cloud introduced new DevOps practices, and the PC era redefined the role of knowledge workers, Agentic AI demands a new operating model—one that prioritizes cadence, delegation, and orchestration. With autonomous AI systems in place, businesses can now delegate execution and decisioning to AI agents, allowing human teams to focus on high-level strategy and innovation.
If Agentic AI is the engine, the Agentic X Model is the chassis—the operating model that enables businesses to deploy, scale, and govern these agents to create sustainable competitive advantage.
This shift is not just a technological evolution—it’s a response to a new economic reality. As we move from the PACE world—Predictable, Accountable, Controlled, and Efficient—to a VUCA world—Volatile, Uncertain, Complex, and Ambiguous—businesses must rethink their entire approach. Agentic AI offers a completely new set of autonomous capabilities to adapt and thrive in this environment, where uncertainty and speed are the new norms.
The key to navigating this shift lies in developing what we call the Agentic X Model: a holistic, six-factor operating framework that evolves Galbraith’s classic Star Model to accommodate the demands of an AI-driven world. From strategy and structure to governance and incentives, the X Model redefines how businesses can align their operations to not just survive, but thrive in the agentic age.
In this white paper, I will reference Bessemer Venture Partner’s AI Agent Autonomy Scale, which outlines the evolutionary shift from passive AI tools to autonomous agents capable of driving real outcomes. This framework complements the Agentic X Model, providing businesses with the roadmap they need to unlock the full potential of autonomous AI and scale effectively in the new economic world order.
II. From Mainframes to Agents: A Framework for Tech Revolutions
Every major technological revolution didn’t just introduce new tools—it forced organizations to rethink how they operate. The winners weren’t those who adopted tech the fastest; they were those who reimagined their business models, workflows, and organizational design to unlock its full potential.Let’s examine four pivotal eras of transformation:
1. Mainframe → PC: Empowered Knowledge Workers
The shift from centralized mainframes to personal computers brought computing power directly to the individual. Knowledge work became decentralized. Instead of relying on IT gatekeepers, employees gained the ability to create, analyze, and communicate independently. Organizations had to evolve from top-down command chains to more networked structures where knowledge and decisioning were distributed.
2. On-Prem → Cloud: Agile, Scalable Operations
The move to the cloud didn’t just lower infrastructure costs—it changed the pace of business. It enabled continuous delivery, remote collaboration, and API-driven ecosystems. This era gave rise to DevOps and agile methodologies. Businesses became faster, leaner, and more iterative. Static planning cycles gave way to continuous innovation.
3. Predictive AI / ML: Optimization and Personalization
The last decade was defined by predictive AI—models that could forecast customer behavior, segment audiences, or detect anomalies. This wave improved decisioning, but it largely remained human-in-the-loop. Humans analyzed the outputs and acted on them. The competitive edge came from smarter recommendations, but action still required manual coordination.
4. Agentic AI: Outcome Delegation and Orchestration
We’re now entering the agentic era—where AI doesn’t just advise, it acts. Agentic AI can interpret intent, coordinate across systems, and execute workflows to achieve business outcomes. This marks a fundamental shift: from human-centric execution assisted by tools, to agent-led orchestration guided by human intent. Businesses must now rethink not just what gets done, but how and by whom.
In the past, each tech revolution was marked by a new way of doing things—whether that was empowering knowledge workers with PCs or enabling scalable operations through the cloud. Today, the next revolution is not just about enabling efficiencies through technology. As highlighted in Bessemer’s AI Agent Autonomy Scale Report, we’re seeing the rise of AI agents that act autonomously, enabling businesses to execute complex tasks without human oversight. This marks the transition from traditional tools to AI-driven decisioning and orchestration.
III. Operating Model Innovation Across Eras
Each revolution was won not by technology alone, but by companies that redefined how they operate. Salesforce didn’t just move CRM to the cloud—it redefined how software was delivered and monetized. Amazon didn’t just leverage e-commerce—it reimagined logistics and infrastructure to create a platform business model. These winners rewired their operating models in tandem with tech revolutions.
In contrast, today’s business environment is shaped by Volatility, Uncertainty, Complexity, and Ambiguity (VUCA)—factors that demand a different kind of organizational response. Rather than optimizing for control and efficiency, companies must prioritize learning, agility, and resilience. As highlighted by Bessemer Venture Partners, the shift from passive tools to autonomous AI agents is one of the key ways companies can gain adaptive capacity and competitive advantage in a VUCA world
The PACE World: Yesterday’s Operating Assumptions
Before VUCA, companies thrived in a PACE environment—Predictable, Accountable, Controlled, and Efficient. Business environments were stable enough to justify long-term planning cycles, clear hierarchies, and efficiency-first thinking. Strategy flowed from the top; execution followed linear playbooks. The focus was on doing known things, better.
The VUCA World: Today’s Operating Realities
In contrast, today’s business environment is shaped by Volatility, Uncertainty, Complexity, and Ambiguity (VUCA)—factors that demand a different kind of organizational response. Rather than optimizing for control and efficiency, companies must prioritize learning, agility, and resilience.
What this means for Traditional SaaS companies
From Product to Platform – B2B SaaS companies are shifting from just offering a product to building platforms that integrate with other tools and ecosystems. This shift allows for more scalable, value-adding opportunities.
Customer Expectations Have Skyrocketed – In the age of hyper-personalized experiences, B2B customers now expect on-demand, customized solutions that seamlessly integrate into their workflows. SaaS businesses must be agile and responsive to these demands or risk losing market share.
Data is No Longer Just for Reporting – In the past, SaaS companies treated data as a reporting tool. Today, data-driven decision-making is core to their growth strategies—AI can now leverage that data to optimize and automate operations.
The Speed of Change in SaaS – The SaaS business model was once seen as stable and predictable. Today, the velocity of change means that adaptability and innovation cycles are shorter than ever—those that can pivot and evolve quickly are the ones that win.
From Individual Performance to Systemic Excellence – In the SaaS world, we’ve moved past focusing only on the individual’s performance. Now, it’s about optimizing entire systems—using AI to orchestrate workflows and maximize efficiency across the board.
To fully understand the impact of these tech shifts, it's helpful to consider how business operations themselves have evolved. Over time, organizations have moved through four distinct operational mindsets:
1. Operate – Define the jobs to be done, train people to perform them, measure results, improve performance. This was the era of industrial optimization—clarity, control, and repeatability were the cornerstones.
2. Automate – Use software to make people more efficient or effective. CRMs, ERPs, and workflow tools didn’t change what was done, but how fast and how accurately it could be done.
3. Eliminate – Advanced software and AI began to fully remove tasks—and sometimes entire roles—from workflows. A classic example: Rocketfuel and the rise of programmatic advertising, which redefined media buying by removing manual planning in favor of machine-led execution.
4. Elevate – Agentic AI introduces a fourth operational leap. Instead of automating existing workflows, it invites businesses to rethink what workflows are necessary in the first place—and who (or what) should execute them. This is about elevating human work to its highest form: setting intent, designing systems, and orchestrating outcomes through agents.
Why Continuous Recalibration Matters
In today’s VUCA world—defined by Volatility, Uncertainty, Complexity, and Ambiguity—static plans and linear workflows break down quickly. Companies must shift from managing predictability to mastering adaptability. Agentic AI enables this shift by creating systems that don’t just automate tasks but can also recalibrate in response to real-time signals. It’s not enough to elevate work once; the work itself must evolve continuously. This is what sets the stage for the Agentic X Model.
IV. The X Model: From VUCA Response to Agentic Advantage
Revolutions in technology demand revolutions in how we operate. If Agentic AI is the engine, then the Agentic X Model is the chassis—the operating model that allows companies to deploy, scale, and govern AI agents in service of real outcomes. At its core there are 3 fundamental shifts that business leaders need to embrace in this new paradigm:
Orchestrators/AI scale
The Agentic X Model offers a framework for businesses to move beyond automation and into autonomous execution, as described in Bessemer's AI Agent Autonomy Scale. At higher levels of autonomy, AI systems are no longer just tools to aid decision-making—they become fully autonomous agents that execute and orchestrate business outcomes in real time. As companies move through the X Model, they progressively enable greater levels of AI autonomy, which allows for more efficient and scalable business operations.
Drawing from Jay Galbraith’s classic Star Model, we’ve detailed six factors that define the agentic enterprise in today’s AI-driven world. Bessemer’s AI Agent Autonomy Scale outlines the transition from L1 AI (assisting humans) to L5 AI (autonomously driving outcomes). The Agentic X Model mirrors this scale by evolving organizational structures and processes to accommodate AI at each level of autonomy.
To be clear, The Agentic X Model is not a maturity model—it’s an operating blueprint. Companies that align these six factors don’t just implement AI agents—they build businesses that learn faster, move smarter, and scale with intention.
V. Case Study – Landbase: GTM Reinvented with Agentic AI
Landbase is a fast-growing SaaS platform that provides go-to-market (GTM) automation for B2B companies. Its core product helps companies plan, launch, and manage outbound campaigns. Traditionally, these activities were done manually—by SDRs following playbooks, marketers running segmented email sequences, and ops teams stitching together data and systems.
If we were to apply the Agentic X Model to Landbase, this is how it might play out: By aligning its operating model across all six factors of the Agentic X Model, Landbase could turn agentic capability into competitive advantage.
Strategy – The leadership team defined “intent-based” growth objectives (e.g., "expand into healthcare with high-fit leads"). These goals cascade into agent directives rather than static campaign briefs.
Structure – GTM pods are structured around flow, not function. Each pod includes a marketer, a sales orchestrator, and agent systems that coordinate across data, content, and channels.
Processes – Agents manage end-to-end workflows: selecting targets, generating personalized content, executing multi-channel outreach, and escalating human follow-ups based on response thresholds.
People – Orchestrators at Landbase design and tune the agent workflows. Instead of dialing or writing copy, they act as coaches, workflow designers, and system integrators.
Rewards – Metrics focus on agent effectiveness and system-level conversion rates, not just individual rep activity. We can assume that the orchestrators are rewarded for improving autonomous funnel velocity.
Governance – Agentic GTM systems are fully observable. Dashboards track agent activity, decision trees, and escalations. Human-in-the-loop checks are embedded in risky steps like contract generation.
Results:
4x increase in pipeline per GTM pod
50% reduction in human touchpoints per opportunity
30% faster time-to-first-meeting
This thought experiment shows how the X Model rewires strategy, structure, and systems to drive outcomes. That’s the potential of operating agentically. The key lesson learned:
Don’t retrofit AI into existing playbooks—redesign the playbook itself.
VI. The CEOX.io Playbook
ceoX.io helps SaaS companies operationalize their Agentic X Model in three structured phases. Each phase is designed to help organizations progress from manual, role-based execution to fully orchestrated, agent-led workflows.
VII. Conclusion: Shaping the Future with the Agentic X Model
The rise of Agentic AI is not just another technological advancement—it’s a fundamental rethinking of how businesses must operate, compete, and scale. As Bessemer Venture Partners highlights, businesses that fail to adopt autonomous AI will fall behind. Those that embrace it can execute at scale, continuously recalibrate, and adapt faster than ever before.
The shift from a PACE world—Predictable, Accountable, Controlled, and Efficient—to a VUCA world—Volatile, Uncertain, Complex, and Ambiguous—demands more than just new tools. It requires organizations to rethink their operating models to thrive in an environment where speed, agility, and the ability to adapt to finite resources are the new norms.
The Agentic X Model provides a clear blueprint for businesses to harness the capabilities of Agentic AI. By aligning strategy, structure, processes, people, rewards, and governance with AI’s autonomous potential, businesses can unlock efficiencies, increase agility, and drive real-world results at scale.
Through our exploration of Agentic AI-first companies, we’ve seen how the Agentic X Model can transform even established workflows into more dynamic, responsive, and effective systems. This isn’t just about adopting AI; it’s about reengineering how organizations operate—from high-level strategy to daily execution.
As businesses face increasing complexity and volatility, adopting the Agentic X Model is no longer optional—it’s crucial to remain competitive in this AI-driven world. Companies that master this shift will not just survive—they will thrive, shaping the future of business in ways previously unimagined.