No PriorsScaling Global Organizations in the Age of AI with ServiceNow Chairman and CEO Bill McDermott
CHAPTERS
Cold open: Why platforms won’t be replaced by LMs (cost, context, and trust)
McDermott opens with a rebuttal to the “SaaSpocalypse” idea: replacing enterprise platforms with language models is far more expensive and less reliable. He emphasizes total cost (rebuild + human time + GPU/token economics) and the business reality that software errors are less forgivable than human ones.
- •Replacing an enterprise platform carries massive switching and opportunity costs
- •Replicating a simple platform app with an LM can be ~10x more expensive
- •Token/GPU economics materially change the cost structure vs. SaaS
- •LLMs are non-deterministic and lack deep enterprise context by default
- •Businesses tolerate human mistakes more than software mistakes
Bill McDermott’s origin story: buying a deli at 16 and learning “customer first”
McDermott explains that buying a deli wasn’t a stunt—it consolidated multiple part-time jobs into one. The experience taught him durable lessons about knowing your customers, delivering dignity and consistency, and building EQ through constant human interaction.
- •Bought the deli via a note: make payments or lose everything—high accountability
- •Success came from obsessing over customer needs and repeat business
- •Identified distinct customer segments (workers, seniors, kids) and served each differently
- •Creative tactics (delivery, video games) drove foot traffic and loyalty
- •High-volume customer interaction built EQ and people skills
Getting the deal done with no cash: relationships, consignment, and earning a “shot”
He details the practical mechanics of acquiring and operating the deli despite having no money. Supplier relationships enabled initial inventory on consignment, reinforcing a lifelong theme: opportunities matter most, and trust is built by paying people back and executing.
- •The seller wanted out; the lease risk was high—only a naïve buyer would pay for it
- •McDermott negotiated a performance-based note with strict consequences
- •Leveraged supplier relationships to stock shelves on consignment
- •Viewed the first chance to operate as a gift to maximize
- •Core leadership mindset: people thrive when given real opportunity
Leadership in the AI era: speed is permanent, but human connection matters more
Responding to the pace of AI-driven change, McDermott argues leaders must accept accelerating tempo as the new baseline. He frames turbulence as a source of inspiration and insists AI should amplify human ambition—not replace the human bonds that make teams effective.
- •Change is frightening, but it can be energizing if leaders lean in
- •“It’ll never move this slow again” — adapting to continuous acceleration
- •Leadership remains rooted in human connection and trust
- •Risk of losing human power due to digital-first habits
- •AI’s purpose: serve people and expand human ambition
The Xerox hiring story: agency, urgency, and someone breaking policy for talent
McDermott recounts his pivotal Xerox interview, where he asserted agency and urgency to secure the job immediately. The story underscores mentorship, decisive moments, and the responsibility leaders have to recognize potential and take calculated risks on people.
- •Approached the interview as survival and destiny, not a formality
- •Showed initiative by ensuring the decision-maker knew he was waiting
- •Boldly requested an immediate hire to keep a promise to his father
- •Hiring manager broke policy—later acknowledged it as unique
- •Lesson: leaders must notice uniqueness and invest in people
Can agency be taught? Coaching confidence through real practice
McDermott argues agency isn’t purely innate; it can be developed through structured practice and real-world reps. He credits work—especially people-facing work—for building confidence and advocates training, coaching, and certification to build presence and leadership capability.
- •He describes himself as once shy; work helped him “come into his own”
- •Agency grows from controlling effort, discipline, and commitment
- •People skills come from listening, caring, and being present
- •Modern tools can reduce human connection unless counterbalanced
- •Organizations should simulate/train/coach interpersonal leadership skills
Seeing AI disruption as opportunity: clarifying LMs vs workflow platforms
To reduce confusion, ServiceNow created a blueprint for “agentic business” and teaches the distinction between LMs that recommend and platforms that execute. McDermott’s key framing: ‘AI thinks, workflow acts’—closing cases across departments requires context, data, and orchestration.
- •White paper used to educate executives on agentic business concepts
- •LMs provide guidance quickly but may not close enterprise ‘cases’ end-to-end
- •Workflow platforms orchestrate data/context across HR, finance, legal, compliance, etc.
- •“AI thinks, workflow acts” as a practical enterprise operating principle
- •Combining platforms + AI creates better outcomes than replacing platforms
ServiceNow as the ‘AI control tower’: integrating clouds, models, and systems of record
McDermott positions ServiceNow as connective tissue across hyperscalers, language models, and enterprise systems of record. He extends this vision into cybersecurity and operational technology, arguing that unified visibility and automated workflows are essential to run an agentic enterprise securely.
- •ServiceNow aims to be the business reinvention ‘control tower’
- •Integrates hyperscalers, LMs, and systems of record to enable agentic operations
- •Security expansion framed by cybercrime’s economic scale and urgency
- •OT visibility (devices, networks, manufacturing, medical devices) is increasingly critical
- •Mentions acquisitions (Moveworks, identity, Armis) and fast integration capability
Which enterprise SaaS gets disrupted? Departmental apps vs cross-enterprise platforms
He predicts AI pressure will hit narrow, lower-priority departmental tools more than horizontal, mission-critical platforms with deep integration and switching costs. ServiceNow’s moat, he argues, comes from end-to-end workflows across the enterprise and massive scale in live operations.
- •Disruption risk highest for single-function, lower-value departmental vendors
- •Systems of record and cross-department platforms are harder to replicate
- •Integration breadth becomes a defensibility advantage
- •ServiceNow scale cited: tens of billions of workflows and trillions of transactions
- •Mission-critical reliability is both moat and responsibility
What makes a platform business: switching costs, integration gravity, and data fabric
McDermott defines platforms by their mission-critical role, deep customization history, and integration centrality. For ServiceNow, the platform value comes from orchestrating work across hundreds of systems and enabling rapid process changes using AI, including zero-copy data interactions.
- •Platforms run ‘needle-mover’ processes (e.g., global finance; enterprise workflows)
- •High switching costs due to customization, complexity, and embedded processes
- •ServiceNow integrates with hundreds of significant systems of record
- •Zero-copy approach reduces risk while enabling process completion
- •AI enables instant process redesign, visibility, and impact mapping
AI and implementation speed: going live in under 30 days and compounding SI capacity
AI accelerates implementation and customization, which McDermott views as an advantage for ServiceNow rather than a threat. Faster deployments increase customer ROI and allow systems integrators to complete more projects, even if each project takes less time.
- •Enterprises no longer tolerate multi-year transformation projects
- •AI-driven autonomy enables major customers to go live in <30 days
- •Modular approach delivers quick wins and then expansion
- •Faster time-to-value improves ROI and adoption momentum
- •SIs may shift from fewer long projects to more rapid engagements
Agents reshaping work: fewer net-new hires, higher skill bar, and human roles that endure
McDermott forecasts that agents will absorb much of the operational workload, reducing the need for headcount growth even as revenues rise. Humans will concentrate on innovation, judgment, and trust-based customer relationships, while routine tasks shift to software agents.
- •Net-new headcount growth expected to slow as productivity rises
- •Agents take on supporting-function workload (finance, HR, services)
- •Humans remain essential for engineering innovation and relationship trust
- •Skill differentiation becomes critical—if an agent can do it better, economics wins
- •Customer service example: large majority of cases handled by agents
Measuring whether AI is ‘working’: adoption of the control tower and growth in agent ‘assists’
Beyond revenue growth, McDermott looks for boundary expansion across product areas and rising consumption of agent-driven assistance. He ties success to becoming the enterprise’s “central nervous system” and to measurable increases in how much AI helps humans execute work.
- •Signals include adoption across IT, employee experience, CRM, creators, security
- •Focus on being the agentic front door and identity manager (human + non-human)
- •Success depends on connecting all enterprise nodes through the workflow fabric
- •Tracks the volume and trend of agent ‘assists’ delivered to users
- •Consumption dynamics can amplify shareholder value once usage scales
Enterprise AI readiness and changing buyer conversations: from experiments to prescriptions
McDermott describes uneven maturity: many organizations are still experimenting, while early movers push AI into core operations and business model changes. Customer conversations have become more prescriptive and impatient—buyers want fast, specific transformation plans grounded in deep business understanding.
- •Many firms remain in experimentation; a minority have moved to production at scale
- •Readiness varies by geography (US faster) and industry (finance aggressive, public sector slower)
- •Early movers focus on business model change, productivity, and headcount ROI
- •Post-COVID hiring layers drive renewed pressure for efficiency
- •Customer expectations: brief ‘dance,’ rapid execution, predictable outcomes, AI included
Curiosity beyond ServiceNow and the CEO operating rhythm: time zones, customers, and frontline listening
McDermott shares what fascinates him in tech—improving the human condition and enabling new possibilities across industries and even space. He also outlines his CEO cadence: running a global schedule across regions, staying grounded with customers, and regularly speaking with quota-carrying reps to stay close to reality.
- •Motivated by technology’s potential to improve the world (environment, space, new channels)
- •Leadership philosophy: imagine what doesn’t exist yet and pursue it
- •Daily rhythm follows global time zones: Europe → US → Asia
- •Prioritizes customer time to stay grounded in real needs
- •Actively listens to frontline sellers (dozens of rep conversations per month)
