From Pilots to Enterprise AI: Verizon's Blueprint for a Future-Ready Workforce

From pilots to enterprise strategy

Most tech leaders have AI pilots running. Verizon was early to that game-embedding AI in customer service, retail, network operations, HR, and marketing. As one colleague puts it, "Our pilots are piloting."

Pilots don't equal strategy. To get full value, we moved from isolated initiatives to an enterprise approach and asked bigger questions: How will AI change work at every layer? Where will it speed innovation or free capacity? Where will it add complexity? How do we prepare leaders and teams to thrive?

An enterprise approach

AI isn't a simple upgrade; it changes how work gets done. We started with a role-by-role analysis across every job family.

We broke roles into tasks, then mapped where automation could replace manual work, where it could augment people, and where it could free resources. From there, we built an AI impact heatmap to identify the roles with the highest opportunity and to prioritize where to deploy.

The heatmap showed clear efficiency gains-and new demands. It surfaced skill gaps, new roles to staff, and organization design shifts we needed to plan for.

What the data suggests

External research points to major change. McKinsey projects significant task automation-translating to meaningful workforce shifts over time. See: Generative AI and the future of work.

Our experience: automation creates capacity, but it doesn't always mean a 1:1 headcount reduction. It raises the cognitive load of many jobs and increases the need for distinctly human strengths-creativity, ethical oversight, and nuanced decision-making.

That requires deliberate planning-to redeploy talent and reinvest in it.

Learning and leadership accelerate AI

Transformation at this scale stalls without learning and leadership. We embedded AI-focused development across the company-training more than 100,000 employees with targeted paths by function and specialty. For the 20,000 leaders we train each year, we added an AI overlay to core leadership curricula.

What used to be "nice to have" is now non-negotiable:

• Data fluency, digital acumen, ethical and societal stewardship: once reserved for technical roles, now essential across levels.
• Systems thinking, reasoning, and decision-making: more complex with AI in the loop.
• Agility, adaptability, and resilience: uncertainty is standard; iteration beats perfection.
• Intellectual curiosity: learn faster, act smarter, focus on what's next.

Human leadership still sets the ceiling. Inspiring others, building trust, exercising sound judgment, and leading with emotional intelligence matter more, not less.

Workforce investments and imperatives

Investment is moving fast. KPMG reports that many large U.S. enterprises plan substantial generative AI spending over the next year. See: KPMG's Generative AI enterprise adoption.

As roles evolve and new ones appear, continuous learning becomes a standing budget line, not a one-off program. That includes hiring and growing tech-savvy leaders in every function. Technology can't live only in IT-business leaders must know how AI works, how it should be used, and how it connects to performance.

Leaders don't have to be AI experts-but they do need to set direction, standards, and outcomes for AI use across their teams.

How to operationalize an enterprise AI workforce strategy

• Map work at the task level: build an AI impact heatmap; focus on roles with the highest upside and clear value cases.
• Set guardrails early: risk, compliance, security, bias testing, model monitoring, and human-in-the-loop checkpoints.
• Upskill at scale: tiered curricula for all employees plus role-based pathways for engineers, product, frontline, HR, finance, and sales; add an AI overlay to leadership programs.
• Redesign orgs: expect broader spans, fewer layers, new roles (prompt engineers, AI ops, model auditors), and strong internal mobility for redeployed talent.
• Update incentives: reward adoption quality, cycle-time reduction, accuracy, customer outcomes, and responsible use.
• Measure what matters: track capacity released, time to value, error rates, CSAT/NPS, revenue contribution, and employee engagement.
• Communicate clearly: set expectations on where AI helps, where people lead, and how careers advance.

A note on responsible adoption

Ethical and societal stewardship must be embedded, not bolted on. Bias testing, transparent data practices, and clear accountability keep trust intact with customers, employees, and regulators.

Looking ahead

As we move into the second half of 2025 and beyond, workforce planning needs intent. Embed AI into how you assess, train, lead, and grow talent. Let it inform how you organize teams and define leadership expectations.

Build with AI as well as for it. Use skills data and heatmaps to prioritize, and treat AI as both the focus of transformation and the tool that accelerates it.

Where to upskill next

If you're building role-based learning at scale, curated AI learning paths by job function can speed adoption. Explore options here: AI courses by job.