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Why ServiceNow’s AI Shift Reveals Work’s New Leverage Model

Paul Allen

24 Dec 2025 • 6 min read

AI adoption is outpacing previous tech waves—ChatGPT hit 54.6% adoption within three years, doubling internet and PC pace, according to the Federal Reserve Bank of St. Louis. ServiceNow recently elevated Jacqui Canney to chief AI enablement officer, merging people strategy with AI deployment to reshape workflows across departments. But this move isn’t just about expanding capacity; it signals a deeper rethinking of how work itself is structured to unlock compounding advantage. AI doesn’t align with silos—it demands workforce design built around new, cross-functional workflows.

Rethinking Work Means Rejecting Traditional Structures

The prevailing belief holds that AI pilots or programs can be run as isolated capacity boosts. That misses the central constraint: work is still organized by function, headcount, and department boundaries. ServiceNow’s approach shows these silos are obsolete because AI workflows cut across them. This contradicts conventional strategy and recalls lessons from the tech layoffs analysis at Think in Leverage where misaligned structure destroyed scale advantage.

In contrast, companies that redesign work based on AI-driven workflows—linking roles dynamically rather than by old reports—turn AI from an experiment into an integrated leverage engine. This structural shift repositions the principal constraint from staffing levels to workflow design, making execution more efficient and scalable.

From AI Specialists to Cross-Disciplinary Force Multipliers

A surge in AI-centric roles shows the magnitude of change: demand for AI engineers jumped 143.2% in 2024, prompt engineers rose 135.8%, and AI content creators increased 134.5%, per Autodesk. Yet, companies like Weber Shandwick hire beyond pure engineers, recruiting behavioral scientists and data analysts to integrate AI ethically and creatively. This talent diversification is a masterstroke in constraint repositioning: the key leverage is not just technical skills, but embedding AI fluency across disciplines.

Unlike competitors who stick to legacy hires or narrowly technical profiles, these firms build hybrid teams that make AI a force multiplier for insight and creativity. See parallels in sales workflows where AI learns unstructured data—politics, motivations—as at Moody’s, freeing reps to focus on relationship management, a mechanism discussed in Think in Leverage.

Mandating AI Use Forces a New Performance Culture

Where many companies leave AI adoption voluntary, Microsoft, Shopify, and Coinbase now require it. Meta plans to tie performance reviews directly to AI-driven impact. This isn’t just a productivity hack; it redefines the performance constraint. Employees become data scientists of their own output, with AI metrics integrated deeply into compensation.

This shift will force operational changes echoing the scrutiny documented in our coverage of tech selloffs (Think in Leverage). The leverage point moves from individual effort to continuous, AI-enabled optimization, accelerating feedback loops and value capture.

Who Benefits From Rethinking AI as Embedded Workflow Design?

The biggest constraint flipped is organizational design itself. Companies that still treat AI as an isolated program will lag those fundamentally redesigning work for AI integration, as per BCG’s research showing only 5% restructuring see significant revenue gains. This gives early movers a durable head start that scales without linear investment.

Operators should watch sectors pulling ahead, like financial services and healthcare, where workforce redesign facilitates rapid AI adoption. Regions and companies that embrace cross-functional AI workflows will unlock exponential leverage.

“Design your work for AI, not AI for work” encapsulates the new imperative—build systems that free humans for uniquely human work while AI runs the routine and complex pattern recognition under the hood.

As companies adapt to the integration of AI into workflows, platforms like Blackbox AI can streamline the development process and enhance coding efficiency. By leveraging AI for code generation, development teams can focus on optimizing workflows and driving innovation, embodying the very principles discussed in this article. Learn more about Blackbox AI →

Full Transparency: Some links in this article are affiliate partnerships. If you find value in the tools we recommend and decide to try them, we may earn a commission at no extra cost to you. We only recommend tools that align with the strategic thinking we share here. Think of it as supporting independent business analysis while discovering leverage in your own operations.

Frequently Asked Questions

How fast is AI adoption compared to previous technology waves?

AI adoption is outpacing past technology waves, with ChatGPT reaching 54.6% adoption within three years—doubling the pace of both the internet and PCs, according to the Federal Reserve Bank of St. Louis.

What organizational changes is ServiceNow implementing with AI?

ServiceNow elevated Jacqui Canney to chief AI enablement officer to merge people strategy with AI deployment, restructuring workflows across departments to support cross-functional collaboration rather than traditional silos.

Why is workforce redesign critical for AI integration?

Workforce redesign based on AI-driven workflows breaks down functional silos, enabling roles to link dynamically. This shift moves the constraint from staffing levels to workflow design, boosting efficiency and scalability.

Demand for AI engineers rose 143.2%, prompt engineers 135.8%, and AI content creators 134.5% in 2024, demonstrating a significant talent surge to support AI innovation, according to Autodesk.

How are companies beyond engineering integrating AI talent?

Companies like Weber Shandwick hire behavioral scientists and data analysts to embed AI fluency ethically and creatively across disciplines, creating hybrid teams that multiply insights and creativity.

What impact does mandating AI use have on company culture?

Companies such as Microsoft, Shopify, and Coinbase require AI use, with Meta tying AI-driven impact directly to performance reviews. This fosters a new culture where employees optimize their output continuously using AI metrics.

Which sectors benefit most from rethinking AI as embedded workflow design?

Financial services and healthcare lead in workforce redesign facilitating rapid AI adoption, gaining durable competitive advantages by integrating AI workflows across functions.

What does 'Design your work for AI, not AI for work' mean?

This principle emphasizes building systems where AI handles routine and complex pattern recognition, freeing humans to focus on uniquely human tasks, ultimately enhancing leverage and productivity.