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Key takeaways:
- AI value is uneven—focus where it matters most. Not every use case delivers the same impact. The strongest returns come from domains where AI can reshape decisions, workflows, and operating leverage.
- Broad mandates dilute impact. Spreading AI investment across too many initiatives drives activity—but often limits measurable outcomes. Concentrated effort helps unlock meaningful enterprise value.
- A bimodal strategy drives results. Leading organizations combine targeted reinvention in high-value areas with scaled enablement across the enterprise—aligning investment intensity to where it creates the most value.
A Fortune 100 technology company had built strong foundational AI capabilities but wasn't seeing meaningful returns after years of investment. AI technologies have matured substantially in the past five years, and the company was an early adopter, deploying AI across the board in many functions. Why wasn’t AI delivering more of an impact?
After concentrating effort into a high-value R&D domain, the company redesigned testing and knowledge management workflows using agentic AI, moving away from legacy, labor-intensive processes. The result was a significant improvement in productivity and development efficiency.
Another company, a multinational food and beverage company, was also struggling to generate returns from AI, despite making broad investments across the organization.
It shifted gears, applying more concentrated efforts to using AI within its sustainability function. By rebuilding its scenario planning and ESG reporting to incorporate an AI-enabled operating model with continuous learning, it saw a dramatic transformation. Sustainability shifted from being a mere reporting requirement to one of this company’s strategic data-driven capabilities.
Both companies had discovered a structural gap in the enterprise impacts of AI: Its value is inherently uneven. This unevenness is not unique to these two. To realize meaningful returns, all enterprises should focus their AI efforts more strategically.
AI value concentrates in domains where decision quality, workflow redesign, and operating leverage intersect. However, many organizations continue to pursue enterprise-wide transformation models, which spread capital, talent, and leadership attention across a wide range of initiatives. They are generating activity—pilots, proofs of concept, and localized gains—but not delivering measurable enterprise outcomes.
This approach can dilute the impact of AI. It can slow down and starve high-value opportunities, while over-investing in lower-value use cases.
The solution to dilution: A bimodal AI strategy
To counteract this diffusion, leading organizations are adopting a bimodal transformation strategy:
- Mode 1: concentrated reinvention in selected, high-impact domains
- Mode 2: scaled enterprise enablement across the broader organization.
That doesn't necessarily mean more AI investment. It does, however, require a more deliberate allocation of investment intensity.
The results are dramatic.
PwC has found that fewer than 5% of S&P 500 companies are adopting a bimodal AI transformation strategy. But those that do see twice the revenue of their peers, and their total shareholder returns are outpacing their peers by 5.4 percentage points (a 30% increase in TSR).
AI transformation requires differentiated investment
AI creates value unevenly across the enterprise. Some domains—such as pricing, underwriting, supply chain optimization, and customer personalization—offer outsized potential to drive business results through growth, margin expansion, or risk mitigation. Others deliver incremental efficiency gains without fundamentally changing the company’s competitive position. Both are important.
At the same time, the pace of AI innovation requires organizations to operate at different speeds simultaneously. High-value domains demand rapid, iterative experimentation and reward those that completely reinvent workflows to take full advantage of the new technologies. Lower-value areas require disciplined execution, governance, and scale to pay off.
Despite this, many organizations default to a single-speed model, applying the same funding processes, governance standards, and execution cadence across various initiatives.
The consequences are often predictable:
- Controls designed for incremental experimentation slow down high-value initiatives
- Companies distribute capital too broadly, rather than investing more where the payoffs are bigger
- Too many initiatives can require executive attention, creating fragmentation
- Pilot activity increases, but enterprise-class results never appear
- Experimentation consumes everyone’s capacity, and people lose sight of delivering real value
The issue is not capability, it is the misalignment of investment intensity with value. Sustained performance depends on making explicit strategic choices:
- Seek to lead in domains where AI can create differentiated advantages and warrants concentrated reinvention
- Lag in domains where AI drives productivity and consistency but does not create differentiation, applying disciplined enablement rather than concentrated investment
- Exit or deprioritize where investment no longer delivers the value you need to see from it
Without these distinctions, organizations often overinvest in low-value areas while underinvesting where transformation truly matters. They treat AI as a collection of experiments rather than a managed portfolio with explicit capital allocation, return thresholds, and active reallocation of resources. The results: fragmented ownership, limited impact, and lack of accountability.
Two distinct modes, one integrated strategy
Sustained AI transformation requires two modes operating in parallel, each with a different purpose, cadence, and level of intensity.
Mode 1: Concentrated reinvention
Concentrated reinvention focuses on a small number of high-value domains where AI can fundamentally reshape how work gets done. This requires organizations to rethink workflows end-to-end, prioritizing complex, harder-to-execute opportunities that unlock disproportionate value.
- Direct linkage to business outcomes, such as revenue growth, margin expansion, or risk reduction
- Concentrated capital allocation, rather than incremental funding
- Dedicated, cross-functional teams spanning business, data, technology, and risk
- Explicit executive sponsorship with clear accountability for outcomes
- Authority to redesign workflows end-to-end, often enabled by agentic AI capabilities
- Rapid iteration cycles tied to measurable financial KPIs
This is how organizations build durable leadership through proprietary data, differentiated workflows, and faster learning cycles.
Execution in these reinvention efforts typically follows a studio or pod-based model, with clear backlog prioritization, dedicated funding, and defined time-to-impact targets.
Mode 2: Enterprise enablement
Enterprise enablement provides the foundation that enables AI to scale across the enterprise. Its focus includes:
- Standard AI platforms and tooling
- Governance frameworks for model risk, compliance, and security
- Data standards and architecture
- Workforce enablement and structured training programs
- Scaled rollout of prioritized use cases across business units
- Controlled incubation or “sandbox” environments that allow employees to experiment securely with new and emerging AI capabilities
A large US financial services organization demonstrates this approach. By defining role-based AI personas and building structured learning pathways, it scaled AI fluency from a small pilot group to tens of thousands of employees. By doing that, it established a model for rolling out future AI capabilities across the entire enterprise.
This mode emphasizes consistency and scale. The goal is to deploy AI responsibly and efficiently, while enabling experimentation within defined guardrails.
Why both modes should operate together
Organizations often treat these modes as sequential: We’ll reinvent the foundations first, then pursue transformation and enablement across the enterprise once we figure out what works. In practice, a sequential approach delays value realization and slows momentum.
Leading organizations use both reinvention and enablement modes simultaneously. When aligned effectively:
- Reinvention drives rapid value creation in the most important domains
- Enablement scales capabilities and embeds them across the enterprise
- The company can apply governance consistently without constraining high-impact initiatives
- Learning compounds and diffuses throughout the organization over time
There is also a critical funding dynamic. Efficiency gains from enablement—through automation and productivity improvements—can free up capacity and resources. However, without deliberate portfolio management, those gains often get overlooked or absorbed into other priorities. The bimodal model makes these benefits visible and actionable: capacity and resources freed up through enterprise enablement (Mode 2) are intentionally redirected into priority use cases (Mode 1), and vice versa. This helps align talent, capacity, and investment to where they drive the most value.
Bimodal leaders actively reallocate capital and talent from lower-value activities and legacy programs into high-intensity reinvention domains. This is what converts incremental gains into sustained competitive advantage.
Scaling execution: The role of the AI Studio
Choosing where to apply intensity is only the first step. Delivering results requires an operating model that translates strategy into execution at scale.
PwC’s AI Studio model provides that engine. It is a structured operating model that helps organizations move from strategy to scaled execution by integrating governance, talent, technology, and delivery. The model is flexible—tailored to different organizational contexts rather than applied as a one-size-fits-all approach.
Within a bimodal strategy, bimodal defines the what and where (reinvention vs. enablement); AI Studio defines the how—translating priorities into coordinated execution across both modes.
- Centralized governance aligns executive sponsorship, funding, and decision-making
- Focused resource allocation concentrates investment on reinvention while scaling enablement capabilities
- Cross-functional pods unite business, engineering, and data to accelerate delivery
- Shared technology foundations enable reuse, interoperability, and speed
- Embedded responsible AI controls maintain trust without slowing execution
- ROI discipline ties initiatives to measurable outcomes from the outset
AI Studio supports concentrated reinvention by driving priority reinvention use cases, and enterprise enablement by scaling shared capabilities and governance. It also supports active prioritization across both, keeping efforts aligned rather than competing for capacity.
As a repeatable transformation engine, AI Studio enables organizations to move from diagnostics to scaled deployment and continuous optimization. Organizations that move beyond decentralized pilots to a structured model like AI Studio are better positioned to convert early momentum into sustained advantage.
The path forward for enterprise leaders
The question for leadership teams is no longer whether to invest in AI. It is whether they are investing with discipline.
This requires a shift:
- From broad experimentation to targeted value creation
- From uniform execution to differentiated intensity
- From isolated initiatives to active portfolio management
- From technology-led efforts to business outcome ownership
Investing in AI with discipline also requires making explicit choices: where to concentrate resources, where to scale efficiently, and where to stop investing.
Organizations that take this approach have already begun to separate from the pack, with sustained outperformance observed over the 2022–2025 period. They are not necessarily spending more on AI, but they are extracting more value from each investment through deliberate allocation of capital, talent, and leadership attention. That discipline shows up in both topline revenue growth and total shareholder returns.
AI’s next phase will not be defined by access to technology, but by the ability to deploy it with precision.
Those that align investment intensity to value cannot only accelerate returns, they can help redefine how their organizations compete.
Focus your AI investments where value concentrates
Prioritize high-impact domains to accelerate measurable outcomes from AI
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