From pilot to profit: The real path to scalable, ROI-positive AI

This article is part of VentureBeat’s special issue, “The Real Cost of AI: Performance, Efficiency and ROI at Scale.” Read more from this special issue.

Three years after ChatGPT launched the generative AI era, most enterprises remain trapped in pilot purgatory. Despite billions in AI investments, the majority of corporate AI initiatives never escape the proof-of-concept phase, let alone generate measurable returns.

But a select group of Fortune 500 companies has cracked the code. Walmart, JPMorgan Chase, Novartis, General Electric, McKinsey, Uber and others have systematically moved AI from experimental “innovation theater” to production-grade systems delivering substantial ROI—in some cases, generating over $1 billion in annual business value.

Their success isn’t accidental. It’s the result of deliberate governance models, disciplined budgeting strategies, and fundamental cultural shifts that transform how organizations approach AI deployment. This isn’t about having the best algorithms or the most data scientists. It’s about building the institutional machinery that turns AI experiments into scalable business assets.

The pilot trap: Why most AI initiatives fail to scale

The statistics are sobering. Industry research shows that 85% of AI projects never make it to production, and of those that do, fewer than half generate meaningful business value. The problem isn’t technical—it’s organizational. Companies treat AI as a science experiment rather than a business capability.

“AI is already cutting some product-development cycles by about 40 percent, letting companies ship and decide faster than ever,” said Amy Hsuan, chief customer and revenue officer at Mixpanel. “But only for companies that have moved beyond pilots to systematic deployment.”

The failure patterns are predictable: scattered initiatives across business units, unclear success metrics, insufficient data infrastructure, and—most critically—the absence of governance frameworks that can manage AI at enterprise scale.

The production imperative: A framework for systematic AI deployment

The companies that have succeeded share a remarkably consistent playbook. Through interviews with executives and analysis of their AI operations, eight critical elements emerge that distinguish pilot-phase experimentation from production-ready AI systems:

1. Executive mandate and strategic alignment

Every successful AI transformation begins with unambiguous leadership commitment. This isn’t ceremonial sponsorship—it’s active governance that ties every AI initiative to specific business outcomes.

At Walmart, CEO Doug McMillon established five clear objectives for AI projects: enhancing customer experience, improving operations, accelerating decision-making, optimizing supply chains, and driving innovation. No AI project gets funded without mapping to these strategic pillars.

“We don’t want to just throw spaghetti at the wall,” explained Anshu Bhardwaj, Walmart’s SVP of Global Tech. “Every AI project must target a specific business problem with measurable impact.”

JPMorgan Chase’s Jamie Dimon takes a similar approach, calling AI “critical to our future success” while backing that rhetoric with concrete resource allocation. The bank has over 300 AI use cases in production precisely because leadership established clear governance from day one.

Practical implementation: Create an AI steering committee with C-level representation. Establish 3-5 strategic objectives for AI initiatives. Require every AI project to demonstrate clear alignment with these objectives before funding approval.

2. Platform-first infrastructure strategy

The companies that scale AI successfully don’t build point solutions—they build platforms. This architectural decision becomes the foundation for everything else.

Walmart’s “Element” platform exemplifies this approach. Rather than allowing teams to build isolated AI applications, Element provides a unified machine learning infrastructure with built-in governance, compliance, security, and ethical safeguards. This allows teams to plug in new AI capabilities quickly while maintaining enterprise-grade controls.

“We were among the earliest companies to build generative AI into our infrastructure,” Bhardwaj noted. “Element gives us a safe playground where developers across the company can experiment with AI use cases while maintaining all our governance requirements.”

JPMorgan Chase invested $2+ billion in cloud infrastructure specifically to support AI workloads, migrating 38% of applications to cloud environments optimized for machine learning. This wasn’t just about compute power—it was about creating an architecture that could handle AI at scale.

Practical implementation: Invest in a centralized ML platform before scaling individual use cases. Include governance, monitoring, and compliance capabilities from day one. Budget 2-3x your initial estimates for infrastructure—scaling AI requires substantial computational resources.

3. Disciplined use case selection and portfolio management

The most successful companies resist the temptation to pursue flashy AI applications in favor of high-ROI use cases with clear business metrics.

Novartis CEO Vas Narasimhan was candid about early AI challenges: “There’s a lot of talk and very little in terms of actual delivery of impact in pharma AI.” To address this, Novartis focused on specific problems where AI could deliver immediate value: clinical trial operations, financial forecasting, and sales optimization.

The results were dramatic. AI monitoring of clinical trials improved on-time enrollment and reduced costly delays. AI-based financial forecasting outperformed human predictions for product sales and cash flow. “AI does a great job predicting our free cash flow,” Narasimhan said. “It does better than our internal people because it doesn’t have the biases.”

Practical implementation: Maintain an AI portfolio with no more than 5-7 active use cases initially. Prioritize problems that already cost (or could generate) seven figures annually. Establish clear success metrics and kill criteria for each initiative.

4. Cross-functional AI operating model

Traditional IT project structures break down when deploying AI at scale. Successful companies create “AI pods”—cross-functional teams that combine domain expertise, data engineering, MLOps, and risk management.

McKinsey’s development of “Lilli,” its proprietary AI research assistant, illustrates this approach. The project started with three people but quickly expanded to over 70 experts across legal, cybersecurity, risk management, HR, and technology.

“The technology was the easy part,” said Phil Hudelson, the partner overseeing platform development. “The biggest challenge was to move quickly while bringing the right people to the table so that we could make this work throughout the firm.”

This cross-functional approach ensured Lilli met strict data privacy standards, maintained client confidentiality, and could scale to thousands of consultants across 70 countries.

Practical implementation: Form AI pods with 5-8 people representing business, technology, risk, and compliance functions. Give each pod dedicated budget and executive sponsorship. Establish shared platforms and tools to prevent reinventing solutions across pods.

5. Risk management and ethical AI frameworks

Enterprise AI deployment requires sophisticated risk management that goes far beyond model accuracy. The companies that scale successfully build governance frameworks that manage model drift, bias detection, regulatory compliance, and ethical considerations.

JPMorgan Chase established rigorous model validation processes given its regulated environment. The bank developed proprietary AI platforms (including IndexGPT and LLM Suite) rather than relying on public AI services that might pose data privacy risks.

Walmart implements continuous model monitoring, testing for drift by comparing current AI outputs to baseline performance. They run A/B tests on AI-driven features and gather human feedback to ensure AI utility and precision remain high.

Practical implementation: Establish an AI risk committee with representation from legal, compliance, and business units. Implement automated model monitoring for drift, bias, and performance degradation. Create human-in-the-loop review processes for high-stakes decisions.

6. Systematic workforce development and change management

Perhaps the most underestimated aspect of AI scaling is organizational change management. Every successful company invested heavily in workforce development and cultural transformation.

JPMorgan Chase increased employee training hours by 500% from 2019 to 2023, with much of that focused on AI and technology upskilling. The bank now provides prompt engineering training to all new hires.

Novartis enrolled over 30,000 employees—more than one-third of its workforce—in digital skills programs ranging from data science basics to AI ethics within six months of launching the initiative.

“This year, everyone coming in here will have prompt engineering training to get them ready for the AI of the future,” said Mary Callahan Erdoes, CEO of JPMorgan’s asset & wealth management division.

Practical implementation: Allocate 15-20% of AI budgets to training and change management. Create AI literacy programs for all employees, not just technical staff. Establish internal AI communities of practice to share learnings and best practices.

7. Rigorous ROI measurement and portfolio optimization

The companies that scale AI successfully treat it like any other business investment—with rigorous measurement, clear KPIs, and regular portfolio reviews.

Walmart uses internal ROI calculations and sets specific metric checkpoints for teams. If an AI project isn’t hitting its targets, they course-correct or halt it. This disciplined approach has enabled Walmart to scale successful pilots into hundreds of production AI deployments.

JPMorgan Chase measures AI initiatives against specific business metrics. The bank’s AI-driven improvements contributed to an estimated $220 million in incremental revenue in one year, with the firm on track to deliver over $1 billion in business value from AI annually.

Practical implementation: Establish baseline KPIs for every AI initiative before deployment. Implement A/B testing frameworks to measure AI impact against control groups. Conduct quarterly portfolio reviews to reallocate resources from underperforming to high-impact initiatives.

8. Iterative scaling and platform evolution

The most successful companies don’t try to scale everything at once. They follow an iterative approach: prove value in one area, extract learnings, and systematically expand to new use cases.

GE’s journey with predictive maintenance illustrates this approach. The company started with specific equipment types (wind turbines, medical scanners) where AI could prevent costly failures. After proving ROI—achieving “zero unanticipated failures and no downtime” on certain equipment—GE expanded the approach across its industrial portfolio.

This iterative scaling allowed GE to refine its AI governance, improve its data infrastructure, and build organizational confidence in AI-driven decision making.

Practical implementation: Plan for 2-3 scaling waves over 18-24 months. Use early deployments to refine governance processes and technical infrastructure. Document learnings and best practices to accelerate subsequent deployments.

The economics of enterprise AI: real costs and returns

The financial reality of scaling AI is more complex than most organizations anticipate. The companies that succeed budget for the full cost of enterprise AI deployment, not just the technology components.

Infrastructure and platform costs

JPMorgan Chase’s $2+ billion investment in cloud infrastructure represents roughly 13% of its $15 billion annual technology budget. Walmart’s multi-year investment in its Element platform required similar scale—though exact figures aren’t disclosed, industry estimates suggest $500 million to $1 billion for a platform supporting enterprise-wide AI deployment.

These investments pay for themselves through operational efficiency and new revenue opportunities. Walmart’s AI-driven catalog improvements contributed to 21% e-commerce sales growth. JPMorgan’s AI initiatives generate an estimated $1-1.5 billion in annual value through efficiency gains and improved services.

Talent and training investments

The human capital requirements for enterprise AI are substantial. JPMorgan Chase employs over 1,000 people in data management, including 900+ data scientists and 600+ ML engineers. Novartis invested in digital skills training for over 30,000 employees.

But these investments generate measurable returns. JPMorgan’s AI tools save analysts 2-4 hours daily on routine work. McKinsey consultants using the firm’s Lilli AI platform report 20% time savings in research and preparation tasks.

Governance and risk management costs

Often overlooked in AI budgeting are the substantial costs of governance, risk management, and compliance. These typically represent 20-30% of total AI program costs but are essential for enterprise deployment.

McKinsey’s Lilli platform required 70+ experts across legal, cybersecurity, risk management, and HR to ensure enterprise readiness. JPMorgan’s AI governance includes dedicated model validation teams and continuous monitoring systems.

Cultural transformation: The hidden success factor

The most successful AI deployments are fundamentally about organizational transformation, not just technology implementation. The companies that scale AI successfully undergo cultural shifts that embed data-driven decision making into their operational DNA.

From intuition to evidence

Uber Carshare’s transformation illustrates this cultural shift. The company moved from intuition-driven growth strategies to data-driven optimization after implementing unified analytics. This revealed critical friction points in their customer journey—such as forcing new users to wait for account approval before booking—that were dramatically reducing conversions.

By addressing these data-revealed issues, Uber Carshare achieved 600+ additional new customer signups per month and a 29% increase in app-based bookings. The cultural shift from “we think” to “we know” based on data analysis was as important as the technical capabilities.

Embedding AI literacy across the organization

The most successful companies don’t treat AI as a specialist capability confined to data science teams. They embed AI literacy throughout the organization.

Novartis adopted an “unbossed” management philosophy, cutting bureaucracy to empower teams to innovate with AI tools. The company’s broad engagement—30,000+ employees enrolled in digital skills programs—ensured AI wasn’t just understood by a few experts but trusted by managers across the company.

Managing the human-AI partnership

Rather than viewing AI as a replacement for human expertise, successful companies frame it as augmentation. JPMorgan’s Jamie Dimon has repeatedly emphasized that AI will “augment and empower employees,” not make them redundant.

This narrative, backed by retraining commitments, reduces resistance and encourages experimentation. GE ingrained AI into its engineering teams by upskilling domain engineers in analytics tools and forming cross-functional teams where data scientists worked directly with turbine experts.

Governance models that scale

The difference between pilot-phase AI and production-grade AI systems lies largely in governance. The companies that successfully scale AI have developed sophisticated governance frameworks that manage risk while enabling innovation.

Centralized platforms with distributed innovation

Walmart’s Element platform exemplifies the “centralized platform, distributed innovation” model. The platform provides unified infrastructure, governance, and compliance capabilities while allowing individual teams to develop and deploy AI applications rapidly.

This approach gives business units the flexibility to innovate while maintaining enterprise-grade controls. Teams can experiment with new AI use cases without rebuilding security, compliance, and monitoring capabilities from scratch.

Risk-adjusted approval processes

JPMorgan Chase implements risk-adjusted governance where AI applications receive different levels of scrutiny based on their potential impact. Customer-facing AI systems undergo more rigorous validation than internal analytical tools.

This tiered approach prevents governance from becoming a bottleneck while ensuring appropriate oversight for high-risk applications. The bank can deploy low-risk AI applications quickly while maintaining strict controls where needed.

Continuous performance monitoring

All successful AI deployments include continuous monitoring that goes beyond technical performance to include business impact, ethical considerations, and regulatory compliance.

Novartis implements continuous monitoring of its AI systems, tracking not just model accuracy but business outcomes like trial enrollment rates and forecasting precision. This enables rapid course correction when AI systems underperform or market conditions change.

Budget allocation strategies that work

The companies that successfully scale AI have developed sophisticated budgeting approaches that account for the full lifecycle costs of enterprise AI deployment.

Platform-first investment strategy

Rather than funding individual AI projects, successful companies invest in platforms that support multiple use cases. Walmart’s Element platform required substantial upfront investment but enables rapid deployment of new AI applications with minimal incremental costs.

This platform-first approach typically requires 60-70% of initial AI budgets but reduces the cost of subsequent deployments by 50-80%. The platform becomes a force multiplier for AI innovation across the organization.

Portfolio management approach

JPMorgan Chase manages AI investments like a portfolio, balancing high-certainty, incremental improvements with higher-risk, transformational initiatives. This approach ensures steady returns while maintaining innovation capacity.

The bank allocates roughly 70% of AI investments to proven use cases with clear ROI and 30% to experimental initiatives with higher potential but greater uncertainty. This balance provides predictable returns while enabling breakthrough innovations.

Full-lifecycle cost planning

Successful companies budget for the complete AI lifecycle, including initial development, deployment, monitoring, maintenance, and eventual retirement. These full-lifecycle costs are typically 3-5x initial development costs.

McKinsey’s Lilli platform required not just development costs but substantial ongoing investments in content updates, user training, governance, and technical maintenance. Planning for these costs from the beginning prevents budget shortfalls that can derail AI initiatives.

Measuring success: KPIs that matter

The companies that scale AI successfully use sophisticated measurement frameworks that go beyond technical metrics to capture business impact.

Business impact metrics

Walmart measures AI initiatives against business outcomes: e-commerce sales growth (21% increase attributed partly to AI-driven catalog improvements), operational efficiency gains, and customer satisfaction improvements.

JPMorgan Chase tracks AI impact through financial metrics: $220 million in incremental revenue from AI-driven personalization, 90% productivity improvements in document processing, and cost savings from automated compliance processes.

Leading indicators and predictive metrics

Beyond lagging financial indicators, successful companies track leading indicators that predict AI success. These include user adoption rates, data quality improvements, model performance trends, and organizational capability development.

Novartis tracks digital skills development across its workforce, monitoring how AI literacy correlates with improved business outcomes. This helps the company identify areas where additional training or support is needed before problems impact business results.

Portfolio performance management

Companies that scale AI successfully manage their AI initiatives as a portfolio, tracking not just individual project success but overall portfolio performance and resource allocation efficiency.

GE evaluates its AI portfolio across multiple dimensions: technical performance, business impact, risk management, and strategic alignment. This enables sophisticated resource allocation decisions that optimize overall portfolio returns.

The path forward: Practical implementation roadmap

For organizations looking to move from AI experimentation to scaled production systems, the experiences of these Fortune 500 leaders provide a clear roadmap:

Months 1-3: Foundation building

• Establish executive AI steering committee
• Define 3-5 strategic AI objectives aligned with business strategy
• Begin platform infrastructure planning and budgeting
• Conduct organizational AI readiness assessment

Months 4-9: Platform development and pilot selection

• Implement centralized AI platform with governance capabilities
• Launch 2-3 high-ROI pilot initiatives
• Begin workforce AI literacy programs
• Establish risk management and compliance frameworks

Months 10-18: Scaling and optimization

• Scale successful pilots to broader deployment
• Launch second wave of AI initiatives
• Implement continuous monitoring and optimization processes
• Expand AI training and change management programs

Months 19-24: Enterprise integration

• Integrate AI capabilities into core business processes
• Launch third wave focusing on transformational use cases
• Establish AI centers of excellence
• Plan for next-generation AI capabilities

Conclusion: From hype to value

The enterprises that have successfully scaled AI share a common understanding: AI transformation is not primarily about technology—it’s about building organizational capabilities that can systematically deploy AI at scale while managing risk and generating measurable business value.

As Jamie Dimon observed, “AI is going to change every job,” but success requires more than good intentions. It demands disciplined governance, strategic investment, cultural transformation, and sophisticated measurement frameworks.

The companies profiled here have moved beyond the hype to create durable AI capabilities that generate substantial returns. Their experiences provide a practical playbook for organizations ready to make the journey from pilot to profit.

The window for competitive advantage through AI is narrowing. Organizations that delay systematic AI deployment risk being left behind by competitors who have already mastered the transition from experimentation to execution. The path is clear—the question is whether organizations have the discipline and commitment to follow it.