So you know you need to an “AI strategy.” But where to begin? How to think about it in a logical, structured, holistic way?

It matters. Research suggests companies with formal AI strategies report 80% success rates, compared to just 37% for those without structured approaches.

A structured AI roadmap is a must. One that recognizes the major reasons AI fails, and has a strategy for proactively mitigating those risks.

In this article we’ll introduce the Manifold AI Roadmap - a four-phased approach that treats strategic alignment, organizational capabilities, and technical foundations as integrated components. We’ll also show you how you can implement it inside your organization to increase your odds of success with AI-based initiatives.

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Phase 1: Strategic Foundations

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Executive Leadership

In April 2025, Shopify CEO Tobi Lütke issued an internal mandate. All employees must integrate AI into their daily work. AI usage waw now a "baseline expectation." Teams now had to demonstrate why AI couldn't perform a task before they could ask for more resources or headcount.

Like many strategic initiatives, AI transformation starts and succeeds in the C-suite. Not just “executive sponsorship” in the traditional sense. Active, intentional leadership, ideally tied to measurable outcomes.

That's the level of commitment required. You have to be willing to rewrite incentive systems. Embed AI objectives into performance reviews. Make AI adoption a clear and visible priority.

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ROI-Based Use Case Identification

The best use cases in our opinion are where proprietary data represents legitimate business opportunities. You conduct an analysis across the entire value chain of your organization, looking for areas where the combination of your unique data assets and AI capabilities can create value that competitors can't easily replicate.That’s where we tend to think AI can create sustainable competitive advantages.

For a healthcare provider, that might be reducing claims processing errors by leveraging historical patterns in your specific patient population. For a manufacturer, it could be anomaly detection on equipment using your proprietary operational data. For a bank, it's customer intelligence for better cross-selling based on your unique relationship history.

The key is anchoring every use case to measurable business outcomes.

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Data Infrastructure Reality Check

Poor data quality is consistently identified as the primary technical failure factor. AI outcomes are only as good as the input data. You have to assess the current state, and do so with a brutally honest lens.

• How reliable is your data?
• Do governance policies actually exist and get enforced?
• Is your cloud foundation scalable enough to handle AI workloads?
• Can analysts access data without writing custom queries against three different systems?

It’s important to ask these questions up front, because fixing data infrastructure takes time. You’re going to have to do it eventually. The sooner the better.

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Building Organizational Muscle

Organizational capabilities are as important as technical capabilities. This means identifying existing skills and gaps. Then developing comprehensive up-skilling strategies to build AI fluency across functions.

It also means establishing governance frameworks that address privacy, security, and compliance. And it means creating change management capabilities that can handle the cultural transformation AI requires.

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Phase 2: Pilots That Actually Scale

Most pilot projects are designed to fail at scale. They operate in isolation. They use different data than production systems. They and focus on proving technical feasibility rather than business value.

The enhanced pilot approach flips this dynamic. Instead of building isolated POCs, you build pilots that are designed from day one to scale across the enterprise.

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Single Use Cases, Maximum Learning

OpenAI's research on enterprise adoption emphasizes honing in on a single, carefully chosen use case to start. They use that pilot to establish patterns for operating and building organizational trust.

Consider a recent healthcare implementation we led, an AI assistant that generates structured summaries within five seconds of call completion. The technology delivered $500K in annual savings with zero core system changes. But the real value wasn't the cost savings. It was creating internal champions and demonstrating how AI could augment human workflows rather than replace them.

You want to use the pilot to develop organizational muscle memory for AI adoption, tested governance frameworks under real conditions, and templates for scaling similar applications across other departments.

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Governance Integration From Day One

Early pilots are the moment to test governance. How do you ensure privacy? How (and when) do you keep humans in the loop? How do you maintain audit trails and explainability?

Getting this right at the pilot stage prevents larger-scale risks later. It also builds confidence among stakeholders who might otherwise resist AI adoption due to compliance concerns.

We think the most effective approach here treats governance like code. That means versioned policies, automated checks, and continuous enforcement. Spreadsheets and manual processes don’t work when your systems are self-learning and reinforcing. You need governance frameworks that can scale with AI deployment.

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Technology Selection Matters

General purpose chatbots only get you so far. Enabling redesigned workflows requires more specialized tools.

You also want to build integration capabilities that connect AI to existing workflows rather than creating parallel processes. And you need monitoring and feedback loops that enable continuous improvement.

All of this is necessary to prove AI works for your specific business in ways that create measurable value and can be systematically replicated.

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ROI Stories That Sell

The output of Phase 2 is ultimately a story. A clear, credible demonstration of ROI that executives can point to when advocating for scale.

The most compelling stories demonstrate value across multiple dimensions:

• Efficiency gains
• Quality improvements
• Customer satisfaction
• Strategic positioning benefits

These stories become the foundation for Phase 3 transformation. They provide proof points that overcome organizational resistance, demonstrate patterns that can be replicated, and establish metrics that can be used to measure broader AI success.

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Phase 3: Creating a Portfolio of Transformation

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Strategic Portfolio Management

Bain's research echoes what Shopify intuited: successful requires a redesign of the business with AI at the core. Departmental implementations are not enough. You need integrated AI capabilities that span business functions.

We’re advocates for a strategic portfolio approach that identifies use cases across business functions and prioritizes them based on some combination of technical feasibility (typical a binary go/no-go decision), strategic impact if successful, and organizational readiness. Even if you deviate from whatever ranking this results in, we’ve found doing this exercise provides some objectivity.

The most nuanced lens is around strategic impact. The best initiatives don’t just create a win in one area, but open up new opportunities in other areas as well. It can sometimes be hard to think through the second and third-order effects, but when you spot them they can be powerful.

For example, we worked with an oilfield service provider to automated 75-90% of technician monitoring tasks through AI-driven anomaly detection. It was initially meant as an internal process. But they discovered opportunities to commercialize the solution as a product offering.

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Scalable Infrastructure

The technical requirements for enterprise AI are different from pilot requirements. You need infrastructure that can handle multiple models. Diverse data sources. Real-time processing. Enterprise-grade security.

This is where that data foundation work from Phase 1 pays dividends. Organizations with solid data infrastructure can deploy AI applications faster and more reliably. Those who skip that work find they have to rebuild some or all of entire data architecture later.

Cloud-native architectures are a must here. You need to separate storage from compute. Enable independent scaling of different components. To provide the flexibility to adapt as AI requirements evolve. You also want to establish MLOps capabilities to automate model deployment, monitoring, and maintenance across the enterprise.

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Change Management

Change management is a core competence for any transformation work. With AI, you’re not just teaching your team about a new tool or workflow. You’re teaching them to think about the way they interact with software differently.

You want training programs that build AI fluency across functions. Adoption support to help employees integrate AI into daily workflows. And feedback mechanisms that capture lessons learned and drive continuous improvement.

Most importantly, you must address the human side of AI adoption. All the stuff that folks might not say, but are secretly scared of.

• How do you help employees see AI as a tool that will make their work better, and not as an enemy coming to replace them?
• How do you build confidence in AI-driven decisions (especially the ones where there are stakes involved?)
• How do you automate while maintaining some mechanism for human oversight?

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Performance Measurement

At this point you want to track not just individual AI applications, but the overall impact of your AI portfolio.

Traditional ROI metrics like cost savings and productivity gains matter. But you also want to be looking at strategic metrics like time-to-market improvements. Customer satisfaction enhancements. Competitive positioning benefits (admittedly hard to quantify).

You also want to implement continuous optimization processes to improve AI applications over time. End users need to have a way to submit feedback back to the technical teams and business leaders, so they can rapidly iterate based on what’s actually happening in the field.

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Building Competitive Moats

Candidly not every company gets here. But in an ideal world, you’re not just using AI to make things better. Ideally you’re now able to do things your competitors can’t do at all. Again, that is largely a function of the data you have at your disposal.

This might mean creating personalized customer experiences that competitors with less sophisticated data can't match. It could involve developing predictive capabilities that enable proactive rather than reactive business strategies. Or it might mean building AI-native products and services that create entirely new revenue opportunities.

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Phase 4: Enterprise Scale and Competitive Advantage

Phase 4 is the culmination. The promised land. Where organizations have enterprise-wide AI capabilities and are seeing legit benefits from them.

AI is now embedded in the organizational DNA. It's simply how you operate. Customer service interactions are augmented by real-time intelligence. Predictive supply chains. AI-driven product development cycles.

Advanced Use Case Implementation

Phase 4 is when you get to invest in more sophisticated AI applications that weren't feasible during earlier phases. The kinds of things that require a solid data foundation, good governance and an organization that’s ready to explore the boundaries. This is when you start to see the magic of “compound interest”, where the virtuous loop starts.

Continuous Innovation and Adaptation

At Phase 4 you start to invest in maintaining and improving the system. Tech changes all the time, and nowhere is that true more than with AI tech. You have to have a way to stay agile. To test new applications or models. To be able to respond to technology that changes, and to competitors who eventually get wise an start trying to catch up.

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How to Begin Executing on a Roadmap

The first step, as always, is to “know thyself.” To understand where you actually stand.

• How mature is your data infrastructure? (In most cases, not great.)
• What's the current state of AI skills across your organization? (In most cases, borderline non-existent.)
• Where are the biggest opportunities for AI-driven value creation? (In most cases, everywhere you look.)

This assessment needs to be honest. It's better to discover issues or early than to have them derail AI initiatives later.

It’s okay if you don’t have perfect starting conditions. No one does. What matters is having realistic starting conditions.

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Build Incrementally

Start with carefully chosen pilots that demonstrate value and build organizational confidence. Design those pilots as building blocks for broader transformation. And use them to help build organizational muscle memory in addition to demonstrating early value.

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Measure What Matters

Transformation requires new metrics. On-time delivery and budget are necessary but insufficient. You need metrics that track business impact, user adoption, model performance, and strategic positioning.

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Invest in Capabilities

Start the process of building AI fluency across functions. Do the hard work of establishing governance frameworks that scale with AI deployment. Doing so makes sure you can create organizational cultures that embrace AI-driven decision making.

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Conclusion

We're entering an era where AI capabilities will define competitive advantage across industries. And your organization can participate. The roadmap exists. If you can execute with discipline and systematic thinking, you’ll become an organization others look to as a model to emulate.

Ready to move beyond pilot purgatory? We’d love to help.

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