How to build an AI innovation pipeline that creates real long-term value

As 2026 begins, many organizations are launching AI transformation initiatives. The new year brings with it fresh budgets, renewed strategic focus, and mounting pressure to capture value from artificial intelligence. Yet studies consistently show that most AI projects fail to generate meaningful returns. Companies pour resources into promising experiments that never scale, accumulate tools that are never integrated, and watch initial enthusiasm curdle into skepticism.

What separates organizations that create lasting value from those that don’t is rarely the technology to which they have access. Instead, the critical “secret sauce” lies in having a systematic, rigorous, and repeatable approach that allows the leadership team to move from the identification of opportunities to operational deployment.

This article offers a practical playbook for that journey, using the illustrative example of a midsize manufacturing firm (Aurora Windows). While the playbook itself distills learnings gained from large, technically sophisticated businesses in sectors such as defense and finance, our example shows how these lessons can be applied even in late-adopting companies with limited resources. At present, there are few examples of systematic end-to-end AI innovation pipelines that have been deployed successfully in the real world, so our example can only be illustrative. Nevertheless, forward-looking companies are already beginning their journeys along this path and evidence from decades of organizational and digital transformation efforts allow us to model what success will ultimately look like.

I will be using this playbook in my upcoming guest lecture for International Institute for Management Development (IMD) Business School’s AI strategy and implementation executive program, delivered in collaboration with Misiek Piskorski, dean of executive education at IMD, and Amit Joshi, codirector of the program. IMD is a world-leading business school, ranked No. 1 globally in custom executive education by the Financial Times (2025), renowned for transforming rigorous research into actionable leadership results.

An Illustrative Example: Aurora Windows

Aurora Windows is a 35-year-old, second-generation manufacturing company that designs and produces doors, windows, and architectural glass for commercial and residential building projects. With roughly 220 employees across one main plant and two regional distribution hubs, it sits in the classic “too big to be small, too small to be big” SME band: large enough to feel pressure from global competitors and construction giants, but without a dedicated transformation department or a large consulting budget. Over the next five years, the leadership team aims to position Aurora as the “go-to” innovation partner for sustainable, smart building projects by becoming a fully AI-driven business.

The Innovation Pipeline

To succeed in its goals, Aurora needs to take a disciplined approach to AI enterprise transformation that treats the innovation process as a continuous structured pipeline with clear stages. Projects flow from initial ideation into a rigorous assessment phase and on to operational deployment—a narrowing funnel that sees many ideas entering but only the strongest and most strategically aligned reaching production.

Firms in some sectors—such as tech and pharmaceutical companies—have long relied on continuous product development pipelines that systematically advance projects from abstract ideas to market-ready products. In the AI age, every organization needs to adopt this kind of systematic approach to innovation. But this is more than just a new product development pipeline: Innovation projects must be aligned with the broader organizational culture and processes within which they will be embedded.

Step 1: Current-State Assessment—Establishing Your Baseline

Before Aurora can begin managing an innovation pipeline, the leadership team needs to understand where the company currently stands. They conduct a baseline assessment across three dimensions:

Organizational purpose and strategic clarity

Aurora’s executive team revisits its core mission: creating high-performing, sustainable door, window, and glass solutions that make buildings safer, more comfortable, and more energy efficient. The team articulates three specific five-year goals:

• 40% revenue growth without proportional headcount increases
• Margin protection despite volatile input costs
• Positioning as the go-to AI-driven innovation partner.

This clarity becomes the North Star for evaluating every AI initiative.

Knowledge baseline

The team then assesses the company’s current AI literacy. At present, there is a scattering of expertise across departments, with individual enthusiasts driving the current pilot programs. AI knowledge in the leadership team is limited and most of the business’s staff are unfamiliar with basic machine learning concepts.

Risk appetite

Aurora is a family business that has survived by not taking reckless bets. But the market is shifting. Competitors are beginning to offer AI-enhanced design services and predictive maintenance. The leadership team articulates a balanced stance toward risk: Aurora needs to advance more rapidly than they would normally be inclined to move, but with guardrails in place to protect the brand’s hard-won reputation.

This assessment reveals uncomfortable truths. Aurora has enthusiasm for AI transformation but no shared knowledge base or language for discussing AI, and no accepted criteria for assessing the value of pilot projects. The leadership team has ambition but there is currently no defined path to move projects from the pilot phase to company-wide operation. Most importantly, there is no mechanism for deciding what to do next.

Step 2: Opportunities—Populating the Innovation Pipeline

Aurora’s leadership now launches a structured ideation process to identify projects that are explicitly aligned with the company’s strategic goals. Rather than asking “What can we do with AI?” cross-functional teams ask “What problems prevent us from achieving our strategic goals, and can AI help us solve them?”

The teams quickly generate two dozen initial ideas spanning multiple AI types: analytical AI for process optimization, workflow automation to reduce manual tasks, generative AI for design acceleration, and even agentic AI systems operating semiautonomously within defined parameters.

Each idea receives a rapid initial assessment using five criteria scored 1 to 10:

• Priority: How urgently does this support our core goals?
• Risk: What’s the potential downside if this fails after deployment?
• Value: What’s the likely financial or strategic return?
• Cost: What investment is required to reach production?
• Difficulty: How challenging will implementation and adoption be?

When scored and ranked, clear patterns emerge. Several high-scoring opportunities cluster around production efficiency—using computer vision for defect detection, AI-driven equipment maintenance prediction, and automated quality documentation. A number of initiatives focusing on design acceleration and customer experience receive medium scores. Several “moon shot” projects that were initially very popular with senior leaders receive low scores because they are technically difficult, expensive, and come with significant risks, despite their high potential payoff.

This process also surfaces important dependencies. A design acceleration project that has many supporters would require clean CAD libraries and standardized templates—work that hasn’t started yet. Similarly, a maintenance prediction system needs sensor data that is not yet available but that would be generated if one of the quality inspection projects goes ahead.

The ideation exercise produces more than a ranked list of ideas. It creates a common vocabulary for discussing AI opportunities at the same time as revealing capability gaps and building consensus around which directions make strategic sense. Of Aurora’s 24 ideas, 6 scored highly enough to warrant further detailed assessment. The rest remain in the backlog—not definitively rejected, but requiring either new capabilities or a shift in strategic priority to make them viable.

Step 3: Assessment—Enterprise Architecture Analysis and Fit

The six projects that ranked highest in the initial screening now enter detailed assessment. Aurora’s leadership team first maps the organization’s Strategic Enterprise Architecture (SEA) and then assesses each project’s degree of fit across four dimensions:

Purpose and Strategic Intent

Does this project directly advance Aurora’s three strategic goals with clear, measurable outcomes?

People and Culture

Are leadership and staff ready for the changes the project involves?

Processes and Governance

Can the initiative integrate with current processes and operating models?

Technology Architecture and Data

Is the initiative feasible using existing or available systems?

The results are sobering. Of the six projects under assessment, only three demonstrate clear alignment across all four SEA dimensions. Two of the others could become viable with specific capability-building work.

The SEA analysis also reveals positive insights. The quality inspection camera project will generate structured defect data that several other proposed projects can use. By recognizing this dependency, Aurora can sequence projects to build on this foundation.

Step 4: Operationalization—From Experimentation to Production

The three projects that passed detailed assessment now undergo active experimentation. Aurora structures these experiments as learning journeys, not just technical validations. The visual quality inspection project runs bounded pilots on specific production lines. The AI-assisted design tools are tested with a small R&D team before broader rollout. The data infrastructure project proceeds in phases, upgrading one integration at a time while minimizing disruption.

After six months of experimentation, the newly developed quality inspection tool passes all tests and moves to production. The data infrastructure project shows promise but needs another quarter of refinement—it remains in experimentation. After a promising start, the AI-assisted design tools run into a technical wall. With no clear path forward, the project is paused until a technical solution is identified.

Systems that reach production require ongoing monitoring, cost tracking, and impact measurement. Aurora establishes guardrails to prevent misuse and implements continuous monitoring to catch issues before they become problems.

Sustaining the Pipeline

Aurora’s innovation pipeline is a long-term, repeatable system that provides the engine for continuous AI transformation. But to deliver its value, it must be carefully tended. The leadership team establishes a quarterly review process with three goals:

Project health checks

Are experimental projects meeting milestones? Are production systems delivering expected value? Do any initiatives need intervention, resources, or retirement?

Pipeline rebalancing

As projects advance, move into production, or are killed, the pipeline needs replenishment. The leadership team takes a view across the entire pipeline to ensure that the right mix of projects is moving through, balanced across time horizons, risk levels, and strategic targets.

Strategic recalibration

Markets, technologies, and organizational priorities shift. Quarterly reviews explicitly ask: Do our scoring criteria still reflect strategy? Are new capabilities or partnerships available? Have competitors made moves that change our priorities?

This operating rhythm transforms Aurora’s relationship with AI. Instead of episodic enthusiasm followed by disappointment when pilots don’t scale, the leadership team has a sustainable engine for continuous improvement. Each quarter brings visible progress—some quick wins, some foundation building, some ambitious bets advancing.

Within 18 months, Aurora’s transformation becomes tangible. The company now has three AI systems in production (quality inspection across all lines, automated quality documentation, and a new LLM-powered customer portal). The projects in experimentation and assessment build on these initial experiences and include initiatives that have become viable thanks to the technical capacity, skills, and processes developed while working on the initial round of projects. By avoiding wasteful efforts to develop a series of unconnected pilots with no clear strategic value, Aurora has built a foundation of success that is propelling it past its competitors.

Conclusion: The Management System Behind the Pipeline

Aurora’s story highlights a fundamental truth about AI transformation: Technology is rarely the constraint. Most companies can access impressive AI tools. What they lack are the management systems needed to deploy those tools strategically, build repeatable capabilities, and create sustained value.

An innovation pipeline like the one in our example does not run itself. It requires systems and structures that create both horizontal and vertical collaboration—linking the C-suite to project teams and linking project teams to the rest of the organization. Without these connections, even the best-designed pipelines will stall.

Cultural change is often framed as a precondition for AI transformation. But culture doesn’t shift as a result of exhortation alone. It is shaped and steered by the processes, review rhythms, and governance structures that determine how decisions get made and how work flows through the organization. Quarterly reviews, cross-functional assessment teams, and clear advancement criteria aren’t bureaucratic overhead. They are the mechanisms through which a culture of disciplined innovation takes root.

The companies that succeed with AI won’t be those with the most ambitious pilots or the earliest adoption of new tools. They will be those that build the management systems that are needed to move systematically from opportunity to assessment to operation—and to sustain that movement over time.