According to a recent MIT study, 95% of AI initiatives stall before scaling. And only one in four companies reports significant value from their AI investments (BCG AI Radar Survey). Every company is using AI, but most can't measure if it's actually improving business outcomes.
The fundamental problem is the approach, not the technology. Companies deploy AI capabilities without anchoring them to business goals. They create information without connecting it to execution. Despite massive investment in AI infrastructure, most enterprises struggle to see meaningful returns.
Decision Intelligence (DI) offers a solution. A systematic approach to treating decision-making as a measurable business process, DI helps AI create value by tying it to business goals and turning information into action.
Over the last year, $500 billion has been invested in building AI capacity: data centers, model training, and the broader AI ecosystem. The expected return is massive. McKinsey projects AI will create $20 trillion in value by 2030, adding the equivalent of a new G7 nation to the global economy.
But the bulk of that value depends on enterprise productivity gains, not AI infrastructure alone. The hyperscalers are building supply quickly, but the fundamental question remains: can enterprises figure out how to realize that value?
The first wave of AI deployments (2021 to now) shows mixed results. Only 25% of companies report seeing significant value from their AI pilots. More than half have paused AI initiatives. Paradoxically, people using AI report more burnout than those who aren't.
Three failure points explain why AI pilots stall:
The real breakthrough comes when AI is anchored to business goals, designed for how people work, and built to turn information into action. That's where decisions become critical.
Decision-making drives 95% of business performance (Bain & Company). Yet a Cloverpop study revealed that 78% of data-driven insights never influence actual decisions. Organizations make more than 10 million decisions annually, but few companies treat decision-making as a measurable business process.
We have systems of record for sales, marketing, and finance. We can answer "How many deals closed last quarter?" instantly. But ask "How many strategic decisions did that team make?" or "Which decisions outperformed expectations?" and you'll get blank stares.
This reveals what's fundamentally missing: the decision layer is the most important piece in the entire value chain. When reverse-engineering decisions to understand what actually drives them, we find that 70% of the inputs aren't in data models. These inputs are human-driven: the debates, the conversations, the cognitive biases, the expertise and experience that happen in the decision-making room.
The Missing Decision Layer
Think of the enterprise AI stack as having four layers:
Leaders are recognizing the significance of this layer, as one VP of Commercial Data Science observed, "The decision layer is often overlooked, yet it is the most crucial element driving the business forward." This missing layer is what activates AI inside the enterprise. Without it, insights remain disconnected from the decisions that drive performance.
To incorporate the decision layer, you need Decision Intelligence.
What Is Decision Intelligence?
Decision Intelligence treats decisions as the activation point where enterprise value is fundamentally created. Decisions require more than data. They require people, process, and technology coming together to drive action and value.
DI brings together human intelligence (the expertise and experience of your workforce) with machine intelligence (data science, IT, and automation) across an end-to-end decision-making cycle. The process includes modeling the decision, orchestrating all the components, making the decision, executing it, monitoring its performance, and learning from those decisions in a closed-loop cycle.
The Decision-Back™ approach is foundational. Traditional approaches start with data, synthesize information, create insights, develop recommendations, and hope decision-makers execute. Decision-back thinking flips this. It starts with business objectives, identifies the key decisions required to achieve those objectives, and then determines what insights, data, and tools those decisions require.
The Cloverpop DI platform operationalizes this approach at scale. We create structured decision workflows, orchestrate stakeholder input across distributed teams, capture every decision as institutional knowledge, and track outcomes for continuous improvement. This changes decision-making into a measurable process where AI adds meaningful value.
When organizations implement Decision Intelligence, they see faster decisions and better outcomes. Three examples illustrate the impact:
#1 Consumer Health: Automating Media Investment Decisions
A global consumer health company needed to allocate media budgets weekly across multiple brands, geographies, and channels. The decisions were complex and political: how much total spend, which brands, which regions, which channels (social vs. search, TikTok vs. Instagram)?
They implemented our DI platform to codify their media allocation framework, connecting multiple data sources, including media mix models, lift coefficients, and point-of-sale data. Now, the system provides hundreds of weekly recommendations to media buyers, including specific guidance like "shift $20K from Instagram to TikTok" or "reduce linear TV spend by 15%."
The process became faster and more objective. Media recommendations now reach brand managers 80% faster, using 30% fewer resources, and politics no longer slow down decisions.
#2 Brand Performance Optimization: 2X Faster, 30% Cheaper
A major CPG company was spending hundreds of thousands on brand equity studies and syndicated data, receiving 50-60 page decks in return. Brand managers struggled to connect those insights to actionable tactics for improving performance.
With our DI platform, the company digitized how brand managers think about increasing share and top-line growth, connecting those decision frameworks to multiple data sources. The system now generates specific, automated recommendations (like adjusting media spend or repositioning tactics) tied directly to market share goals.
Brand managers now get recommendations 2X faster and 30% cheaper than traditional analytics suppliers. They no longer manually translate insights into action, since brand equity studies automatically connect to business tactics.
#3 Pharmaceutical Supply Chain: 150,000 Additional Doses Delivered
A pharmaceutical manufacturer producing life-saving oncology drugs faced a critical challenge: each batch required 120+ raw materials worth $20 million.
When suppliers experienced delays, decision-makers used the Cloverpop platform to evaluate alternative suppliers and maintain production schedules. The structured decision framework, combined with pre-connected data sources, enabled rapid, confident decisions.
The team made supplier decisions 40% faster, delivering 150,000 additional drug doses on schedule, because decisions that previously took days now take hours, ensuring patients receive their life-saving medication.
The path to AI business value begins with a simple shift: anchor AI deployments to specific business decisions.
Rather than starting with AI capabilities and searching for applications, begin with the decisions that drive your business objectives. Map the decision logic, identify required inputs and stakeholders, then determine how AI can accelerate or improve those specific decisions.
This decision-back approach ensures AI investments deliver measurable business impact rather than remaining isolated pilots. When decisions become your system of record, AI transforms from an experimental technology into a competitive advantage.
The Cloverpop Decision Intelligence platform operationalizes this approach at scale. We structure decision logic, orchestrate cross-functional collaboration, automate recommendation development, and create institutional knowledge from every decision made.
The companies seeing real AI business value are the ones who've made decision-making a measurable, improvable business process. Schedule a demo to see how.