— With 95 percent of enterprise AI pilots generating zero measurable return and three-quarters of AI’s economic value captured by just 20 percent of organisations, Sandhya Sabapathy argues that the constraint is not model capability but the organisational infrastructure that must be built before value can be absorbed.
A pattern is emerging across three of the most consequential transformation challenges facing organisations today: artificial intelligence adoption, climate adaptation, and regulatory compliance. In each case, the cost arrives first and the value follows, but only for those who invest in the absorption capacity required to bridge the gap.
Sandhya Sabapathy, founder of Kaleidoscope, draws on a body of 2025 and 2026 research to make this case. According to MIT NANDA’s 2025 State of AI in Business report, despite an estimated $30 to $40 billion in enterprise generative AI investment, 95 percent of organisations report zero measurable return on their profit and loss. PwC’s 2026 AI Performance Study, drawing on 1,217 executives across 25 sectors, finds that nearly three-quarters of AI’s economic value is being captured by just one-fifth of organisations.
“The technology is not the bottleneck. The organisations that have not built the infrastructure to absorb it are,” says Sandhya Sabapathy.
What the Value-Capturing 20 Percent Did Differently
McKinsey’s State of Organizations 2026, surveying more than 10,000 executives, finds that 72 percent of leaders say their organisation is not fully ready for the changes, including AI-driven transformation, reshaping how they operate. BCG’s May 2026 analysis argues that the companies capturing AI’s value are those that restructure their functions and reallocate resources before they scale the technology, not after.
The constraint is not model capability. It is organisational design. Companies that restructure functions around AI-native, cross-functional workflows before deploying AI at scale are the ones showing up in PwC’s value-capturing 20 percent.
IDC projects that over 90 percent of enterprises will face a critical AI skills shortage, with the resulting gap sized at $5.5 trillion globally. Governance and skills are not soft costs. They are the infrastructure spend that has to happen before AI value materialises, on the same order of magnitude as the technology investment itself.
“Governance and skills are not soft costs. They are the infrastructure spend that has to happen before AI value materialises,” says Sandhya Sabapathy.
The Cost of Moving Without Absorption Capacity
Research and industry experience consistently show what happens when organisations skip the capacity-building step and move directly to cost-cutting or automation without first building the governance, quality-assurance, and escalation infrastructure required to sustain it.
The pattern is well documented. Organisations that deploy automation at scale before embedding the operational capacity to manage it tend to experience a predictable sequence: initial cost reduction, followed by quality degradation, followed by a reversal that costs more than the preparation would have. The sequence matters. Absorption capacity must be built before automation is deployed at scale, not after the problems it creates have already arrived.
The Climate Adaptation Echo
Climate adaptation follows the same sequence. The International Finance Corporation, AXA Climate, and Scientific Climate Ratings published research in June 2026 demonstrating that investing less than ten percent more upfront in resilience measures can substantially reduce climate-related losses over an asset’s lifetime.
The European Commission’s own estimate puts the EU’s annual climate adaptation investment need at approximately €70 billion per year through 2050, broken down across infrastructure (approximately €30 billion), ecosystems (approximately €21 billion), and food security (approximately €12 billion). Current EU adaptation-related funding runs at an estimated €15 to €16 billion per year against that total requirement.
“In both cases, the cost of preparation is large, immediate, and real. The value materialises only later, and only if the preparation was done correctly,” says Sandhya Sabapathy.
The Regulatory Compliance Pattern
Regulatory compliance presents the same sequence in a third domain. The EU AI Act’s compliance costs for high-risk AI systems are estimated at €320,000 to €600,000 per entity, according to European Parliament analysis. First-year sustainability reporting compliance for large, multi-jurisdictional enterprises is estimated at €500,000 to €2 million or more, according to industry estimates from DigitalEurope and EuroChambres.
Both represent costs that arrive before the compliance regime’s intended benefits are realised. Organisations that defer preparation until the enforcement date is imminent face compressed timelines, premium costs, and the reputational risk of having waited until the last moment on obligations that were visible years in advance.
Building Slow Means Building Absorption Capacity
The common thread across AI, climate adaptation, and regulatory compliance is that absorption capacity must be built before transformation value can arrive. Organisations that move fast without it face a predictable outcome: the cost without the value, followed by a reversal that is more expensive than the preparation would have been.
“The organisations capturing AI’s value are not moving more slowly than their peers. They are moving in the right sequence. That sequence, not the speed of deployment, is what determines whether the cost of transformation is followed by its value or simply by more cost,” says Sandhya Sabapathy.
About Kaleidoscope
Kaleidoscope is founded by Sandhya Sabapathy and works with organisations navigating the intersection of AI strategy, climate adaptation, and regulatory compliance. For more information connect with Sandhya Sabapathy on LinkedIn.
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