How AI can accelerate impact and value creation for investors
On January 21, 2026, the global AI conversation gathered in Davos under the banner of the Inflection of Intelligence. At the Imagination in Action AI Summit convened by MIT CSAIL and partners, the discussions on general, societal, and planetary intelligence, the emphasis was on the scale of capability, infrastructure, and potential impact. The premise was clear. Artificial intelligence has crossed a threshold where it can meaningfully reshape national strategies, institutions, and infrastructure. What received less attention is the practical mechanism through which AI actually reaches underserved users at scale.
That same day, in parallel, we launched the Alliance for Inclusive AI through a public webinar that drew hundreds of registrations from practitioners, funders, and operators working across financial inclusion, climate adaptation, health, agriculture, and development more broadly. The focus of that conversation was more grounded: how AI can be applied responsibly inside real institutions, in ways that translate into measurable value rather than isolated innovation.
Davos represents possibility. Lagos represents reality.
Why the journey from Davos to Lagos matters
For impact investors, acceleration is not about keeping pace with technological headlines. It is about compressing the time between capital deployment, measurable impact and expected Internal rate of return (IRR). Whether the objective is financial inclusion or climate adaptation, value creation must show up in operating metrics that institutions and investors can see and trust.
In inclusive finance, this means reaching more clients sustainably, improving portfolio quality, and lowering cost-to-serve without sacrificing trust or service quality. In climate adaptation, it means detecting risk earlier, responding faster, and allocating scarce resources more effectively in systems already under strain.
For the past two decades, digital transformation has been the primary pathway to these outcomes. Core banking modernization, digital onboarding, mobile channels, data platforms, and analytics have all played a role. Yet many institutions remain stuck in a long middle phase: partially digitized, operationally complex, and slow to translate digital investments into enterprise-level value.
This is the bottleneck AI is expected to break.
Where digital transformation actually creates value
Across financial services and climate-facing institutions, the evidence points to a consistent pattern. Digital transformation creates value when it changes end-to-end business workflows, not when it automates isolated tasks or introduces new channels alongside old ways of working.
In financial institutions, the workflows that matter most are those that shape economics and risk: customer acquisition and onboarding, underwriting and approval, servicing and collections, and internal decision and reporting loops. In climate adaptation systems, the critical workflows are those that link data to action: risk assessment, early warning, financing decisions, response coordination, and monitoring.
When these workflows change, value follows. Cycle times shorten. Error rates decline. Decisions become more consistent. Feedback loops tighten. These operational improvements translate into financial outcomes such as lower cost-to-serve, improved portfolio performance, and greater resilience.
When workflows do not change, digital transformation struggles to move beyond surface-level gains.
This distinction explains much of the uneven performance investors observe across portfolios. Institutions that redesign how work gets done tend to show sustained improvement. Institutions that layer tools onto existing processes tend to show activity without a durable impact.
In my experience, from Lagos to Cairo and Lima to Dhaka, the pattern is remarkably consistent.
The value creation waterfall, revisited from the ground up
One way to make this logic legible is through a value creation waterfall. Digital initiatives do not create value directly. They create value indirectly by improving a small number of underlying performance levers.
Those levers typically include operating efficiency, risk quality, revenue durability, and strategic readiness. When these move together, enterprise value improves. When they do not, digital activity remains fragmented.
In practice, the initiatives that most reliably contribute to the waterfall are operational and workflow-level in nature. Digital onboarding reduces acquisition friction and processing cost. Field staff tools improve productivity and data quality. Automation in decisioning and collections improves portfolio outcomes. In climate programs, better integration of climate data into operational workflows improves targeting and cost effectiveness.
These interventions change how institutions operate day to day. They are visible in branch routines, field visits, call centers, credit committees, and response teams. That is why they show up in the numbers.
By contrast, initiatives that focus on isolated tasks, dashboards, or disconnected pilots tend to sit above the waterfall rather than feeding into it. They may be useful, but they rarely shift the core economics.
The value creation waterfall, revisited from the ground up
AI is best understood as a technology of scale. It performs well where volumes are high, decisions repeat, and patterns can be learned. This makes it a powerful accelerator once workflows are digitized and instrumented.
In financial institutions, AI can accelerate transformation by improving throughput and consistency in areas such as credit decision support, fraud detection, collections prioritization, and customer support triage. In climate adaptation, AI can accelerate forecasting, scenario analysis, monitoring, and early warning, enabling institutions to respond faster and allocate resources more effectively.
Used this way, AI steepens the value-creation curve. It allows institutions to process more cases, manage more risk, and serve more clients without proportional increases in cost.
Applied outside this context, AI tends to remain at the pilot stage: disconnected from core operations and difficult to justify financially.
This is the central lesson of the journey from Davos to Lagos.
Acceleration is also organizational
One of the less visible constraints on digital transformation is change management. Even when the technology works, organizations struggle to adopt new ways of working at scale. Resistance is rarely ideological. Teams do not see immediate and practical benefits.
Embedded AI can help here as well.
By analyzing operational data, surfacing bottlenecks, and simulating alternative process designs, AI can shorten learning cycles. Teams can see which changes improve outcomes and which do not. Leaders can move from opinion-driven debates to evidence-driven iteration.
This matters acutely in environments like Lagos, where conditions change quickly, and margins for error are thin. Acceleration, in this sense, is not about skipping steps. It is about reducing the cost and time of learning while maintaining control.
Bridging the frontier AI capability and operating-level reality, to support institutions, funders, and partners in applying AI in ways that strengthen workflows, governance, and outcomes rather than fragmenting them.
Acceleration is also organizational
Although inclusive finance and climate adaptation are often treated as separate domains, they share a common structural challenge. Both rely on institutions that must operate at scale in complex, uncertain environments. Both depend on workflows that connect data, decisions, and action across many actors.Digital transformation addresses this challenge by improving coordination and reducing friction. AI accelerates it by increasing speed and consistency.
In financial institutions, this can mean faster and fairer access to credit, improved resilience to shocks, and better allocation of capital. In climate adaptation, it can mean earlier risk detection, more targeted interventions, and more effective use of scarce resources.
In both cases, acceleration comes not from AI itself, but from its ability to amplify well-designed operating models.umenda est, omnis dolor repellendus.
Implications for impact investors
For impact investors, the Davos conversations underscore an important shift. The question is no longer whether AI is relevant. The question is how to deploy AI in ways that accelerate value creation rather than increase complexity.
This has several practical implications.
First, funding should prioritize workflow-level transformation rather than standalone tools. AI investments should be evaluated based on how they change core operations, not on novelty.
Second, advisory support and institutional capability matter as much as technology. Acceleration depends on governance, incentives, and the ability to manage change at scale.
Third, expectations must be context-aware. The same AI-enabled intervention will perform differently depending on digital infrastructure, regulatory environment, and institutional maturity. Acceleration is relative, not absolute.
Finally, AI should be funded as part of an integrated transformation agenda. When treated as a separate stream, it fragments effort. When integrated, it compounds value.
