For the past few years, the spotlight has been firmly on AI models.
Organizations have poured investment into generative AI, machine learning platforms and large language models. New capabilities appear almost weekly. Yet despite all this progress, a familiar question keeps surfacing in boardrooms and leadership meetings – where is the business value?
Part of the answer is that the technology itself is no longer the main obstacle. Powerful AI tools are now within reach of most organizations. What remains difficult is turning AI-generated insights into decisions and measurable business outcomes.
This is the last mile of AI, where many projects lose momentum. A model may produce a recommendation in seconds, but acting on it is often far more complicated. Data may be incomplete, critical context may sit elsewhere, and governance teams may not be confident in the output.
As a result, many organizations find themselves investing heavily in AI while struggling to move beyond pilots and proofs of concept. The missing piece is often the ability to connect intelligence to the reality of how the business really operates.
The real AI bottleneck
When executives talk about successful AI programs, they rarely focus on the sophistication of the model. What matters is the outcome. For example, whether fraud losses fall, if customers can be onboarded faster, or if downtime is reduced. Those outcomes depend on much more than AI itself.
For AI to create value, it needs a clear view of what is happening across the business, enough context to understand what those events mean, and the ability to operate within governance guardrails. When any of those elements are missing, recommendations become harder to trust, automation stalls, and adoption suffers.
Many organizations discover that the real bottleneck sits between the model and the business process. Data is fragmented across systems, and essential context is missing. Often, information arrives too late to be useful. Different teams define the same business concepts in different ways. These are not new problems, but AI has exposed them more clearly than ever before.
We see this challenge regularly in financial services. One institution had invested heavily in AI-driven fraud detection, yet investigators still struggled to act quickly because customer records, transaction histories, and external fraud signals were spread across multiple systems. Once those sources were brought together in a trusted, governed view, fraud losses fell, false positives declined, and onboarding processes became more efficient.
AI needs a broader view of the business
One reason many organizations struggle to get AI into production is that the technology depends on a much broader view of the business than traditional analytics ever did.
Most companies have spent years investing in ERP platforms, CRM applications, and operational databases. These systems remain the authoritative record of the business and provide much of the information AI needs to work effectively.
To make good decisions, AI increasingly needs information from outside those core systems. It may need supplier data to understand a disruption in the supply chain. It may need information from partners, SaaS applications, or external intelligence sources to provide business context. Increasingly, it also needs access to live operational signals, whether that's customer interactions, fraud alerts, connected devices, or event streams that reveal what's happening right now.
You can think of this as three layers of information:
- The first is the authoritative data held within core enterprise systems.
- The second is contextual information from partners, suppliers and external sources that helps explain why events are occurring.
- The third is real-time operational awareness, the signals that show what is happening in the moment.
Most organizations have made significant progress managing the first layer. Far fewer have found an effective way to combine all three. A global manufacturer we worked with faced a similar challenge.
Its AI models could predict equipment failures, but critical information about production schedules, supplier delays, and maintenance activities sat across different systems. Connecting those sources gave teams the context needed to identify risks earlier, reduce downtime, and make better operational decisions.
As a result, AI often operates with an incomplete picture of reality. It may understand what happened yesterday, but not what is happening now. It may have access to internal records, but lack the external context needed to make a confident recommendation.
This is where the last mile challenge starts to emerge.
The ecosystem effect
The rise of AI is also changing how organizations think about data sharing. For years, most businesses focused primarily on their own internal systems. Today, many of the decisions that matter depend on information that sits beyond organizational boundaries.
A manufacturer responding to supply chain disruption needs visibility into suppliers and logistics partners. A bank trying to detect fraud benefits from external signals as much as internal customer data. Public sector organizations often need information from multiple agencies to improve services and outcomes.
The common thread is that intelligence increasingly flows across an ecosystem rather than a single enterprise. Trusted AI outcomes depend on trusted access to data wherever that data resides.
Technology leaders therefore face a balancing act: making data accessible across distributed environments while maintaining governance, security and ownership. Without that foundation, even the most advanced models struggle to deliver consistent results.
Closing the last mile
If there is one lesson emerging from the first wave of enterprise AI adoption, it is that success depends less on deploying another model and more on creating the conditions that allow AI to operate effectively.
That starts with trusted data. Business users need confidence that the information feeding AI systems is accurate, up to date, and governed consistently. The challenge is also making it available in a way that AI can use without creating new silos or adding more complexity. AI needs access to core business systems, external ecosystem data, and real-time operational signals. If those remain disconnected, the quality of the output will always be limited.
Why understanding matters
Data alone, however, rarely tells the full story. AI needs to not only understand the data itself, but the business context around it. For example, where it came from, how it relates to other information, and the policies that govern its use. That context is often what separates a useful recommendation from a misleading one. Without it, even accurate data can lead to poor decisions.
This is where active context comes in. It provides AI with the richer understanding needed to interpret information correctly, connect it to the wider business environment, and make decisions that are grounded, relevant, and well governed.
Turning insight into action
The final step is where many organizations still struggle. Insights need to become actions.
Too often, AI generates recommendations that sit in dashboards, reports or isolated applications. The greatest value comes when intelligence is embedded directly into operational workflows, helping people make decisions faster or automating routine processes altogether. That is where AI starts to move beyond experimentation and deliver measurable business outcomes.
For technology leaders, a useful test is to ask a handful of simple questions.
- Can AI access information as events happen, or is it relying on yesterday's data?
- Can it combine internal and external sources to build a complete picture?
- Are governance policies applied consistently across different environments?
- Do people trust the outputs enough to act on them?
If the answer to any of those questions is no, the next investment should probably be focused on the data foundation rather than the model itself.
Where the next gains will come from
The organizations generating the greatest returns from AI are not necessarily those deploying the newest technology. More often, they are the ones that have found a way to connect trusted data, business context, and operational processes.
In banking, that might mean reducing fraud losses while accelerating onboarding. In manufacturing, it could mean identifying disruptions earlier and reducing downtime. In the public sector, it may involve improving citizen services through better collaboration across departments. In each case, the value comes not from the model itself, but from embedding intelligence into operational decisions and workflows.
That is the last mile of AI. For many organizations, it remains the biggest barrier to success. It may also be the biggest opportunity.
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