AI in 2026: From Experiment to Enterprise Value

In 2024, AI was a proof-of-concept.

In 2025, it was a pilot.

In 2026, it will be judged on results.

According to McKinsey, more organizations are integrating generative AI into their operations, with 72 percent using it in one or more business functions in early 2024, compared with 56 percent in 2021. This suggests that businesses embedding AI into their core functions are increasingly realizing measurable benefits, while those treating AI as a side experiment may struggle to keep up.

This marks a shift from experimentation to expecting real, proven results from AI.

And that changes everything.

The Experimentation Era Is Closing

Over the past two years, AI adoption has accelerated at an unprecedented speed. According to McKinsey & Company, over 65 per cent of organisations report using generative AI in at least one business function. That figure has nearly doubled in under a year.

But usage is not the same as value.

Research from Gartner suggests that a significant proportion of AI projects never progress beyond the pilot stage, often due to unclear ROI, integration challenges, or governance concerns.

We’ve seen this firsthand. Teams build promising AI prototypes. A chatbot that works beautifully in isolation. A predictive model that performs well in a sandbox. An automation layer that speeds up one small task.

But when it comes to integrating those tools into live operational systems, CRM platforms, clinical workflows, underwriting engines, and customer service environments, the complexity multiplies.

The result? AI exists. But impact remains patchy.

2026 is the year businesses stop asking “Can we use AI?” and start asking “Is it delivering?”

What Changes in 2026? Risk, Regulation and Real Stakes

The next wave of AI adoption will not be driven by curiosity. It will be driven by pressure.

Two sectors illustrate this better than most: healthcare and finance.

Healthcare: Clinical Safety Meets Intelligent Systems

Healthcare sits at the intersection of immense opportunity and enormous risk.

AI is already being used to support diagnostics, predict patient deterioration, optimise hospital operations, and automate administrative burdens. According to a report from GOV.UK, a major pilot of AI in the NHS demonstrated that AI-powered administrative support can significantly reduce backlogs and improve efficiency, with staff saving an average of 43 minutes per day. However, expectations for reducing errors in this context remain high.

When AI influences clinical decisions, questions of explainability, governance and accountability become critical. Models must be auditable. Outputs must be interpretable. Human oversight is non-negotiable.

In our own work supporting regulated environments, we’ve seen that the real complexity is not model performance. It is workflow integration. Where does AI sit in the patient journey? Who signs off on decisions? How is liability managed?

The opportunity is vast. So is the scrutiny.

Finance: Speed, Fraud and Competitive Pressure

Financial services face a different, but equally intense, dynamic.

AI is already reshaping fraud detection, credit scoring, risk modelling and customer service automation. The competitive advantage is clear: faster decisions, better risk prediction, and lower operational costs.

But regulators are watching closely. The EU AI Act and increasing global oversight are pushing financial institutions toward stricter model governance, transparency and fairness standards.

Here, AI cannot simply be innovative. It must be defensible.

Financial organisations are shifting from isolated AI features to broader, platform-level solutions—redesigning workflows with intelligence integrated at multiple stages rather than deploying single-purpose tools.

In finance, AI maturity will separate market leaders from laggards faster than almost any other sector.

The Four Shifts: From Experiment to Enterprise Value

Four clear shifts define the path from experimentation to true enterprise value.

1. From Tool to Workflow

AI must live inside the operational journey.

2. From Feature to Outcome

Business impact must be defined before development begins.

3. From Prompting to Process Design

Competitive advantage lies in orchestration and workflow, not just the model.

4. From Innovation Theatre to Measurable KPIs

AI investments must withstand financial scrutiny.

Success comes when organisations see AI as essential to product and operations—not just as a technical afterthought.

At Sonin, we identify where intelligence creates leverage and design AI-enabled workflows that integrate securely into live environments. We define outcomes first, map user journeys, and ensure governance from the start.

AI does not create value on its own. Intelligent product strategy does.

What Enterprise-Ready AI Actually Looks Like

As AI matures, so too must the way it is delivered.

Enterprise-ready AI has clear characteristics:

• It is integrated into core systems, not sitting in isolation.
• It operates within defined governance frameworks.
• It has transparent decision logic where required.
• It includes human oversight where risk demands it.
• It is continuously monitored and optimised.
• It scales with the organisation.

Crucially, it is tied to a measurable business objective.

The real divide is not technical expertise but how well AI aligns with business strategy and how well teams collaborate.

That is why AI transformation is not simply a development challenge. It is a business design challenge.

Assessing Your Enterprise Readiness

As we move further into 2026, the most important question is not whether you are using AI.

It is whether you are ready to rely on it.

Enterprise readiness requires clarity in four areas:

• Do you understand the operational problem in financial terms?
• Have you mapped the workflow in which the AI will operate?
• Do you have baseline metrics to measure improvement?
• Is governance defined and owned at the right level?

If those foundations are not in place, AI will struggle to move beyond experimentation. When these foundations are strong, AI quickly drives measurable business improvement.

For businesses serious about scaling AI, the next step is structured discovery, a clear view of where intelligence creates measurable value, how it integrates, and what operational change is required. That is where the real transformation begins.

The Next Phase of AI

The hype cycle is cooling. The real work is beginning.

The future of AI is about building robust, flexible infrastructure that delivers sustained value.

Healthcare organisations are embedding intelligent triage into live clinical pathways.

Financial institutions are redesigning risk engines around predictive systems.

Operational teams are automating entire decision chains rather than isolated tasks.

The shift from experiment to enterprise value is not automatic. It is intentional.

At Sonin, we help businesses move from AI curiosity to AI capability. Through structured discovery, product strategy, secure design and scalable development, we ensure intelligence is not just implemented, but embedded.

If you are exploring how AI can deliver measurable impact within your organisation, now is the time to move beyond experimentation. The question is no longer whether AI will shape your sector; it is whether it will. It is about designing your business to maximise its potential.