The year of applied AI: Building real AI value in 2026
The global move from AI experimentation to applied intelligence has accelerated, and companies are now expected to integrate AI into real operations and generate real value rather than experiment with isolated pilots.
Gartner has forecast that up to 40% of apps in the enterprise will have AI agents by 2026, while IDC has said that value is increasingly becoming the metric of business impact. The firm believes that in 2026, AI is going to become a ‘structural force of transformation’ where agentic AI is used for invention and automation.
This article explores how applied AI is changing company performance and why integration maturity is set to define how AI’s value is measured in 2026.
What this article answers:
- Why 2026 marks a shift from AI trials to production deployment.
- What leading analysts identify as the drivers of AI ROI.
- Why pilots stall without integration maturity.
- How Mint’s AI Factory supports adoption at scale.
- What leaders can prioritize to realize measurable outcomes?
Why AI value matters now
The past year has marked a turning point in how companies understand the work required to make AI deliver value. The focus is no longer on running isolated pilots or testing isolated models because they are expensive and low on return on value. Instead, meaningful outcomes have become the litmus test as they show how well AI is integrated into processes and the flow of operational work.
The evidence shows that value emerges when AI is implemented as part of the operating model rather than as a short-term technology experiment.
What does AI value look like inside the business?
Applied AI environments follow a consistent pattern. Data flows into operational systems and not just into isolated dashboards, and teams understand the context behind the data outputs. The insights generated by AI systems are linked directly to decisions and measurable outcomes, so AI strengthens existing processes rather than introducing parallel ones.
This behavior change is driven by the same pattern seen in data-literate environments, where teams improve decisions by discussing, challenging and applying insights rather than reviewing dashboards in isolation.
When AI outputs connect clearly to everyday work, organizations move from passive reporting to active, insight-led performance.
How Mint moves pilots into production
Mint’s AI Factory model focuses on embedding AI into the rhythm of work by ensuring that the implementation of AI is relevant and explainable. Instead of AI as a trend shoehorned into the organization, the right tools are chosen based on the lived reality of the organization. We ensure this by assessing the business across key areas such as data quality, integration maturity and the behaviors needed for effective adoption. These steps can involve workflow redesign, structured enablement or solutions built around clear, role-specific outputs.
The emphasis is on repeatability because that’s when the AI brings value. Mint builds reusable capability patterns that help organizations scale without rebuilding each solution from scratch. Over time, this improves adoption, strengthens confidence and supports measurable value across business functions.
Mint’s approach can help your company move away from isolated experimentation to consistent, production-ready AI capability that delivers business outcomes that are repeatable and relevant in 2026.
