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When AI Readiness Meets ROI Reckoning

5 min read

2026 is the year AI readiness meets ROI reckoning, when AI will need to be treated as a strategic capability, not a perpetual pilot.

Leaders must demonstrate ROI, enhance governance, and operationalize AI at scale or risk falling behind competitively. Our insights outline the essential lessons from thousands of leaders on what it takes to succeed in 2026.

Companies that break out of pilot mode and scale strategically are 3x more likely to exceed ROI expectations.

Key trends across 5 stages of maturity

Where does your organization stand in AI maturity?

Use the chart below to benchmark where you are among your peers.

Below we dive deeper into the lessons learned from our yearlong series of surveys across AI maturity levels, challenges, and actions organizations can take to unlock ROI.

Breaking pilot perpetuity

Most organizations remain in the experimentation stage

Companies that remain in Stage 2: Experimentation are five times more likely to report returns below expectations and are far less likely to exceed them.

Scaling AI beyond pilots, by integrating it into workflows, processes, and platforms, is a key differentiator between experimentation and transformation.

Becoming AI ROI‑positive

Faster operationalization is the best path to positive AI ROI

In some cases, however, organizations may need to initially redefine what “return” and AI success measurement means. Our research reveals that firms achieving positive ROI do not merely focus on cost-cutting; instead, they integrate AI into broader areas such as growth, customer experience and workforce augmentation.

AI optimization barriers

Overcoming challenges to AI optimization

The biggest obstacles to AI optimization remain consistent:

Systems integration and data connectivity
Use-case clarity and value articulation
Talent, skills and enablement
Security/compliance/ regulatory guardrails
Technology/platform limitations

These barriers slow progress across all maturity levels and prevent organizations from moving from experimentation into scaled optimization.

Agentic AI complexity

Preparing for continuing agentic AI complexity

Last year, we observed a significant shift in the AI landscape, from the use of single AI agent assistants to orchestrated multiagent teams that manage end-to-end processes, enhancing efficiency and oftentimes improving legacy processes. We expect the rapid advancements in the complexity of agentic AI to continue.

To manage the rising complexity of AI agents, leading organizations are establishing AI agent governance boards (AGB). Access the full paper to learn more.

Regulatory clarity and AI trust

Regulatory ambiguity continues to hinder AI advancement

Compliance and security restrictions are the top challenges related to data across all levels of maturity, and organizations that operate in multiple jurisdictions encounter significant complexity as requirements evolve. To scale AI safely and responsibly, it is essential to embed governance by design, including human-in-the-loop controls, auditability, clearly defined data boundaries, and comprehensive risk engineering.

FAQs

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What is the biggest barrier preventing organizations from achieving AI ROI?

Organizations struggle most with systems integration, data connectivity, unclear use cases, talent shortages, and compliance hurdles—factors that prevent AI from moving beyond pilot projects and delivering measurable return.

Why do so many companies remain in "pilot mode"?

Many organizations explore AI capabilities without integrating them into core business processes. This limits automation, data feedback loops, and cross functional impact—key drivers of ROI.

What are the five stages of AI maturity and which stage delivers the most ROI?

The five stages are Initial, Experimentation, Defined, Optimization, and Transformation. The greatest ROI occurs in Stages 4–5, where AI is scaled across the enterprise.

How does agentic AI improve business performance?

Agentic AI automates multistep workflows, augments human decision-making, and orchestrates end to end processes. Mature organizations use multiagent frameworks to improve efficiency, quality, and speed.

Why is AI governance important for scaling AI safely?

Strong governance—including AI Agent Governance Boards—ensures transparency, compliance, risk controls, and oversight. It prevents fragmented AI deployments and supports responsible scaling.

Which KPIs should organizations use to measure AI ROI?

Organizations should measure AI ROI using a broader set of outcome‑based KPIs—such as productivity, revenue growth, customer or employee satisfaction, time‑to‑market, and decision quality—rather than relying on cost savings alone and explicitly link these metrics to business outcomes like growth and agility.

How do AI ROI challenges differ across industries?

AI ROI challenges vary by industry. Financial services face regulatory hurdles, healthcare struggles with fragmented data, manufacturing battles legacy systems, tech requires scalable architectures, and the public sector contends with silos and security demands. These factors make strong data foundations and governance-by-design essential for success.