Rootstack AI Report 2026: From experimenting to transforming the business

Introduction

Artificial intelligence is no longer an emerging technology. It has become a new layer of enterprise infrastructure.

In 2025, 88% of organizations surveyed by McKinsey reported using AI in at least one business function. The adoption of generative AI also continued to grow, with 79% of organizations regularly using it in at least one function.

But there is an increasingly important difference between using AI and transforming a business with AI.

In 2026, the conversation is no longer only about which tools to use. The challenge is to turn artificial intelligence into productivity, growth, better customer experiences, new products, and sustainable competitive advantages.

The Rootstack AI Report 2026 examines eight trends shaping this new stage of enterprise AI.

1. From experimenting with AI to generating business value

The first stage of AI adoption was marked by pilots, proof-of-concepts, and individual tools. In 2026, the focus is shifting toward impact.

Stanford's 2026 AI Index reports that global corporate investment in AI more than doubled during 2025, while private investment grew 127.5%. Private investment in generative AI increased by more than 200%.

However, greater investment does not automatically mean greater returns. McKinsey found that although 88% of organizations are already using AI in at least one function, nearly two-thirds have not yet begun scaling AI across the enterprise. In addition, 64% report that AI is driving innovation, but only 39% identify an impact on enterprise-level EBIT.

This creates a new gap:

The gap between adoption and value.

Leading organizations will need to move from:

Experiment → Implement → Integrate → Scale → Transform

The question is no longer what AI can do. It is which business outcomes it can change.

2. AI is moving from productivity to growth

During the early years, the main promise of enterprise AI was doing more with less:

• Automating tasks.
• Reducing hours.
• Generating content.
• Analyzing information.
• Accelerating processes.

These applications remain important, but the value of AI is expanding.

The 2026 AI Index shows that companies are reporting benefits in innovation, customer satisfaction, competitive differentiation, profitability, and revenue growth.

PwC also found that between 2018 and 2024, industries more exposed to AI experienced 27% productivity growth, compared with 9% in industries less exposed to AI. They also recorded approximately three times greater revenue growth per employee.

This changes how ROI should be measured.

A mature AI strategy should evaluate:

• hours saved;
• operational capacity;
• speed;
• cost reduction;
• customer satisfaction;
• conversion;
• new revenue;
• innovation.

AI should no longer be justified only by how much less it costs to do something, but by how much more value it enables organizations to create.

3. AI Agents will change how work gets done

The next evolution of enterprise AI will not simply be AI that answers questions.

It will be AI that plans, executes, and coordinates actions.

Agents can receive an objective, access information, use tools, execute actions within defined boundaries, and escalate exceptions to a human.

Microsoft's 2025 Work Trend Index found that 81% of leaders expected agents to be moderately or extensively integrated into their AI strategies within the following 12–18 months.

This creates a new evolution:

AI as an Assistant
People do the work and AI helps.

↓

Collaborative AI
AI executes part of the process.

↓

Agentic AI
AI executes the process while people supervise exceptions.

This can transform areas such as:

• customer service;
• sales;
• human resources;
• finance;
• operations;
• marketing;
• IT.

The most important shift will be moving from automating tasks to automating entire workflows.

4. Data, integration, and architecture become the real differentiators

Companies have devoted significant attention to models:

Which model is better?

Which model reasons better?

Which model costs less?

But for an organization, there is a more important question:

What data and systems can AI work with?

An advanced model has limited business value if it cannot securely access:

• corporate data;
• CRM systems;
• ERP systems;
• databases;
• APIs;
• documents;
• internal knowledge;
• transactional systems.

Enterprise AI requires an architecture that connects:

Data + Applications + Models + Agents + APIs + Rules + Security + Observability

This is why, in 2026, technology modernization and AI strategy are becoming increasingly interconnected.

AI cannot indefinitely overcome the limitations of the architecture it is built on.

5. Governance becomes a competitive advantage

The more autonomy AI has, the greater the need for control. A chatbot that provides an incorrect answer may create frustration.

An agent that modifies information, executes a transaction, or makes an incorrect decision can create financial or operational consequences.

NIST recommends managing AI risks throughout the entire AI lifecycle and provides practices for developing trustworthy and responsible AI systems.

An enterprise AI strategy should consider:

• access control;
• permissions;
• privacy;
• traceability;
• evaluation;
• human oversight;
• security;
• compliance;
• observability.

Governance must be designed from the beginning, not added after a solution reaches production. Enterprise AI does not only need intelligence. It needs boundaries.

6. Talent and organizations are changing too

The conversation around AI and employment often focuses on which jobs will disappear. But the most immediate change is taking place in the skills required to work.

The World Economic Forum estimates that 86% of employers expect AI and information processing technologies to transform their businesses by 2030. Skills gaps also rank among the leading barriers to transformation.

PwC found that the skills required in jobs most exposed to AI were changing 66% faster than those in less-exposed jobs. In addition, workers with AI skills received an average 56% wage premium in 2024.

The worker of the future will not necessarily be the person who executes the most tasks.

It will be the person who can:

• define objectives;
• work with agents;
• validate results;
• interpret data;
• make decisions;
• design workflows;
• supervise automated systems.

Organizations will need to change as well.

The question will become:

What should a person do, what should an agent do, and where do we need collaboration between both?

7. Latin America has an opportunity, but must close the adoption-to-value gap

AI can have a particularly significant impact across Latin America. The region faces longstanding challenges related to productivity, digitalization, infrastructure, and talent availability.

The World Bank estimates that 30% to 40% of jobs in Latin America and the Caribbean are exposed in some way to generative AI. Between 8% and 12% of jobs could experience productivity improvements through GenAI, although digital infrastructure limitations could prevent up to 17 million jobs from benefiting from this potential.

The IDB identifies a similar situation at the business level: approximately 80% of companies in Latin America and the Caribbean already use AI tools, but only 23% report economic benefits and just 6% identify a significant impact.

This reveals a clear opportunity. Latin America does not necessarily have an AI adoption problem. It has a problem of scaling and capturing value.

Companies that successfully integrate AI with their processes, data, and systems can use it to increase capacity, productivity, and competitiveness.

8. The new model: Businesses designed to work with AI

All these changes point in the same direction. The future is not about continuously adding AI to existing processes. It is about redesigning processes around humans + software + AI agents from the start.

Rootstack proposes viewing enterprise AI maturity across five stages:

1. Explorer

Experiments with AI tools and pilots.

2. Adopter

Implements AI across specific functions.

3. Integrator

Connects AI with data, applications, and workflows.

4. Operator

Uses agents and automation in critical processes.

5. AI-Native Enterprise

Designs products, processes, and operations around AI.

Moving between these levels requires five capabilities:

Strategy + Data + Integration + Governance + Talent

Organizations that successfully combine these capabilities will have a much greater advantage than those that simply adopt more AI tools.

Conclusion

Enterprise artificial intelligence is entering a new stage. The first stage was experimentation. The second was adoption.

The third is integration and transformation.

The data available in 2026 shows a clear reality:

• adoption continues to accelerate;
• investment continues to grow;
• AI agents are entering workflows;
• AI is beginning to generate measurable productivity and revenue impact;
• skills are changing;
• governance is becoming critical;
• and Latin America has a significant opportunity to close productivity gaps.

But there is a difference between companies that use AI and those that transform with AI.

The first incorporate tools.

The second redesign how the business operates.

That is why the question for business leaders is no longer:

What can we do with AI?

It is:

What could we accomplish as a company if our processes, data, and teams were designed to work with AI from the beginning?

That will be one of the defining competitive advantages of the years ahead.