The 10 Enterprise AI Trends for 2026: What Is Already Real, and What We Still Have to Wait For

The major studies from late 2025 and early 2026 describe a turning-point year for AI in business. We read them for you, and tested them against the Italian numbers.

One thread runs through every report this year: in 2026 artificial intelligence stops being an experiment and becomes a question of results. Almost every company now uses it, yet only a minority truly profits from it. In this article you will find the ten trends that matter, explained in plain language, and above all a question the global reports never ask: of these ten, which have genuinely reached Italian companies by mid-year, and which are still a work in progress?

What are the 10 enterprise AI trends for 2026?

For each trend: what it is in one sentence, and the figure that tells the story.

1. Agentic AI: AI that acts, not just answers

Until yesterday AI answered questions. Now it starts to do things: it carries out tasks, sees them through, and several "agents" work together. It is the number-one trend in every report, and also the trickiest. PwC reports that nearly 8 in 10 executives say they have already introduced agents. But there is a flip side: Gartner predicts that over 40% of projects will be abandoned by 2027, once costs rise and the value stays unclear. A lot of momentum, not yet much substance.

2. The "performance gap": AI is everywhere, value is with the few

If one figure sums up the year, it is this: 74% of AI's economic value ends up in the hands of 20% of companies (PwC's 2026 AI Performance study). McKinsey confirms it from the other side: only a minority sees a real impact on the books. In plain terms, buying AI no longer sets you apart; the edge is turning it into concrete results.

3. Rewiring: redesign processes, don't just add tools

Why do the best companies profit and the others don't? Not because they use more AI, but because they use it better: they rethink how work is done instead of bolting a chatbot onto the same old processes. McKinsey calls it "rewiring". Leading companies are twice as likely to redesign workflows; most, instead, still use AI only at the surface.

4. Governance and Responsible AI: rules become an advantage

For years, rules around AI were seen as a brake. In 2026 they become the condition for growing safely. The paradox, captured by EY, is that adoption runs faster than the rules: many companies use AI without adequate controls. Those that give themselves clear governance earn a concrete reward: employees who trust AI more and therefore use it more. Trust and automation grow together.

5. Decision Intelligence: from "looking at data" to "deciding with data"

For years, software showed what had happened. Decision Intelligence goes one step further: it helps you choose what to do now, comparing the possible scenarios. In January 2026 Gartner gave it its first official market ranking, the sign that it is no longer a niche. The companies furthest ahead are already automating far more decisions than the rest. We come back to it in the final focus, because it is the trend closest to the heart of the "performance gap".

6. Physical AI: AI leaves the office and enters the factory

Robots, drones and smart machinery bring AI into the physical world. According to Deloitte, nearly 6 in 10 companies already use some form of "physical" AI, with manufacturing and logistics leading, and the share is set to climb quickly. It is the trend that moves AI from the screen to the warehouse.

7. Domain-specific models: from generalist to tailor-made

The large "know-it-all" models give way to smaller, specialised ones trained on the language of a specific sector. Gartner predicts that by 2028 more than half of the models used by companies will be of this kind: more accurate on real tasks and cheaper to run. It is the technical ingredient that makes both agents and automated decisions reliable.

8. Sovereign AI: where your data sits matters too

With AI increasingly leaning on the cloud, companies start asking not only "which model do I use", but "where does my data end up, and who manages it". Deloitte finds that the vast majority now consider so-called sovereign AI important and weigh a vendor's country of origin. For Europe and Italy, the theme is intertwined with the AI Act, privacy and strategic autonomy.

9. Work and skills: a two-speed market

AI doesn't erase work, it splits it. PwC's jobs barometer (over a billion job ads analysed) shows two tracks: roles where AI amplifies the expert grow twice as fast and pay more. Those with AI skills are worth on average 62% more on the job market today. But mind the human factor: EY calculates that without training, companies leave up to 40% of the possible benefits on the table.

10. AI security: protect AI, and use it to protect yourself

More agents and more company data inside models mean more points to defend. Gartner predicts that by 2028 over half of enterprises will use security tools built specifically for AI. Deloitte sums it up in a formula: "AI for security, and security for AI". AI is both a defence weapon and a new target to protect.

Italy after two quarters: what has gone mainstream, and what is still being worked on

It is July, and two quarters are enough to see where Italian companies have actually put their money and where they are still testing. The Italian AI market is worth 1.8 billion euros in 2025, up 50% on the year before, and it is the fastest-growing piece of the entire digital market. But within that growth, not every trend weighs the same.

What has gone mainstream (and why it captured the investment)

GenAI on text and documents. This is Italy's real mainstream. Among companies using AI, 7 out of 10 apply it to read and extract information from text. Why did it win? A very low barrier to entry, immediate value and no need for perfect data: you switch on a tool and it works the next day.

Off-the-shelf copilots. 84% of large enterprises have bought licences for tools like Copilot, ChatGPT or Gemini, a 31% jump in a year. Why did it win? Predictable cost per seat, adoption driven from the bottom up (almost half of workers already use them), and a benefit you can feel on everyday tasks: writing, summarising, searching.

AI in marketing, sales and administration. These are the most widespread, fastest-growing areas, led by customer-service chatbots and automatic document reading. Why did they win? High-volume, low-risk processes where a mistake is easy to fix and the advantage shows up straight away.

The common thread: after two quarters, Italian investment went where AI touches text, content and the customer relationship, with fast returns and no major IT projects.

What is still being worked on (and why it hasn't taken off yet)

Agentic AI. It is the word of the year, but accounts for just 4% of the Italian market. Why is it behind? It needs clean data, redesigned processes and control rules almost no one has yet; and on important decisions a person is still there to supervise. Investment is in the testing phase, not at scale.

Governance and Responsible AI. Only 9% of large enterprises have structured AI governance. Why is it behind? The AI Act is still being rolled out, dedicated skills are scarce, and while AI stays at the surface, putting order in place feels postponable. Until you try to scale, and then it becomes urgent.

Widespread Decision Intelligence. The theme is on the agenda, but only about 38% of companies have a clear data strategy. Why is it behind? Automating decisions needs reliable, integrated data, and that is exactly what is missing. It is the country's number-one bottleneck.

Physical AI. Autonomous physical movement of machines involves less than 6% of Italian users, far below global levels. Why is it behind? It requires investment in plant and equipment, is concentrated in a few sectors and has longer payback times.

SMEs. They use AI in 15.7% of cases versus 53.1% of large enterprises, and the gap is widening. Why are they behind? A skills shortage (the number-one barrier), costs, and difficulty measuring the return. It is also why Italy overall stays at 16%, below the European average of 20%.

The rule, in short, is simple: what is already mainstream is whatever has a low barrier and a fast return, namely GenAI on text and documents, copilots and AI in marketing and administration. What is still under construction is whatever requires stronger foundations, namely agentic AI, governance, widespread Decision Intelligence, physical AI and SME adoption.

Focus: what is Decision Intelligence, and where is it applied?

Why everyone is talking about it in 2026

Among the ten trends, Decision Intelligence deserves a closer look because it is the one that made the sharpest jump: from emerging concept to recognised market category. In plain terms, it is software that helps you decide by combining data, analytics and AI. Classic business intelligence tells you what happened; Decision Intelligence focuses on a different question: "what is the best decision to make now, and with what consequences?", comparing the possible scenarios and making the trade-offs clear.

A simple way to place it: business intelligence describes the past, advanced analytics predict what will happen, Decision Intelligence helps you decide and automate the best choice, and generative AI acts as an accelerator, making everything faster and easier to grasp in natural language.

How big is the market

Estimates vary by scope, but the direction is clear: double-digit growth throughout the decade. The main research firms put the market between 13 and 15 billion dollars in 2024, with projections ranging from 36 to 50 billion by 2030. The strongest signal, though, came in January 2026, when Gartner devoted its first official ranking to the category, certifying its shift from niche to mature market.

Where it is actually applied

From documented use cases, Decision Intelligence is at its best on decisions that recur often and carry weight:

• Finance, credit and risk: automatically assessing loans and credit lines by weighing many variables at once, with auditable decisions in seconds.
• Fraud detection: one of the most mature areas, blocking fraud in real time before it completes.
• Supply chain and procurement: cutting excess inventory, avoiding stock-outs and balancing supply and demand.
• Pricing and retail: dynamic prices, personalised offers and assortments optimised store by store.
• Production and planning: adjusting plans to forecast demand and supporting both operational and strategic choices.

The common thread is that Decision Intelligence aims to orchestrate data, computation and AI within a governed system, designed for recurring decisions rather than isolated pilots. It remains a young category, with boundaries still settling, but its appearance in a dedicated ranking signals that the enterprise-AI debate is shifting from "which model to use" to "how to govern decisions".

Key takeaways

• In 2026 AI moves from experimentation to execution at scale. The frontier is agentic AI, but it is also the first risk of disappointment: Gartner predicts 40% of projects abandoned by 2027.
• The figure of the year is the performance gap: 74% of AI's economic value is captured by 20% of companies (PwC). What makes the difference is not how much AI you use, but process redesign, governance and a growth orientation.
• After two quarters, what is mainstream in Italy is whatever touches text, content and customers (GenAI on documents, copilots, AI in marketing and administration), because the barrier is low and returns are fast. Large-enterprise adoption at 53.1%.
• Still being worked on: agentic AI, governance, physical AI, widespread Decision Intelligence and SME adoption, all of which require integrated data, rules and skills. Italy overall sits at 16%, below the European average of 20%.
• Decision Intelligence is the trend that made the sharpest leap: double-digit market growth and Gartner's first ranking in 2026. Key areas: finance and risk, fraud detection, supply chain, pricing, production.
  

Frequently asked questions (FAQ)

What are the main enterprise AI trends for 2026?

The ten that emerge from the major studies by McKinsey, Deloitte, PwC, EY and Gartner are: agentic AI and multiagent systems, the performance gap between adoption and value, process redesign, governance and Responsible AI, Decision Intelligence, physical AI, sector-specialised models, sovereign AI and data provenance, work and skills transformation, and AI security. The four themes cited by all sources are agentic AI, the value gap, governance and skills.

Why do most companies fail to get value from AI?

Because the gap is not technological but methodological. Per PwC, 74% of value is captured by 20% of companies: leaders don't use more AI, they redesign processes, invest in governance and aim for growth, not just cost cutting.

Which AI trends are already mainstream in Italian companies in 2026?

GenAI applied to text and documents, off-the-shelf copilots present in 84% of large enterprises, and AI in marketing, sales and administration. Among large enterprises, adoption has passed the halfway mark (53.1%).

Which AI trends in Italy are still lagging?

Agentic AI (only 4% of the market), structured governance (9%), physical AI (under 6%), widespread Decision Intelligence (held back because only around 38% have a clear data strategy) and SME adoption (15.7% versus 53.1% for large enterprises).

What is Decision Intelligence, and why is it being talked about in 2026?

It is the discipline, and software category, that combines data, analytics and AI to support and automate business decisions. It is much discussed because the debate is shifting from "which model to use" to "how to govern decisions", and because in January 2026 Gartner gave it its first official market ranking.

In which business areas is Decision Intelligence applied?

Mainly on recurring, high-impact decisions: finance, credit and risk; fraud detection; supply chain and procurement; pricing and retail; production planning.

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