Generative AI for Enterprises in 2026: Trends, Tools, and Risks

2025 was a roller-coaster year for artificial intelligence. Model races, everyday releases, disappointing generative AI pilots’ success statistics, talks about artificial general intelligence (AGI), and even superintelligence (ASI) – all of these raised questions about the abilities and limits of AI that businesses wanted the answers to.

According to the 2025 survey report by Cloudera, 96% of enterprise IT leaders stated that AI is at least somehow integrated into their processes, with generative AI being the primary model in 60% of cases. Such a level of AI adoption leaves no doubt that companies want to implement AI. But many of them are still lost in the opportunities it can provide.

In this article, we’d like to provide an overview of what generative AI has to offer for enterprises in 2026 and which of the trends you can confidently follow. So, if you want to learn whether agentic AI is as powerful as they say, what new AI regulations we can expect, and how government investment in sovereign models may affect multinational enterprises, just keep reading.

How Did 2025 Treat Generative AI in Enterprises?

Apart from generative AI being the main part of enterprise AI adoption strategies, we could also see a few other important trends.

A survey of the state of AI in 2025 conducted by McKinsey revealed that those who work in technology, media, and healthcare use AI agents more often and in more cases compared to other industries. Respondents in the same survey identified the main value of artificial intelligence as innovation, employee and customer satisfaction, and a competitive edge.

Marketing and sales, corporate finance, and development company units experienced the highest revenue increase in 2025, caused by AI implementation.

According to the ISG State of Enterprise AI Adoption from September 2025, the majority of AI use cases are still focused on classification and recognition (33%), and optimization (26%), which is explained by the fact that those AI applications have less risk and higher accuracy. However, content generation and data summarizing appeared to be growing categories, constituting 17% of all mentioned cases.

The average spent per use case was $1.3M, but despite large investments and overall positivity around AI implementation, the majority of enterprise AI initiatives in 2025 were still at the pilot stage, with around 31% of them reaching full production.

These numbers convey a pretty clear message: generative AI adoption in enterprises was scaling. But how exactly was that? Let’s talk more practically.

The main enterprise generative AI use cases from 2025

The last year widened the range of use cases for generative AI and paved the way to the full-scale automation of enterprises. So, here’s how businesses leveraged the power of AI models across their departments.

Generative AI in marketing and sales

There are many generative AI tools that can help with the creation of content, and that was one of the first, the most obvious uses gen AI got famous with. It gained particular popularity among marketing and sales teams. However, 2025 was more creative about how to use AI technology for brand promotion and lead generation:

• Many businesses started automating personalized marketing content creation at scale, so AI assistants could now generate email sequences, social media posts, and even entire marketing campaigns made for specific customer segments based on processing data from previous interactions
• AI-powered search enabled sales and marketing teams to access and use data more accurately for market research and strategy creation, with platforms extracting relevant data from the enterprise context to inform targeting decisions
• Sales teams began applying generative AI for generating job descriptions, proposal writing, and designing personalized outreach messages that resonated with prospects’ specific pain points
• Virtual assistants started handling repetitive tasks like follow-up scheduling and lead qualification, freeing up human teams to focus on relationship-building

Generative AI in customer support

Thanks to the natural language processing powers of generative AI, companies have already been using it to resolve customer support tickets for a few years. The trend didn’t change, but the scale and the quality did. In 2025, that’s what worked for customer care in enterprises:

• Virtual agents became smarter, with a better understanding of the context: they could now reference previous customer interactions and proprietary data to provide truly personalized support
• They stopped being rule-based and started making decisions based on data, according to the instructions, powered by advanced machine learning algorithms that could recognize patterns in customer issues
• Agents became triggers to other workflows, connecting customers with businesses more and ensuring the relevance and speed of provided support. For instance, a conversational AI system could automatically escalate complex issues while handling routine inquiries independently
• Enterprise generative AI tools integrated easily with existing business processes, allowing customer support teams to access customer data across multiple platforms through a single AI assistant

Generative AI in code generation

Software development teams experienced perhaps one of the most dramatic transformations with the adoption of generative AI in 2025. What started as simple code autocomplete evolved into sophisticated AI agents that could understand entire codebases and participate in the development lifecycle:

• Developers began using generative AI applications to automate routine tasks like writing unit tests, debugging, and refactoring legacy code
• Enterprise generative AI tools like GitHub Copilot and similar platforms became standard in development environments, helping teams improve efficiency by handling boilerplate code while developers focused on complex architecture decisions
• AI systems trained on proprietary data helped maintain coding standards specific to each organization, ensuring consistency across large development teams
• The integration of generative AI capabilities into IDEs meant that code generation became contextually aware: the AI could recommend solutions based on the specific frameworks, libraries, and patterns already in use within a project

Generative AI in knowledge management

One of the most transformative use cases of generative AI in 2025 was how enterprises approached knowledge management. Large language models finally broke the code on making institutional knowledge accessible and actionable:

• Companies implemented enterprise AI platforms that could search across documents, emails, wikis, and databases using natural language queries, effectively creating an intelligent search engine for corporate knowledge
• Google Cloud’s Document AI and similar generative AI solutions helped businesses extract and organize information from unstructured documents, making years of accumulated data suddenly usable
• AI platforms enabled employees to ask complex questions about business functions and receive answers synthesized from multiple sources, with citations back to the original materials
• Personal productivity tools powered by gen AI helped employees quickly find relevant data without navigating complex folder structures or remembering exact document names
• The implementation of responsible AI practices ensured that confidential data remained protected while still allowing employees to leverage AI for knowledge discovery

Generative AI in business operations

Beyond specific departments, 2025 saw generative AI technologies embed themselves into core business processes across enterprises:

• Organizations started incorporating generative AI into workflow automation, with AI agents handling everything from invoice processing to contract analysis
• Data analysis became more accessible to non-technical teams through conversational interfaces: employees could ask an AI system to analyze trends or create reports without knowing SQL or Python
• AI applications helped reduce operational costs by automating tasks that previously required manual effort, from data entry to quality control
• The most successful generative AI platform implementations were those that could work with training data specific to each company’s unique processes and terminology
• Machine learning models began working alongside neural networks to not only generate content but also learn from how employees interacted with AI-generated content, continuously improving their outputs

Generative AI in the infrastructure

What made all this possible was the maturing of enterprise generative AI tools and platforms. Companies moved beyond experimenting with consumer-facing generative AI tools and started building robust AI solutions designed for enterprise needs:

• Platforms like Vertex AI provided the infrastructure for companies to build and deploy their own generative AI capabilities using their own data
• The emergence of specialized AI apps for different business functions meant that adopting generative AI didn’t require building everything from scratch
• Integration between generative AI systems and existing business software became smoother, allowing AI assistants to pull information from CRM systems, ERPs, and other core platforms
• The conversation around responsible AI practices matured, with enterprises implementing governance frameworks to ensure their AI application deployments were ethical, transparent, and compliant

The future of generative AI in enterprises became clearer in 2025: it wasn’t about replacing humans but about augmenting human capabilities. By leveraging generative AI to execute repetitive tasks and analyzing data, employees gained time to focus on creative problem-solving and strategic thinking. This practical foundation set the stage for what we’re seeing in 2026.

9 Turns Enterprise Businesses Will Take with Generative AI in 2026

2026 is predicted to be the year when AI reshapes enterprises. More investments, more revenue, and more innovations are predicted according to reports from Deloitte, IBM, McKinsey, and Gartner, as well as from independent AI researchers and analysts.

We’ve read them all, so here’s an overall picture of what to expect from AI in 2026:

1. 74% of companies plan to deploy agentic AI within two years
2. Model-based approaches to generative AI adoption will be replaced with multi-model systems
3. AI interactions will go beyond chat into personalized user interfaces
4. 54% expect to move 40% of their AI initiatives from pilot stage to production in the first half of the year
5. Multimodal AI will become more advanced, reaching close to the human perception level
6. Edge AI applications are projected to reach 80% in the next two years
7. More countries will implement AI and cybersecurity regulations
8. AI sovereignty will matter more than ever
9. Companies will invest more in employee AI education

These are the main predictions about generative AI in enterprises. Each of them will, one way or another, affect enterprises in 2026. But to understand how exactly they fit into your AI strategy, we need to go beyond the numbers and dive into each case individually.

How Will AI Workflow Automation Co-Exist With Agentic AI?

For the longest time, agentic AI was on everyone’s lips in Silicon Valley. From OpenAI and Anthropic, to NVIDIA and Microsoft leaders – everyone believes that one day our lives will be partially or fully automated. And it seems like this day is so much closer in 2026.

Today, most enterprises use AI to automate their processes. And while it’s proven to increase productivity, save costs, and promote faster decision-making, it’s limited to predefined scenarios.

Agentic AI, in turn, represents a more proactive and agile form of automation, where the agent doesn’t just follow a simple instruction but can adapt and take actions based on real-time data through deep reasoning. This makes it more independent, requiring less human interaction.

And what is generative AI’s role here? Well, generative AI, with all of its popularity, is a reactive technology that alone cannot automate anything. It reacts to your request and generates the answer, whether it’s text, image, video, audio, or code.

They’ve learnt the statistical relationships between words and between pixels and between waves. And they’ve learned that from massive data sets. So when you provide a prompt, GenAI predicts what should come next based on its training, but its work does end at generation. It doesn’t take further steps without your input.

Martin Keen, Generative vs Agentic AI: Shaping the Future of AI Collaboration

Thus, generative AI and agentic AI are two different ways AI approaches systems. However, both generative and agentic AI share a common foundation: Large Language Models (LLMs). LLMs are responsible for reasoning in agentic AI: the ability of a system to make decisions on its own.

So, how will generative AI, workflow automation, and agentic AI co-exist? Enterprises will move towards big automation systems, where:

• Generative AI helps with data analysis, summarization, content creation, and reasoning
• AI workflow automation excels in automating repetitive tasks that don’t require agility and live data-based decisions, and can be handled according to predefined rules
• Agentic AI will make enterprise automation more independent and effective, leaving space for minimal human interaction at only critical points

Such systems can be custom-created and integrated into enterprise networks or developed with the use of agent builders.

Here’s the comparison of the most popular options for building AI automation agents and systems:

The Essential Q&A for Strategic Implementation of AI in Enterprises

In 2025, most organizations treated AI as a series of isolated experiments, but in 2026, a shift toward complex orchestration and high-stakes production began. Moving past simple chat interfaces requires a move toward multi-model systems, autonomous agents, and “sovereign” infrastructures that prioritize data security over convenience.

To successfully scale these technologies, leadership must address the friction between rapid technical adoption and the practical realities of regulation, job restructuring, and data quality. Let’s break down the essential questions your team must answer to transition from fragmented pilots to a unified, production-ready AI strategy.

Why sticking to only one AI model will no longer be the right move?

According to IBM, companies won’t stick to a single model, but will instead integrate the capabilities of different models into systems.

Relying on one single model isn’t the strategy anymore. 2026 will become a year when enterprises invest in the orchestration of more complex and capable AI workflows, where a few smaller models, each responsible for automating specific tasks, cooperate.

What’s more, combining the capabilities of different generative AI models can benefit the effectiveness of enterprise AI systems, since each model excels in different types of work.

To make it easier for you to choose the models for your priority assignments, here is a brief comparison of the main generative AI model providers:

You can find more tech specifics of these and other models here.

If you need more information to understand what models can help your specific cases, Glorium Technologies also offers AI consulting services.

How will generative UI help enterprises work faster and more efficiently?

The idea of AI going beyond chat interface to more immersive and personalized experiences is expected to take shape in 2026. Google has already introduced the gen UI concept for its products in November 2025, and more and more companies will implement generative interface capabilities into their systems.

For enterprises, this shift will bring:

• More personalized experience for customers
• Faster prototypes for new products
• More interactive dashboard for better analytics

Will adopting generative AI finally move from pilots to production?

A McKinsey survey from the last quarter of 2025 discovered that only 7% of organizations had fully deployed and integrated AI technology, while 62% are still at the experimenting or piloting stages. But this is something that will change for enterprises in 2026.

The main driver for the shift will be more structured, high-quality data. So far, 80%–90% of enterprise data was unstructured, and is believed to be the main reason why AI initiatives couldn’t go past the pilot stage, according to the latest Salesforce State of Data and Analytics report. But this year, with more investments into data preparation, companies will finally be able to deploy and scale their AI systems.

How can multimodal AI technology adoption benefit enterprises in 2026?

Aaron Baughman, IBM Fellow and Master Inventor, believes that multimodal AI will drastically improve in 2026 and reach a nearly human level of data understanding and perception.

This will allow for more independent multimodal AI agents that can autonomously perform various tasks due to their abilities to receive not only textual and numeric, but also visual and audio information.

For enterprises, this can mean:

• Better internal knowledge management
• Faster and more pleasant customer support
• More accurate analytics and decision-making
• Opportunity to delegate more complex tasks to artificial intelligence

Which industries will heavily invest in edge AI?

According to Deloitte, global edge AI, or, in other words, physical AI adoption, is projected to grow from 58% to 80% within two years, with manufacturing, logistics, and defense industries leading the way.

The main applications are expected to be:

• Intelligent security systems and smart monitoring (21%)
• Collaborative robotics (20%)
• Digital twins (19%)
• IoT-driven retail (16%)
• Autonomous logistics (13%)
• Smart materials (7%)

What new AI regulations can we expect?

The EU AI Act, presented in 2025, was just the beginning of a more regulated AI use. In 2026, many countries, including member states of the EU, are expected to establish AI governance and provide specific guidelines regarding AI that they expect from companies operating within their digital borders. In this manner, Italy, for example, has implemented special additions to the EU AI Act.

The government of the U.S. is yet to debate about whether and how AI regulations will be established, but separate states, like Colorado and California, have already listed expectations from companies using AI in such industries that work with “consequential decisions” like healthcare, employment, or legal.

Will sovereign AI become a new trend for enterprises?

Even though AI laws provide some level of security, relying on technology that stores and processes data in another state might leave space for regulatory loopholes, and thus may raise ethical concerns, endanger citizens’ privacy, or even national security.

According to Gartner, in order to enhance independence and guard against extraterritorial regulatory intrusion, 65% of governments globally will implement some sort of technical sovereignty standards by 2028.

Deloitte’s State of AI report has already shown that reliance on foreign-owned AI infrastructure and technologies is a concern for at least 66% of businesses. And for enterprises, where compliance and data security are a must, sovereign AI may become a big trend in 2026.

Will enterprises need to invest more in AI education for their workers?

McKinsey’s AI in the Workplace report from 2025 revealed that 48% of employees place a significant role in AI adoption on internal AI education. Yet, half of them felt like the support they got was moderate or less.

This is also backed by Deloitte. They found that 53% of companies simply raise overall awareness of AI, and 83% haven’t made any changes to jobs around AI capabilities.

To enable full AI adoption and finally move from experiments and pilots, enterprises will need to significantly invest in generative AI education to fit the new reality.

Each department requires practical training on integrating generative AI tools into its workflows:

• Marketing and sales teams: Learn prompt engineering for personalized content, leverage AI-powered search to analyze customer data, and determine when to use AI assistants versus human creativity
• Software development teams: Apply code generation best practices, understand large language models, and implement responsible AI practices when developing applications
• Customer support teams: Understand how conversational AI manages customer interactions and identify when to assign tickets to AI agents versus when human judgment is required
• Data and analytics teams: Use generative AI to analyze data and validate AI-generated insights. These teams often become advocates for generative AI within the organization
• Leadership and management: Evaluate AI solutions based on ROI, understand governance frameworks, and lead organizational change as business functions evolve with AI.

But education alone won’t be enough. Beyond teaching employees how to use AI tools, enterprises will need to restructure jobs to align with what AI capabilities can actually handle. According to Deloitte, 83% of companies haven’t done this yet. Here’s what the process looks like:

• Map AI responsibilities: Identify repetitive tasks in each role that AI can automate, such as data entry, initial drafts, scheduling, routine research, and basic customer inquiries
• Redefine roles for higher value: Shift human focus to tasks requiring creativity, complex judgment, and relationship building. For example, sales teams prioritize negotiations over proposals, customer support manages complex escalations, and developers concentrate on architecture rather than routine code
• Adjust success metrics: Move away from measuring employees by volume and speed on tasks now managed by AI. Instead, focus on decision quality, customer satisfaction, and effective use of AI to enhance impact
• Involve employees in the transition: Engage teams in determining which tasks AI should manage and which require human involvement. This approach reduces anxiety and helps identify the most valuable uses of enterprise AI

Companies that restructure jobs around AI capabilities will enable employees to focus on more meaningful work while AI assistants manage repetitive tasks. This is when AI adoption fulfills its promise of operational efficiency and employee satisfaction.

Are there any enterprise generative AI adoption risks and challenges to be aware of?

The future of generative AI in enterprises looks bright. With the right strategies, businesses will be able to embrace automation, improve processes, and increase ROI. However, there are no wins without a battle. And to fully adopt AI, enterprises will need to have a few.

According to Statista, three main barriers to AI adoption in 2025 were the lack of skills to support adoption, the lack of vision among managers and leaders, and the high costs of available AI products and services.

The no-skill gap seems to be easy to address: you just need to hire the right talent. But what is the right talent, and where to find them? To simplify this for you, we’ve prepared a guide on how to hire development teams, which will help you understand which experts you need and what hiring approach to take.

The lack of vision is something more complicated. It’s not enough to just want AI in your enterprise. You need to have a problem AI can resolve, or a process AI can improve. What might give your company a clearer picture of where gen AI might be the most beneficial is looking at AI use cases and seeing which of them can be applied to your enterprise.

Finally, AI development costs can be adjusted based on your priorities. You don’t have to invest billions right away or use the most expensive model or services to succeed. What matters is the right strategy and using AI where it fits.

What Enterprise AI Solutions Will Companies Implement in 2026

Having reviewed these nine trends, you can now consider their implications for enterprise generative AI tools and solutions you may develop or adopt.

These trends are shaping a new landscape for generative AI applications that extend well beyond basic chatbots and content generators. By 2026, enterprises will invest in several key categories of AI solutions, each targeting specific business processes and operational challenges.

Intelligent automation platforms

They will integrate traditional workflow automation with generative AI and AI agents. These platforms will adapt to changing conditions and make real-time decisions by analyzing data, rather than relying solely on predefined rules. For example, such systems could process customer support tickets or manage supply chain logistics, using natural language understanding to interpret requests and machine learning to optimize outcomes.

Enterprise knowledge engines

Such technology will change how companies access and utilize proprietary data. These generative AI solutions will search across documents, emails, databases, and video recordings to provide relevant information as needed. Unlike traditional search engines, these AI-powered tools will understand context, synthesize information from multiple sources, and generate summaries or reports based on their findings.

Specialized AI copilots for various business functions

This will become standard. Marketing teams will use AI assistants to analyze customer data and create personalized content across channels. Sales teams will leverage AI to draft proposals, prepare for meetings, and identify high-potential leads. Software development teams will collaborate with AI agents for code generation, testing, and documentation, allowing developers to focus on architecture and complex problem-solving.

Multimodal AI systems

Multimodal will process and generate content across text, images, audio, and video. These technologies will enable use cases such as visual quality control in manufacturing, sentiment analysis that considers both content and tone in customer interactions, and automated content creation for marketing campaigns across multiple formats.

Vertical-specific AI solutions

Solutions that are tailored to industries like healthcare, finance, and retail will become more prevalent. These solutions will understand industry terminology, comply with sector regulations, and integrate with specialized software. For example, a healthcare AI solution would be trained on medical literature and clinical workflows, while a financial services AI platform would address regulatory requirements and accounting principles.

Hybrid AI architectures

They combine cloud-based and edge processing and will become more common. Enterprises will deploy AI systems that run locally on devices for sensitive data or real-time decisions, while using cloud resources for complex analysis. This approach addresses privacy concerns and operational efficiency.

Composable AI platforms

These platforms will enable enterprises to build custom solutions by combining different AI capabilities as building blocks. Rather than purchasing monolithic AI applications, companies will assemble generative AI systems using components for natural language processing, data analysis, content creation, and decision-making, all coordinated through a central platform.

The convergence of agentic AI, multimodal systems, generative interfaces, and other trends is creating opportunities that were not possible even a year ago. The priority now is to identify which enterprise generative AI tools align with your specific challenges and to translate these trends into actionable strategies for your organization.

How to Navigate Generative AI Adoption For Enterprises in 2026?

All of these trends for generative AI adoption in enterprises, as well as potential obstacles, show us one important thing: it doesn’t matter what AI you use; it matters how and where. The models won’t matter anymore; systems will. And to build a system that can automate your enterprise efficiently and securely, you will need the right partner near you.

At Glorium Technologies, we can help you address your unique generative AI adoption challenges and turn them into secure and scalable solutions. Reach out to us to get professional assistance from an experienced AI development team.

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