Artificial intelligence is moving into a new phase in 2026. Businesses are no longer using AI only to generate text, summarize documents, or answer customer questions. A growing number of organizations are now experimenting with agentic AI—AI systems designed to plan tasks, make decisions, use digital tools, and carry out multi-step workflows with varying levels of human supervision.
The shift could significantly change how companies operate.
McKinsey’s Global Tech Agenda 2026 found that leading technology executives are increasingly incorporating AI and data directly into their companies’ operating models. The research, based on a survey of more than 600 technology and business leaders, highlights agentic automation as one of the areas companies are investing in to create measurable business value.
Businesses interested in the wider development of artificial intelligence can also read our coverage of how the Chinese AI model DeepSeek shook the global technology industry.
What Is Agentic AI?
Agentic AI refers to artificial intelligence systems capable of pursuing a goal through a sequence of actions rather than simply responding to a single prompt.
A conventional generative AI chatbot might answer a question or draft an email. An AI agent could potentially receive a broader objective, determine what steps are required, interact with different tools or systems, evaluate the results, and continue working until the task is completed or human intervention is required.
For example, instead of asking an AI system to write a sales email, a business could use an AI agent to:
- identify potential customers;
- research each company;
- prepare personalized outreach;
- update customer relationship management records;
- schedule follow-up activities;
- analyze responses; and
- recommend the next action.
This represents a major shift from AI as an assistant toward AI as an active participant in business workflows.
Why Agentic AI Is Becoming Important in 2026
The biggest opportunity is not simply faster content generation. It is the possibility of redesigning entire workflows.
McKinsey reported in February 2026 that forward-looking CIOs are investing in agentic automation while moving toward product- and platform-based operating models. According to the firm, top-performing companies are increasingly treating technology as a source of business value rather than simply a cost center.
The trend is part of a much wider AI infrastructure race. OpenAI, Google, Microsoft, Nvidia and other major technology companies are investing heavily in the computing infrastructure needed for increasingly advanced AI systems. Business Innovative News previously reported on OpenAI renting Google AI chips to expand ChatGPT infrastructure.
1. AI Agents Could Automate Multi-Step Business Processes
Traditional automation works particularly well when tasks follow predictable rules.
For example: If an invoice arrives → extract the amount → enter it into accounting software.
Agentic systems aim to handle more complicated situations. An accounts-payable agent, for example, could eventually receive an invoice, verify the supplier, compare it with a purchase order, identify discrepancies, request clarification, route the invoice for approval and update the accounting system.
Instead of automating one isolated action, organizations could potentially automate significant parts of an entire workflow. McKinsey says agentic AI is moving beyond simple rule-based automation toward work involving greater levels of judgment, creating opportunities across global business services and other corporate functions.
Read McKinsey’s Agentic AI and the Future of Global Business Services
2. Small Teams May Be Able to Build Companies Faster
The impact could be particularly significant for startups.
AI-native companies can increasingly build workflows around specialized agents performing tasks in marketing, software development, sales, financial analysis and customer service.
Humans would still determine company strategy and oversee important decisions, but AI could allow a relatively small workforce to manage considerably more operational work.
This creates an opportunity for entrepreneurs while also increasing competitive pressure on established businesses.
3. Customer Service Could Move Beyond Chatbots
Customer-service AI has traditionally focused on answering frequently asked questions.
Agentic AI could take the process further. Instead of simply explaining how to change a reservation, an authorized agent might eventually check availability, modify the booking, send confirmation, update the customer record and escalate unusual cases to a human employee.
The same model could be applied to banking, insurance, ecommerce, travel and subscription businesses.
The increasing use of AI chatbots already demonstrates how rapidly users are becoming comfortable interacting with automated systems. Business Innovative News has covered this trend in its report on AI chatbots being used for health advice in the UK.
4. Marketing Operations Could Become More Autonomous
Marketing departments already use generative AI extensively for content creation.
Agentic systems could connect several stages of marketing into a continuous workflow.
An AI marketing agent could potentially:
analyze campaign performance, detect declining conversion rates, examine customer segments, propose new campaign ideas and generate reports explaining which channels are producing the strongest results.
Multiple agents could also work together.
One could research competitors while another analyzes customer behavior and a third prepares campaign concepts.
Humans would remain responsible for strategic positioning, brand decisions and high-risk actions.
5. Sales Teams Could Use Agents Throughout the Customer Journey
Sales is another area with significant potential.
AI agents could help businesses research prospects, qualify leads, summarize meetings, prepare proposals, update CRM systems and identify accounts that need follow-up.
This could reduce administrative work for sales representatives and allow them to focus more heavily on relationships, negotiation and complex purchasing decisions.
6. Finance and Accounting Workflows Could Change
Finance departments contain many processes that involve collecting and comparing information from multiple sources.
Agentic systems could potentially support:
invoice reconciliation, expense classification, financial reporting, cash-flow monitoring, anomaly detection and management reporting.
However, financial operations also demonstrate why businesses need strict AI controls.
An AI agent should not automatically receive unlimited authority to make payments or modify important financial records.
Companies will need clearly defined authorization levels, audit trails and human approval requirements.
7. Software Development Could Become More Agent-Driven
Software development is already one of the most visible areas for AI agents.
Instead of merely suggesting snippets of code, advanced development agents can increasingly examine codebases, create files, run tests, identify errors and propose fixes.
Agentic development could therefore allow companies to build digital products faster.
However, software engineers remain important for architecture, security, quality assurance and translating real-world business requirements into reliable systems.
The broader AI industry is also undergoing significant commercial and legal changes. For example, Business Innovative News has been following the ongoing Elon Musk vs OpenAI legal battle and its potential impact on the AI industry.
Agentic AI Could Force Companies to Redesign Their Operating Models
Perhaps the biggest transformation will happen at the organizational level.
McKinsey’s latest research argues that companies generating value from AI are not simply adding AI tools to old processes. They are making deliberate changes to how their operating models work. Agentic AI can influence decision-making, organizational structure and how work moves between humans and machines.
Read McKinsey’s research on AI operating models
Organizations may therefore need to reconsider:
who owns AI agents, who approves their actions, which decisions must remain human, how activity is audited and how employees collaborate with automated systems.
Simply purchasing AI software will not answer those questions.
The Data Challenge
Agentic AI depends heavily on reliable business data.
An agent cannot consistently make useful decisions if customer records are incomplete, databases conflict, permissions are unclear or internal information is scattered across incompatible systems.
McKinsey reported in April 2026 that nearly two-thirds of enterprises worldwide had experimented with AI agents, but fewer than 10% had scaled them to deliver tangible value. Eight in ten companies cited data limitations as an obstacle to scaling agentic AI.
Read McKinsey’s Building the Foundations for Agentic AI at Scale
This suggests that the competition to adopt AI agents could also become a competition to develop better data infrastructure.
Companies with organized, accessible and well-governed proprietary data may have a major advantage.
Security and Governance Will Become Critical
Giving AI systems the ability to act introduces risks that ordinary chatbots do not face.
Businesses will need to consider:
Access control: What systems can an AI agent access?
Authorization: What actions can it perform without human approval?
Data privacy: What customer or company information can it see?
Auditability: Can the company review everything the agent has done?
Cybersecurity: Could an attacker manipulate an agent into taking unauthorized action?
Accuracy: What happens when the system makes the wrong decision?
Accountability: Who ultimately takes responsibility for an AI-generated decision?
These questions become much more important as agents receive greater autonomy.
Agentic AI Will Change Jobs, but Human Judgment Remains Important
The adoption of AI agents will inevitably change some job responsibilities.
Routine digital tasks are particularly likely to become automated or partially automated.
At the same time, organizations will need employees capable of designing workflows, supervising agents, validating results, handling exceptional situations and making decisions requiring contextual judgment.
The workplace may therefore move toward a model in which employees manage combinations of software, data and AI agents instead of manually completing every individual task.
Startups Could Gain an Advantage Over Large Companies
Startups generally have fewer legacy systems and organizational structures.
That can make it easier for them to build companies around AI-native processes from the beginning.
Established companies may face more difficult challenges involving fragmented data, outdated software, complex approval processes and departmental silos.
Large organizations still hold important advantages, including established customer relationships, capital, proprietary information and industry expertise.
The strongest competitors may therefore be the companies that combine those existing assets with fast, AI-driven operating models.
Agentic AI vs Generative AI
The distinction can be summarized simply.
Generative AI produces something.
It might generate text, images, code or analysis.
Agentic AI attempts to accomplish something.
It can potentially determine the required steps, interact with digital tools, evaluate outcomes and continue working toward an objective.
The technologies are closely connected because AI agents frequently use generative AI models for reasoning and communication.
But giving AI the ability to take action makes agentic systems potentially more powerful—and more difficult to control.
What Comes Next?
The next stage of enterprise AI is likely to focus less on individual AI tools and more on networks of specialized agents.
A company could eventually operate with hundreds or thousands of AI agents performing specialized activities across finance, marketing, sales, IT, customer service and operations.
McKinsey has described a potential emerging model in which humans work alongside networks of virtual and physical agents, with AI increasingly becoming part of the organization’s workforce rather than remaining a standalone software tool.
Some agents will operate independently.
Others will collaborate with employees or additional agents.
The technological capability is advancing rapidly, but business adoption is likely to remain more gradual because organizations must solve issues involving data, security, governance and accountability.
Final Thoughts
Agentic AI could become one of the most important business technology shifts of 2026 and the years that follow.
Generative AI demonstrated that machines could create useful content. Agentic AI is beginning to demonstrate how artificial intelligence could participate directly in completing business processes.
McKinsey’s 2026 research shows that leading companies are already integrating AI and agentic automation deeper into their operating models to generate measurable business value.
The businesses that benefit most may not necessarily be those deploying the largest number of AI agents.
They are more likely to be organizations that identify the right workflows, establish reliable data foundations and create clear boundaries between machine autonomy and human judgment.
