AI in the Workplace: What’s Actually Working in 2026 | Kore.ai

How AI in the workplace is impacting productivity in 2026?

AI in the workplace moves productivity into the fast lane. Here’s how it’s helping employees get more done every day.

Recently, NVIDIA CEO Jensen Huang described AI as “the most powerful technology force of our lifetime, that will touch every industry, every company, and every person”. McKinsey, too, has drawn a similar parallel, comparing AI’s impact to that of the steam engine.

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Not everyone might agree with the scale of these statements, but the "should we adopt AI" debate is largely behind us. According to Kore.ai’s State of AI report, 71% of organizations are now using AI across functions. From automating routine tasks in HR and IT to supporting informed decisions in finance and operations, AI is increasingly woven into how work gets done. 

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Now, all of this sets the stage for the next phase of AI at work. As AI evolves into Agentic AI, intelligent systems are beginning to execute tasks and work alongside people with greater autonomy. This marks the moment when workplace AI starts to shape enterprise-wide workflows and organizational design.

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So what does all of this actually mean in practice for enterprise leaders? Let’s first understand what AI in the workplace really means in 2026, and then move to how leaders can make it an integral part of everyday work to deliver measurable value at scale.

Key takeaways (The TL;DR):

Before we jump into the specifics, here are the key takeaways that you need to know.

• AI in the workplace has evolved. We’ve moved beyond simple task execution towards agentic systems that understand goals and help work progress across workflows. 
• The biggest challenge to adopting AI at work is readiness. Gaps in data foundations, integration, governance, and skills are what hold organizations back. 
• Culture determines who benefits most from AI. Employees who become AI power users feel more in control, more productive, and more engaged, but they get there faster when leaders actively encourage responsible AI use. 
• AI at work only succeeds when it’s governed. Security, compliance, and transparency are a must-have if AI is going to deliver value across teams and regions.

Defining AI in the workplace

AI in the workplace means how intelligent systems support everyday work across the enterprise, automating routine tasks, surfacing relevant information, and enabling better decisions. Operating across existing systems and workflows, workplace AI reduces manual effort and makes organizational knowledge easier to access and act on, helping employees work more efficiently.

In an enterprise setting, AI in the workplace typically shows up in a few ways:

• Employee self-service and internal support: From IT to HR, AI for work helps employees find answers, raise requests, and resolve common issues more quickly, cutting down on back-and-forth and improving everyday productivity. 
• Search enterprise knowledge: Modern workplaces generate vast amounts of information across systems and tools. AI in the workplace makes that knowledge usable by surfacing relevant context and answers across platforms. 
• Automate tasks: From routing requests and updating records to triggering follow-ups, workplace AI takes on repetitive tasks, freeing teams to focus on higher-value work. 
• Analytics and insights: Workplace AI analyses patterns across operational data, helping leaders and teams identify issues early and make more informed, data-backed decisions. 
• Meeting intelligence and content summarization: Workplace AI captures discussions, summarizes key points, highlights action items, and turns unstructured conversations into information that teams can act on. 
• Agentic workflows: Agentic AI systems monitor activity across workflows, understand goals, and take action within defined guardrails, moving work forward without waiting for explicit commands.

In practice, these capabilities work best when they are connected. The most effective workplace AI brings them together and operates as a single system. For example, AI that supports meetings can also surface relevant information, prompt follow-up actions, and carry context forward across workflows. 

See AI for work in action

The state of AI in the workplace in 2026

Work in large organizations has become increasingly fragmented and taxing. According to Gallup, around half (48%) of employees say they experience burnout on the job, driven by information overload and fragmented workflows. On top of that, knowledge workers waste an average of nine hours each week searching for documents, emails, and answers across systems.

It is against this backdrop that AI in the workplace has quickly become one of the most widely adopted use cases of AI, alongside business process automation and customer service. Today, 31% of enterprises are already using AI specifically to improve employee productivity and reduce everyday friction.

Employees are already seeing the benefits where AI is in place. Microsoft’s Work Trend Index found that 90% of knowledge workers say AI helps them save time, while 85% say it helps them focus on their most important work. It’s little surprise, then, that many organizations are prioritizing AI for productivity and efficiency, with 89% of organizations planning to increase investment in workplace AI over the coming months.

However, despite widespread use, many enterprises remain underprepared for AI at scale. The aforementioned Kore research also shows that 70% of organizations are not yet prepared to leverage AI and only 30% consider themselves “fully ready” to support AI across the business, with gaps most often appearing in data foundations, infrastructure, governance, and talent. 

In short, making meaningful progress with AI in the workplace is more about being properly prepared than sheer ambition. This gap between ambition and readiness becomes clearer when you look at how workplace AI has evolved, and where it is heading next.

Read this executive guide to learn more about AI for work.

Evolution of AI in the workplace: From RPA bots to AI agents

AI in the workplace hasn’t arrived in one sudden leap, but has evolved gradually, from automating simple tasks to supporting increasingly complex work. Looking at that evolution explains why AI is now more embedded in everyday operations. Here’s a quick breakdown of workplace AI evolution:

1. Task automation

For many enterprises, the first exposure to workplace AI came through automation. Technologies such as RPA and scripted workflows were used to handle repetitive, predictable tasks like routing requests, updating records, or triggering follow-ups. 

These systems delivered clear efficiency gains, but they worked strictly within predefined rules. Every action had to be set in motion by a person, and anything outside the script quickly became a limitation.

2. Predictive and analytical AI

As enterprises accumulated more data, AI began to play a broader role in analysis and forecasting. Predictive models helped teams identify patterns, anticipate demand, and flag potential issues. In the workplace, this typically meant dashboards, alerts, or recommendations that supported decision-making. 

While useful, these systems remained largely reactive. They could highlight what was happening, but people still had to decide what to do next.

3. Generative AI for knowledge work 

Generative AI marked a noticeable shift. Suddenly, employees could interact with AI in natural language, generate content, summarize information, and find answers more easily. This made AI far more accessible and expanded its use beyond technical teams. Even so, much of this capability still sat in individual tools or moments of interaction, rather than being woven into end-to-end workflows. 

4. Agentic AI 

Agentic AI represents the latest stage of workplace AI evolution. Instead of waiting to be prompted, these systems are designed to understand goals, observe activity across workflows, and take action within agreed guardrails. 

For instance, an agent might prepare information ahead of a meeting, coordinate follow-up tasks across systems, or move work forward based on context it already understands. The emphasis shifts from assisting with individual tasks to supporting work as it unfolds.

Watch this webinar to learn how enterprises are adopting Agentic AI.

Use cases of AI in the workplace

AI in the workplace shows up differently across teams, but the value it delivers is largely the same. Less time spent searching, documenting, and coordinating, and more time spent actually doing the work. Across functions, workplace AI is used in different ways:

Use case 1. Internal support, help desk, and ticketing

AI is most immediately valuable where work bottlenecks form fastest, and internal support is a classic example. IT, HR, and shared services teams deal with high volumes of similar requests, where speed and consistency matter more than novelty. 

AI helps by acting as a first line of support, answering common questions, raising and routing tickets, and resolving routine issues automatically, while escalating complex cases with full context.

In practice, this can be handled by AI agents such as:

• IT Service Desk AI Agent 
• HR Helpdesk & Employee Support Agent
• Ticket Automation Agent 
• ITSM Virtual Assistant 

These agents are designed to integrate directly with ticketing systems, apply policy-aligned responses, and reduce ticket volume without removing human oversight. The result is faster resolution for employees and less time lost to back-and-forth.

Use case 2 Enterprise search and knowledge access

Most enterprise knowledge already exists. The problem is finding it when it matters. Information is spread across documents, emails, tickets, meeting notes, wikis, and internal tools, which means employees often spend more time searching than acting. 

AI enterprise search changes that dynamic. Instead of navigating folders or guessing keywords, employees can ask direct questions and get clear, contextual answers pulled from across systems, with sources attached. 

For example:

• A finance analyst can ask why a policy changed and see the relevant approval trail 
• A product manager can quickly surface past decisions before a planning meeting 
• A new hire can find answers without relying on tribal knowledge 

By making knowledge accessible in the flow of work, AI reduces friction, speeds up decisions, and helps teams move with more confidence.

Use case 3. Day-to-day execution and operations 

A lot of operational work doesn’t fail because people don’t care, but because coordination gets messy. Tasks live in different tools, updates happen in meetings, and follow-ups rely on someone remembering to chase them. 

AI helps by quietly managing these workflows in the background, routing tasks, updating records, and prompting next steps without constant human intervention.

This is typically handled by AI agents such as:

• Task Management Agent
• Incident Management Agent
• Approval Management Agent

These agents are designed to connect actions across systems like ERP, CRM, ITSM, and collaboration tools. Instead of manually stitching updates together, teams get a clearer view of what’s moving, what’s blocked, and what needs attention next so that work keeps progressing without constant check-ins.

Use case 4. Project delivery and cross-team coordination

This is where AI fits more naturally than “dashboards”. For project managers, delivery leads, and PMOs, the challenge is rarely a lack of tools. It’s keeping work aligned across teams, timelines, and systems without spending half the week chasing updates.

AI supports project delivery by acting as a coordination layer across tools like Jira, ServiceNow, collaboration platforms, and internal systems. 

In practice, AI can: 

• Track project status across systems and surface risks early 
• Summarise progress updates automatically 
• Flag delays, dependencies, or workload imbalances 
• Help teams stay aligned without constant reporting cycles 

Rather than replacing project management, AI reduces the overhead around it, allowing teams to focus on delivery rather than administration.

Use case 5. Leadership visibility 

For leaders, the problem isn’t too little data. It’s too much noise. AI helps by aggregating signals across projects, operations, and support functions, and presenting a clear picture of where attention is needed. 

Instead of asking for updates or reviewing lengthy reports, leaders can see patterns, risks, and progress across initiatives. This is often supported by agents such as:

• Customer 360 Agent
• Executive Insights Agent
• Company and Investor Profile Agent

This agent creates a structured profile of the company, integrating financial metrics, ownership data, strategic direction, and historical performance. When governed properly, AI gives leaders clarity without forcing teams into constant reporting mode.

Explore more AI for work use cases

AI in the workplace is reshaping enterprise culture

AI is not only changing how work gets done; it is also shaping the culture within organizations. Microsoft’s research shows that employees can be categorized into four groups when it comes to using AI at work: skeptics, novices, explorers, and power users. 

While skeptics might save ten minutes a day, power users save more than thirty minutes and, more importantly, feel far more in control of their work. They are better able to manage their workload (92%), feel motivated (91%), and enjoy work more (91%). 

As more employees move into that power-user category, meaningful cultural shifts start to appear: 

• “Ask AI first” becomes the norm 

When someone needs an answer, the first step is increasingly to ask AI rather than interrupt a colleague or hunt through old documents. That simple change removes a lot of everyday friction and helps people stay focused on their own work. 

• Knowledge becomes shared 

When information lives in searchable systems rather than in people’s heads, influence shifts. Value comes less from knowing where things are, and more from applying judgment and insight. Expertise starts to matter more than tenure. 

• Collaboration becomes lighter and more focused 

AI takes care of the mechanics of coordination, summarising meetings, tracking actions, and carrying context forward. That means fewer catch-up meetings and less manual coordination, leaving more time for discussions that genuinely need people in the room. 

• Work becomes flexible 

When AI captures and connects what happens across meetings and workflows, missing a sync or stepping away no longer means falling behind. Teams gain flexibility without sacrificing shared understanding.

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These shifts, however, don’t happen on their own. The same Microsoft research also shows that power users are far more common in organizations where leaders actively encourage the use of AI. When leaders talk openly about how AI fits into everyday work and support practical experimentation, employees are much more likely to bring it into their day-to-day work.

In that sense, workplace AI reshapes culture through everyday habits. When it’s introduced thoughtfully, it helps organizations spend less time navigating complexity and more time focusing on work that actually matters.

Benefits of using AI in the workplace

When AI is embedded into everyday work, the benefits tend to compound. The biggest gains come from doing familiar work with less friction and greater consistency. A few benefits stand out across enterprises that are using AI well.

1. Greater productivity 

One of the clearest benefits of workplace AI is the time it gives back. By automating routine tasks, summarising information, and handling coordination in the background, AI reduces the cognitive load placed on employees.

McKinsey estimates that, taken together, AI could add the equivalent of $4.4 trillion annually in productivity gains to the global economy across a wide range of use cases. For enterprises under pressure to do more without exhausting their people, AI can meaningfully reduce the time spent on routine tasks and free up knowledge workers for higher-value work.

2. Faster, informed decision making

Leaders and teams no longer need to wait for reports to be compiled or data to be manually analyzed. AI can surface patterns, highlight risks, and provide timely insight based on real operational data. 

According to BCG’s global survey, companies that integrate AI into core decision-making processes report faster cycle times and improved decision quality, particularly in complex, data-heavy environments.

3. Better use of enterprise knowledge

A persistent drag on performance in large enterprises is information sprawl, where knowledge is distributed across various documents and platforms. AI doesn’t just index this content; it understands context and delivers answers where and when they are needed.

According to McKinsey, AI boosts how knowledge flows across functions like sales, customer operations, and engineering. This means teams can avoid duplicated efforts and make decisions rooted in a shared understanding of the organization’s history and direction.

4. Enhancing employee experience 

When routine work becomes less of a burden, the employee experience improves. According to Business Insider, employees using AI report spending more time on meaningful work and less on administrative burden, which correlates with higher engagement and satisfaction.

This matters because engagement and performance are intertwined; people who feel supported and effective are more likely to contribute more and innovate.

5. Competitive advantage 

Taken together, the workplace AI benefits position organizations to operate more efficiently and compete more effectively. According to BCG insights, companies that move beyond isolated experiments and embed AI into workflows are already generating value and outperforming peers.

Real-world examples of AI in the workplace

Real-world examples from enterprises like AMD and a global bank show that AI can reduce resolution times and boost employee satisfaction, all while keeping teams focused on strategic work.

Case study 1: How AMD transformed HR with Agentic AI

AMD, a global leader in high-performance computing, needed a scalable way to support its 30,000-strong workforce across regions and time zones. A lean HR team was struggling with high volumes of repetitive queries and manual processes. 

AMD introduced an agentic AI HR system to act as the first point of support for employees, enabling self-service for common requests, delivering role- and region-specific responses, and escalating sensitive issues when needed. 

Results: 

• 80% reduction in HR resolution time 
• 50% of queries resolved via self-service 
• 70% increase in employee satisfaction

“As a global leader in AI, we saw a clear opportunity to bring leadership in our own workplace. Our work with Kore.ai shows what’s possible when you use AI not to replace people, but to enhance how they work, connect, and lead.” 

— Robert Gama, SVP & Chief Human Resources Officer at AMD. 

Read full case study

Case study 2: Global financial services firm empowers 80,000 wealth advisors using AI

A leading global financial services organization faced a productivity challenge in its wealth management division. Advisors were spending too much time searching across disconnected systems to answer routine client questions. 

By deploying an agentic AI solution to unify enterprise knowledge, advisors gained compliant, real-time access to information through natural language queries, without disrupting existing systems. 

Results: 

• 12% reduction in time spent searching for information 
• Document access increased from 20% to 80% 
• 22% increase in employee satisfaction

Read full case study

Case study 3: Leading global bank scales HR support with AI

A leading global financial services institution, with more than 40,000 employees, needed to modernize HR support without increasing headcount. HR teams were managing tens of thousands of monthly queries across regions and channels. 

The bank introduced an AI-driven HR assistant to handle high-volume queries, deliver consistent policy-aligned answers, and route complex cases to HR specialists.

Results: 

• 94% resolution rate for AI-handled tickets 
• 83% reduction in HR ticket volume 
• 0% increase in HR headcount

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Read full case study

Key challenges of implementing AI in the workplace, and how to address them

Introducing AI into the workplace is rarely a single decision. It’s a series of choices about where AI fits and how much change an organization is ready to absorb at any given time. Most challenges don’t stem from the technology itself, but from how it intersects with people and processes. 

1. Choosing the right starting point 

One of the earliest hurdles is deciding where AI should be applied first. With so many potential use cases, it’s easy to spread effort too thin or chase novelty rather than impact. Organizations that make steady progress tend to start with a small number of well-defined use cases tied to clear outcomes, reducing response times, improving access to information, or cutting down repetitive work. 

Beginning with focused, visible wins helps teams build confidence and creates a foundation that can be expanded over time. 

2. Aligning AI with business 

AI initiatives often struggle when they are treated as standalone technology projects. A solution that looks efficient in isolation may clash with customer expectations, regulatory requirements, or the organization’s way of working. 

The way through this is alignment. Leaders who take the time to ask how AI supports broader business goals, rather than simply what it can automate, are better able to decide where AI makes sense and where human judgment should remain central. 

3. Managing risk without slowing down

As AI becomes more embedded in workflows, concerns around security, data privacy, governance, and unintended consequences come to the fore. Unlike traditional systems, AI can act quickly and at scale, which means small errors can have outsized effects. 

Enterprises that navigate this well focus on guardrails rather than restrictions. They conduct early risk assessments, involve legal and compliance teams from the outset, and maintain human oversight, particularly in the early stages of deployment. 

4. Navigating legal and ethical uncertainty 

Regulation around AI is still evolving, especially in areas such as data use and employee monitoring. Waiting for absolute clarity before acting can leave organizations stuck, but moving too quickly without principles creates its own risks. 

Many enterprises address this by setting internal standards. Clear guidelines around transparency, fairness, and accountability help teams understand when AI is appropriate and when human involvement is required. Ethical clarity builds trust internally and reduces the likelihood of reputational or legal issues later on. 

5. Managing employee expectations

AI often provokes mixed reactions from employees – curiosity, concern, and sometimes anxiety about what it means for their roles. At the same time, many employees want access to AI tools and opportunities to learn how to use them effectively. 

When organizations fail to provide clarity, people tend to find their own solutions. The rise of “shadow AI”, where unapproved tools are used quietly to get work done, is often a sign that demand has outpaced guidance. Enterprises that address this head-on set clear expectations, provide practical training, and create safe ways for employees to experiment. 

6. Building readiness 

Even the best ideas struggle without the right foundations. Data quality, system integration, and internal skills all play a role in whether AI delivers value or stalls at the pilot stage. 

Rather than aiming for perfection upfront, many organizations take an incremental approach. They invest in improving data access, encourage collaboration between business and technical teams, and scale gradually based on what works. This steady approach allows AI to become part of everyday work without overwhelming the organization.

Future of AI in the workplace

As AI becomes more embedded into everyday systems and gains a better understanding of context, its role begins to extend beyond individual tasks and into the coordination of work itself. This is where agentic AI starts to shape the next phase of the workplace. 

As we have seen in real-world examples of AI in the workplace, organizations are moving beyond isolated tools and laying the foundations for what can be described as the Agentic Enterprise, where AI is embedded into the very structure of how work gets done. 

This reimagined enterprise will be powered by thousands of AI agents operating in sync, executing tasks, making decisions, and adapting in real time. In this structure, humans will remain firmly in control, steering strategy and setting up governance, while swarms of specialized AI agents take on execution. 

Imagine this: 

• A supply chain manager defines delivery SLAs, and agents coordinate logistics, reroute shipments, and resolve bottlenecks before they even surface. 
• A customer service leader outlines experience benchmarks, and agents handle inquiries, escalate edge cases, and personalize support across languages and platforms. 

As this approach takes hold, the workplace begins to feel less like a rigid hierarchy and more like a connected network, where leadership provides direction, and AI ensures execution keeps pace. 

It may sound ambitious, but it reflects the direction enterprises are already heading. Gartner predicts that by 2028, 15% of day-to-day work decisions will be made autonomously by agentic AI, highlighting just how quickly this shift is moving from concept to reality. 

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For organizations thinking about the future of AI in the workplace, the opportunity lies in preparing for a model where AI supports work continuously in the background, allowing people to focus on the decisions and creativity that truly require a human touch.

The Kore.ai advantage of implementing AI in the workplace 

As AI becomes part of everyday work, enterprises quickly realize that value doesn’t come from isolated tools or informal usage. To work at scale, AI needs to be governed properly, connected to real business systems, and designed to operate reliably across teams, functions, and regions. 

Kore.ai’s AI for Work is built with this reality in mind. It brings together enterprise knowledge, agentic workflows, and governance into a single platform, helping organizations move from experimentation to consistent, scalable impact.

• Agentic AI built for enterprise work - AI agents that understand context, take action, and operate across systems, supporting work end to end rather than in isolated moments. 

• Secure access to enterprise knowledge - Agentic RAG search delivers accurate, real-time answers from structured and unstructured data, with role-based access ensuring information is shared appropriately.

• Governed and compliant by design - Built-in guardrails, audit trails, analytics, and compliance with frameworks such as SOC 2 and GDPR provide the control and transparency enterprises need to deploy AI confidently.

• Ready-to-use AI agents - Pre-built, domain-specific AI agents for HR, IT, sales, and finance, designed to work at enterprise scale and adapt to existing processes.

• Multi-agent orchestration at scale - Multiple AI agents can coordinate tasks, share context, and execute workflows across tools, with human oversight built in where it matters most. 

• No-code & pro-code deployments - Business teams can create and deploy AI agents easily, while developers retain the ability to extend and customize more complex workflows.

• Deep enterprise integrations - Connect AI agents to core business systems using hundreds of pre-built connectors and thousands of API actions, allowing AI to operate with full organizational context.

See Kore.ai’s Workplace AI in real-time

Conclusion: AI in the workplace closing takeaway

AI in the workplace is no longer something enterprises are merely experimenting with; it’s become part of everyday work. Across organizations, AI is already influencing how teams stay productive, make decisions, and collaborate. 

As we’ve mentioned earlier, the real value of workplace AI lies in making work simpler. For leaders, that means getting the basics right:

• Putting solid data foundations in place
• Being clear about where AI should help
• Creating an environment where people feel comfortable using it 

When AI is introduced thoughtfully and governed well, it quietly supports people to work better at scale, without getting in the way.

Want to explore how workplace AI helps in practice? Book a custom demo for your use case.

Not ready yet? Learn more about AI for work.

FAQs

Q1. What does AI in the workplace actually mean? 

AI in the workplace refers to how intelligent systems are used to support everyday work across an organization. This includes automating routine tasks, helping employees find information quickly, analysing data, and supporting better decisions, all within the tools and workflows people already use.

Q2. What are the best practices for implementing AI in the workplace?

Effective AI implementation starts with aligning AI initiatives to clear business goals, building a strong data strategy, and putting governance policies in place early. Most enterprises see better results by starting with small, measurable pilots, maintaining human oversight, and scaling only once AI proves reliable and valuable.

Q3. How do generative AI and agentic AI help in the workplace?

Generative AI focuses on creating content or answers in response to prompts, while agentic AI goes a step further by coordinating tasks, making decisions, and taking action across workflows within defined guardrails. In the workplace, agentic AI supports end-to-end execution rather than isolated interactions.

Q4. What are the cons or risks of using AI in the workplace? 

The main risks tend to sit around data privacy, security, bias, and lack of transparency, particularly as AI becomes more embedded into workflows. These risks are manageable, but they require clear governance, strong access controls, and human oversight, especially in high-impact decisions.

Q5. How should organizations prepare and train employees to work with AI? 

Successful adoption depends on helping employees adapt. Organizations should invest in training programmes, prompt engineering basics, and ongoing support that builds confidence and AI literacy. Clear change management and manager-led coaching help employees integrate AI into daily work more comfortably.

Q6. What ethical and legal considerations should enterprises keep in mind? 

Enterprises need to consider fairness, transparency, explainability, and data governance when deploying AI. Clear AI usage policies, human-in-the-loop systems, and regular monitoring help organizations meet regulatory expectations and maintain trust as laws and standards continue to evolve.

Q7. How can organizations measure the success of AI in the workplace?

Organizations can measure AI success by tracking outcomes such as time saved, reduction in manual tasks, faster decision cycles, employee satisfaction, and adoption rates. The most effective teams link AI initiatives to clear business metrics rather than usage alone.

Q8. What ethical considerations should organizations keep in mind when using AI? 

Ethical AI use centres on transparency, fairness, and accountability. Employees and customers should understand when AI is involved in decisions, how outcomes are generated, and where human judgment applies, especially in areas that affect people directly.

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