How Does AI Increase Productivity in the Workplace? The Case for Agentic AI in the Enterprise

Enterprise AI has moved past the hype stage. AI-powered workflows have already demonstrated their value in increasing productivity. Now, the focus is making it work at scale.

AI adoption rates are climbing. Roughly 37% of U.S. businesses with at least 250 employees report using AI in their operations, according to the Census Bureau's Business Trends and Outlook Survey. That's well above the 19.8% national rate.

But adoption and scale are two different problems. When you roll out a deployment and usage climbs, the productivity return stays hard to pin down in any report.

Here, we'll walk through how AI can actually increase worker productivity and how you can demonstrate it. We'll cover where manual processes quietly drain capacity, the metrics that make those gains visible, and what tends to stall enterprise adoption before it scales.

The hidden cost of manual processes

Manual processes can cost you more than the time they consume, and the damage often compounds across departments. One team's workaround becomes another team's delay, and the added expenses quietly multiply. By the time these inefficiencies surface, they look like a simple headcount problem.

According to Slack's Workforce Index, desk workers spend roughly a third of their day on tasks they consider low-value. Add those hours up across your enterprise, and that's a substantial share of payroll pointed at work nobody really wants to be doing.

Three operating expenses tend to account for much of this drag:

1. Manual data entry can introduce mistakes that require rework. HR and payroll often feel this most acutely, where a single error can affect everything from paychecks to compliance reporting.
2. Support requests can sit in queues for hours or days waiting for an assignee. Employees wait for answers, work stalls, and follow-up traffic pulls time away from higher-priority tasks.
3. Skilled employees spend hours on repetitive tasks that software can handle. High-cost resource capacity ends up pointed at your lowest-value work.

How does AI increase productivity?

AI can increase productivity by helping remove the manual steps between a request and its resolution. Three capabilities drive this:

• Taking over routine work that eats up employee hours
• Retrieving information in real time from across isolated systems
• Executing multi-step processes end to end without manual intervention

These productivity improvements are often measurable if you know where to look. Researchers studying 5,172 customer support agents for the Quarterly Journal of Economics found that AI assistance increased issues resolved per hour by 15%. Less experienced workers improved by roughly 30%.

Enterprise AI has also evolved quite a bit over the years. Generative AI came first, built on large language models. In 2026, Stanford's AI Index reported that organizational AI use at 88%, demonstrating that software could effectively interpret a request written in plain language.

Over time, though, there became a need for AI to do more than answer basic questions. That's where traditional automation capabilities began evolving into agentic systems.

Agentic AI can take a user's request and reason on it. It then plans specific goals associated with that request, mapping multiple steps across applications. The result is that support systems can resolve a request from start to finish, without needing to queue a support ticket or arrange a case handoff.

Top ways AI increases enterprise productivity

There are many ways AI can help improve productivity across an enterprise. The use cases with the clearest ROI tend to be the ones where work volume is high and repeatable.

Four areas stand out for having the largest potential impact:

Automating repetitive IT and HR requests

A handful of request types tend to account for a large share of tier-1 support ticket volume:

• Password resets
• Software provisioning
• PTO submissions
• Access requests
• Onboarding tasks

The common denominator is a fixed sequence: verify identities, check entitlements, make the change, send confirmation. AI agents can run that sequence end to end without disrupting workflows or handing the task off to someone else.

Over time, this can help reduce meaningful ticket volume. ManTech, a technology services provider to U.S. federal agencies, cut its tier-1 support workload by 50% using an AI assistant across IT and HR. The company’s employment verification process now runs in 60 seconds, saving roughly 1,000 hours of manual work each year.

Accelerating information retrieval and enterprise search

An employee looking up a benefits policy or a software setup guide often has to check three places before finding what they need. They might open an intranet, dig through a ticketing tool, or message a colleague who might know the answer.

Every time your team has to stop what they're doing, it costs time, and tool sprawl makes this worse. Agentic AI can query the tools in your tech stack concurrently and return a single grounded answer or resolution.

Companies like AkzoNobel are already experiencing the benefits of this kind of enterprise search. Their agentic AI solution now supports more than 25,000 employees, allowing them to search information across business applications in over 100 languages across 150 countries.

Enabling multi-step workflow automation

Not all AI works the same way, especially when a request is more complex. Here's how generative and agentic AI tackle the same type of prompt:

• Generative AI answers a prompt directly. Depending on what’s asked, it can reply, summarize a document, or explain a policy. Then it stops and waits for the next request.
• Agentic AI is designed to reason on a prompt to understand the intent behind the request. It can plan the steps needed to resolve the query, then execute those actions across your systems.

Think of the workflow that follows when you ask for an expense report through an AI assistant. The receipt gets scanned, matched against policy, sent to the right approver, and logged in finance, without you opening a form. A 401(k) update or a new-hire access request moves the same way.

Run this kind of efficiency across hundreds of workflows, and the numbers can add up quickly. Ciena, a networking and communications technology company, built more than 100 use cases on agentic AI, which helped them cut approval times from three days to 30 minutes.

Supporting smarter decision-making with real-time data

Strong enterprise decisions often depend on the quality of data available. Too often, that data sits locked in a report or database teams can't easily reach.

Agentic AI can help remove the guesswork by automatically surfacing the data teams need when they need it. Teams can then act on that information without depending on other departments for support.

Intercontinental Exchange takes advantage of this kind of capability through its own agentic-powered knowledge base. The company can now analyze its support data in real time to turn raw tickets into a prioritized to-do list that shows teams what's slowing the business down and where.

How to measure AI productivity gains

Measuring your AI productivity comes down to answering two key questions:

• How much work gets resolved without anyone touching it?
• How fast does everything else move thanks to automation?

Five metrics are worth tracking consistently:

• Ticket deflection rate: The share of requests AI resolves start to finish, with no agent involved
• Average resolution time: How long a request takes from submission to close, before and after deployment
• Time saved per employee per week: The hours AI returns to each employee by taking on routine work
• Cost per ticket: The average cost to resolve a single request, including labor and tooling
• Employee self-service rate: How many people solve their own requests without opening a ticket

Track these together, and you'll get a clearer picture of whether AI is resolving real issues within your enterprise or just handling the easy tasks. A correlation between increased self-service usage and lower operating costs is a good sign you're on the right track.

A strong agentic platform can surface these metrics for you, rather than requiring you to stitch reports together from separate tools to prove ROI.

Overcoming AI adoption challenges

Adoption alone is rarely enough to sustain long-term productivity gains. Enterprises that see the best results often start by clearing out these common obstacles proactively.

Build versus buy

When implementing an AI solution, you'll need to decide between assembling your own AI stack from point solutions or deploying a purpose-built agentic platform.

Building gives you more control but requires engineering time, ongoing maintenance, and an effective roadmap. A purpose-built platform trades some of that control for speed, integration capabilities, and a system that can improve over time without expensive retraining.

Change management

Employees use the tools they trust and follow policies they support. Building that trust early, through clear communication, hands-on training, and quick wins that show people the value firsthand can go a long way.

When employees worry that AI threatens their role, adoption tends to suffer.

Data security

An agentic platform reaches into the systems where your sensitive data lives. So setting up access controls from day one matters.

A platform built with enterprise security in mind can respect existing permissions while keeping each person's access scoped to what they're cleared to view.

Ethics and compliance

AI decisions should be fair, explainable, and auditable. Regulations shift, so your standards need to hold as they do.

Look for a platform that can show how it reached a result and maintain a record of what it does. This can help your team work within their compliance requirements.

Increase productivity across the enterprise with Moveworks

AI implementations tend to deliver the most value when they connect to the systems your employees already use. Moveworks was built for exactly that.

As an agentic AI platform, Moveworks acts as the intelligent front door to your enterprise. Using a conversational AI Assistant, employees can ask for what they need in natural language. The platform is designed to reason through each request and execute resolutions across the systems where the work actually lives, including ServiceNow, Workday, Okta, and Slack.

Moveworks’ productivity boosting features are designed to help teams extend the capabilities of the Moveworks AI Assistant with three core features:

• Brief Me is a document assistant that lets employees upload up to five files and ask questions about them, pulling grounded summaries and cross-referencing details. It can help teams avoid reading every page to find the information that matters.
• Quick GPT puts ChatGPT-style help inside the assistant. When internal systems don't have an answer, employees can use a company-provided GPT model to research a topic or draft content, without pasting sensitive information into a public tool.
• Powered by an advanced Reasoning Engine, Moveworks provides multi-step workflow automation that can process requests typically handled by more than one team. One conversation can move an IT ticket, an HR change, or a finance approval through each tool it needs to reach, without formal handoffs.

Procore uses features like these to improve its own productivity. With help from Moveworks, Procore reduced its HR support needs by 19%, giving new hires fast access to what they need from day one.

Ready to start seeing measurable productivity gains across your enterprise? Explore Moveworks today.