Work is broken. Enterprise AI can fix it, but only if it's accurate, governed, context-aware, well-connected, and measured by real business outcomes.
Michael Brenner
Vice President, Thought Leadership and Customer Advocacy
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Look around and it doesn¡¯t take long to notice that work feels broken.?
Too many tabs. Too many tools. Too many urgent requests that land on people who are already at capacity. How many tabs are open on your browser right now?
We¡¯ve given employees pilots, portals, and more apps than they can count. We¡¯ve added AI features that summarize email and draft replies. And yet the work day still feels like a grind.
Here¡¯s the paradox of productivity: AI is finally capable of doing real work. But it¡¯s not always making life easier for people or more productive for our companies.
Leaders don¡¯t need another pilot, another demo, or another headline.?
They need a path to a different kind of work day. One where the noise disappears into the background and we can finally focus. Where AI doesn't suggest the next word in an email. It takes a foundational leap. It knows after listening in on a team call that you'll need to update your org chart and headcount budget to align with company changes ... and it does that work for you.
That¡¯s where the new rules for enterprise AI begin. So here is a roadmap for turning the paradox of productivity into real progress. And it starts with one non?negotiable: AI has to get it right.
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When I talk to leaders, their message is blunt: we cannot experiment with payroll or A/B test closing the books.
Functions like accounting, finance, and HR cannot ask AI Agents to guess the next number in the sequence. These outcomes can¡¯t be mostly right.
In a recent ²ÝÝ®ÊÓƵ survey, IT leaders had a near-zero tolerance for AI-generated errors, with the vast majority saying an error rate of 1% or more is unacceptable in critical functions like Finance and HR.
HR, finance, and IT live in a world where ¡°almost right¡± is simply wrong.
HR, finance, and IT live in a world where ¡°almost right¡± is simply wrong. People get paid or they don¡¯t. The books balance or they don¡¯t. Compliance is met or it isn¡¯t.
General?purpose AI that¡¯s disconnected from the context, rules, and guardrails of the business don¡¯t work in these use cases. That¡¯s because general-purpose AI makes guesses by design. It predicts. It recommends based on patterns and likelihoods. That¡¯s what makes it powerful, but also risky for your business.
By contrast, enterprise AI must be built for precision, governance, and auditability from day one:
It has to be grounded in the systems that already run the business.?
It must understand the context between HR and Finance data.
It needs to be trained on the platforms, security models, and business rules.
It must run alongside every transaction logged, audited, and traced.
That¡¯s where the magic happens: when you pair the logic and trust of enterprise platform software with super intelligent predictability.?
AI that¡¯s free?floating in a browser tab can write a nice paragraph. AI that¡¯s embedded in your HR and Finance platform can move money, change roles, and manage risk with the right guardrails.
For years, ERP systems were like digital filing cabinets, storing and organizing records of what has already happened.?
Enterprise AI will create a collaborative system that continuously learns, connects fragmented signals, and finally helps humans reach better decisions about their people and money.
But getting it right is only the first step. Once AI can act inside your core systems, the real question becomes: who¡¯s in charge??
That¡¯s where the second rule comes in.
We all love the idea of agents that can do our work for us.
Schedule meetings. Approve routine requests. Flag the anomalies. File expenses (Oh, yes please!). That¡¯s the promise.
But more and more we¡¯re asking the question:?
What happens when an agent goes rogue?
In our research, the biggest barriers to letting AI do more of the work were:?
Security and access controls (52%)
Data quality and fragmentation (51%)
Compliance and audit requirements (45%)
In other words, leaders don¡¯t want agents without guardrails. We want agents that understand how work actually happens. Agents that respect permissions. That follow policy. That can explain why they did what they did.
Payroll is an example of a business process that requires as much precision as you can get. In early deployments, ²ÝÝ®ÊÓƵ payroll agents are delivering up to 4x faster compliance resolution without loosening governance controls. It scans for issues, identifies gaps, and guides administrators through fixes in seconds.
Legal and procurement teams are seeing the same pattern.
Contract review used to be a classic bottleneck: thousands of agreements, each one carrying risk, each one needing human eyes. Technology firm NetApp was able to review more than 90,000 contracts with the help of AI agents, saving over $2.5 million and thousands of hours of manual work.
These agents are automating precision work with guardrails fully employed. They¡¯re operating inside clearly defined processes, with clear rules and oversight.
If your data and processes are fragmented, your agents are flying blind.
That¡¯s enterprise AI: agents that make work faster without adding risk.
But even that isn¡¯t enough.
You can have accurate AI. You can have governed AI. And still not get the transformation you¡¯ve been promised. Because if your data and processes are fragmented, your agents are flying blind.
That¡¯s where the third rule comes in.
Why haven¡¯t most organizations moved from AI as a helper to AI as a doer? It¡¯s not because AI can¡¯t handle complexity. It¡¯s because it doesn¡¯t have the right context.
Today, most enterprises look like this:
HR data in one system
Finance data in another
Tickets and requests in a third
Contracts and documents scattered across drives and apps
And then we drop AI on top and ask it to ¡°be smart.¡± No wonder teams are stuck with pilot fatigue.
For AI agents to handle the messy, high?stakes work like closing the books, running payroll, or managing headcount, they need more than a prompt. They need the full picture, including:
Who this person is and where they sit in the org
What policies apply to their role, region, and contract
What approvals are needed and from whom
What has happened in the past and what usually goes wrong
That¡¯s context you can only get from a connected Enterprise AI platform. The stuff your HR and Finance systems already know.
When you combine that with AI¡¯s ability to read, summarize, and reason over unstructured data, something powerful happens. You move from tools that record work to agents that perform work.
And you unlock an entirely new way of running the enterprise.
For example, early access customers of ²ÝÝ®ÊÓƵ Planning Agent used to spend hours every month getting to one version of the truth. Now they are seeing about a 30% reduction in data exploration, adding up to roughly 100 hours per month.
TX Group, a Swiss media and technology company, transformed its financial operations by slashing troubleshooting search times from two hours to mere seconds, saving core admins up to 30 minutes weekly. By surfacing account variances and drivers in just two minutes, the group eliminated manual reporting hurdles, successfully shifting the FP&A team¡¯s focus from chasing data to delivering high-value strategic analysis.
The agent is sitting on top of the same trusted planning, HR, and financial data. It knows the entities, the hierarchies, the history.
The difference isn¡¯t just the AI. It¡¯s the context. The agent is sitting on top of the same trusted planning, HR, and financial data. It knows the entities, the hierarchies, the history.?
So when a leader asks, ¡°Why did margins move last quarter?¡± The answer isn¡¯t a generic chart. It¡¯s a grounded explanation tied to their actual business.
Now, most employees don¡¯t live in your planning tool. Or your HRIS. Or your finance system. They live in email, chat, calendars, documents, and a dozen other apps that were never designed to talk to each other.
If AI only works inside a single system, you¡¯re still asking people to be the glue. That¡¯s where the fourth rule comes in.
One big reason work feels broken is that some of our systems are connected, but our work isn¡¯t. People still spend too much time playing find the truth.
Which spreadsheet is up to date?
Which system has the right number?
Which version of the policy applies?
In our research, more than half of IT leaders said data fragmentation is a top barrier to scaling AI. Employees don¡¯t talk about fragmented architecture. They just feel the pain every time they re?enter the same information or chase down a missing field.
The new rule for enterprise AI is this:
No one should have to care where the data lives.
In the new work day, workers won¡¯t log into ten systems to get ten partial answers. They¡¯ll talk to an AI teammate that can:
Find the right information across HR, finance, CRM, ticketing, and more
Take the right action in the right system
Explain what it did, and why
We¡¯re already seeing this in frontline hiring.
At 7-Eleven, Inc., store leaders used to spend hours every week bouncing between tools like job boards, email, scheduling apps and more, just to keep roles filled. Hiring was constant, manual, and exhausting.
Now, up to 95% of store hiring is automated. Time?to?fill has dropped by 70%, giving store managers back more than 2 million hours a year.
From a manager¡¯s point of view, you could say it feels like magic. Candidates move from application to screening to scheduling to offer with just a few simple prompts.?
But under the hood, the agent is doing something very grounded. It¡¯s pulling data from the right systems, applying the right rules, and updating the right records without asking the manager to think about which system owns what.
That¡¯s the shift. Instead of people navigating a maze of disconnected tools, agents navigate the maze for them.
So an employee can say, ¡°Update my home address and show me how this affects my tax forms and benefits,¡± and the agent knows which systems to hit, which policies to apply, which approvals to trigger.
They don¡¯t care where the data lives. They just see the work get done.
Now we have just one more rule for Enterprise AI. And it¡¯s all about ensuring we are seeing real value.
Most AI programs are still just measuring usage, implementing features, and testing new functionality.
What we should be testing is whether managers are spending less time in spreadsheets? Or whether HR and Finance teams are doing fewer manual reconciliations and handoffs?
If those things aren¡¯t changing, your AI isn¡¯t creating real value yet.
Enterprise AI should show up in the rhythm of the day:
Fewer tickets and quick questions for HR and IT
Fewer fire drills at close
Fewer status meetings to share information that agents could update in real time
More time for the work only humans can do¡ªcoaching, listening, designing the future
In our research, IT leaders aren¡¯t planning to rip and replace their core systems. They expect to keep using the same HR and Finance platforms with AI built in.?
Here¡¯s the one question leaders need to ask:
Is this technology actually changing how work feels?
Are employees less drained at the end of the day? Are leaders making faster, better decisions with more confidence? Are we spending more time connecting with colleagues, celebrating customers, and using our creativity?
If the answer is no, it doesn¡¯t matter how impressive the model is. You¡¯ve changed the tools. Not the work day.
AI gives us a once?in?a?generation chance to redesign how work gets done in the first place.
From broken work to a new work day
Work feels broken. Not because people aren¡¯t trying hard enough. But because the system around them was built for a different era. Siloed tools. Manual handoffs. Endless status checks.
AI gives us a once?in?a?generation chance to fix that.
Not by sprinkling assistants on top of old processes. Not by adding more dashboards to already crowded screens. But by redesigning how work gets done in the first place:
From software that records work to agents that perform it
From people acting as the integration layer to agents connecting the work for them
From human capacity as the ceiling to a hybrid workforce of people and AI working together
That¡¯s the shift these new rules point to.
AI that gets it right where the stakes are highest.
Agents that operate with guardrails baked in.
Complexity handled through rich, connected context.
Work that is connected across systems so people don¡¯t have to be.
Impact measured not by the demo, but by how the work day feels.
Leaders don¡¯t need more hype. They need the courage to rebuild the work day around these principles. To move from pilots to production. From experiments on the edge to transformation at the core.
Work is broken. And we know how to fix it.
If we apply these five rules of enterprise AI to redesign the work day, so the busywork goes to agents, and the meaningful work goes back to people.
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Survey Methodology Note
The research referenced in this article was conducted online in the U.S. by The Harris Poll on behalf of ²ÝÝ®ÊÓƵ among 206 IT Leaders, who are employed full-time in the IT department, with a title of Director or higher, at a business with 1,500+ employees and who have decision making responsibility for core infrastructure AI deployment. The survey was conducted February 13 - 19, 2026. ? Raw data were not weighted and are therefore only representative of the individuals who completed the survey.
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