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Businesses do not lose time on agreements - or any other business process - because they lack AI tools. They lose it because the steps involved, like contract intake, review and approvals, still move through disconnected systems, inboxes and document versions.

There is an opportunity to embed AI where work actually happens so that talent spends less time on routine admin in favor of work that requires judgment and negotiation - and offers growth and interest.

Most organizations are looking for AI gains in the wrong place. The deeper issue isn’t a lack of access to intelligent tools, but poorly designed workflows that often go unnoticed. In agreement processes, for example, the cost shows up in slower revenue, more dull manual review, weaker visibility around important changes or areas of risk, and unnecessary pressure on legal, procurement and HR.

AI becomes useful when it is built into those workflows to automate repeatable tasks, apply consistent policy checks and help teams move faster without losing control.

AI projects underperform when they're bolted onto broken processes. Instead, redesign existing processes, or reimagine them completely. Many users treat AI as a layer when deeper results come from reworking how tasks move across teams and how people spend their time interacting with each other.

Contract management - better called ‘agreement work’ - is a good example because though it touches revenue, supplier management, hiring and compliance, it often still runs through fragmented handoffs and manual reviews.

Deloitte research calls this the “agreement trap”, with poor agreement management costing businesses significant time and value - nearly $2 trillion in lost global economic value.

That multi-trillion-dollar figure is the sum of thousands of small frictions inside every organization. Teams lose time on intake, triage, chasing status, checking standard clauses and routing approvals.

Highly skilled people are pulled into repetitive work that does not require their full expertise. Then, deep insights on key people, mechanisms, and alliances sit locked up in static documents, not being used within the knowledge base of new intelligent solutions used to set corporate strategy.

Tasks and processes are the challenge that technology offers a solution for, and they outlast whatever technology solution is applied. Within agreement management for example, legal teams are there to review and remove risk, yes, but they also act as traffic controllers for broken processes, ensuring the task is moved onward to completion.

Procurement and HR face similar issues when requests arrive inconsistently and progress can be hard to track.

Early lessons in AI implementation have shown leaders that AI is most effective when the underlying workflow is structured. If the process is unclear then AI can accelerate confusion at scale.

Those with tech expertise and skill in workflow creation design can standardize rules and responsibilities such that AI will support the consistency and speed in a well-designed system.

So, a strong first question isn’t “where can we deploy AI?” It’s “where do existing workflows force skilled people to do routine and less valuable work?”

In agreements, that includes comparing terms against policy, flagging deviations, chasing approvals, and surfacing the subsequent step for the following decision-maker. AI works well here because the task is repeatable and the human still owns judgment.

With AI embedded in the workflow of this example, routine review is automated so standard clauses and known risks are checked quickly. Teams work from shared playbooks, making reviews more consistent.

Then progress becomes visible across the process, with fewer requests disappearing into email chains, sitting with absent people, or in a game of pingpong.

An AI-assisted review compares contracts against pre-approved playbooks, suggests redlines inside the workflow, centralizes intake, review and approvals, prompting the person, rather than the other way around.

Faster does not have to mean riskier when control of the workflow has been tightly defined. In any business managing a workflow, whether agreements and contracts or anything else, the best use of AI is to make business applications more consistent, with human experts able to spend significantly more time on areas like exceptions, negotiation and complex risk.

Used well, AI allows experts to focus on the areas where nuance matters most - just when it’s most needed.

Agreements cut across every major business function. Poor agreement workflows affect deal velocity, supplier onboarding, compliance and employee processes at different points in the workflow. That makes agreement management part of the whole digital workplace conversation, not a legal-contractual-sales-back-office issue.

More recent Deloitte research found that companies with advanced agreement management are more likely to outperform financially, and that 85% of organizations with advanced agreement management say it contributes to strategic goals.

It’s proof that the market is shifting towards structured agreement management solutions for areas of risk reduction and revenue acceleration. Leaders are building AI into structured workflows around intake, review and approvals because that is where their users feel the friction most acutely. It’s been that way since the first complex contract between two organizations was signed.

This example from agreement management shows that the next phase of enterprise AI can be less about eye-catching claims and more about fixing the everyday workflows that shape how humans do real business.

Agreements are one example, but they make the lesson clear: AI delivers the most value when it helps people do real work fast, consistently, and visibly. But more than that, it shows how by rethinking the invisible we reassess what is possible, making business work better.

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