Your organization rolls out an AI agent to assign duties, draft updates, and nudge overdue approvals. However inside days, it’s flagging accomplished work, tagging the fallacious folks, and creating confusion as an alternative of readability.
It’s a well-known end result for corporations that undertake agentic AI with out the workflows, knowledge, or programs to help it. New analysis from Wrike reinforces that disconnect: 74% of workers say their firm treats knowledge like gold, but most don’t handle it effectively sufficient for AI to make use of it successfully.
Even the neatest, most context-aware instruments stall with out sturdy foundations. And automation doesn’t repair damaged operations—it magnifies them.
To get agentic AI proper, organizations want a phased method that tightens processes, clarifies what’s price automating, and ensures AI is ready as much as truly transfer work ahead.
What occurs when AI meets a damaged system
The push to undertake agentic AI has outpaced the work wanted to make it efficient. Many leaders assume their programs are prepared—till AI is requested to behave. That’s when the cracks present.
AI can’t make knowledgeable selections when workflows are improvised, institutional information is undocumented, and escalation paths stay in somebody’s head.
Approvals that occur advert hoc in Slack and inconsistent staff processes depart no single supply of reality for AI to observe.
And when knowledge is scattered throughout siloed platforms—the main reason for misplaced institutional information up to now 12 months—even probably the most dynamic, context-aware fashions battle to generate correct insights or determine dangers.
AI is sort of a microphone: It doesn’t enhance your voice, it simply makes it louder. With out structured workflows that outline possession, execution order, and visibility, AI solely amplifies dysfunction at scale.
The constructing blocks of an AI-ready workflow
To ship worth, AI wants to grasp what’s taking place, who’s doing it, and the place work lives. That requires workflows constructed with:
- Readability—Are undertaking roles and steps clearly outlined so AI can shortly grasp aims?
- Accountability—Is possession constant and visual so AI can route duties and escalate points to the fitting folks?
- Visibility—Can groups simply monitor progress and determine blockers earlier than they derail timelines?
- Connectivity—Are programs built-in so AI can entry data throughout instruments, not simply in silos?
- Consistency—Are workflows standardized sufficient for AI to detect patterns and suggest enhancements?
These components give AI the context it wants so as to add worth. However even well-designed workflows collapse with out dependable knowledge. AI wants clear, organized inputs, which suggests imposing naming requirements, having good high quality descriptions in place, surfacing the fitting recordsdata, and making a single supply of reality.
Getting these fundamentals proper reveals the place work breaks down, making it simpler to replicate and enhance. It’s an opportunity to ask not simply how to automate, however why. What’s slowing you down? The place’s the friction? What’s repetitive, irritating, or pulling focus from higher-impact work?
That’s the place AI makes an actual distinction.
3 steps to get agentic AI proper
Whereas good workflows aren’t a prerequisite for agentic AI, the adoption course of will shortly floor what’s damaged. A phased method helps you to experiment, shut gaps, and construct belief in AI instruments as you go.
Section 1: Construct AI fluency
Earlier than deploying AI into manufacturing, give groups visibility into how the system causes, what actions it is going to take, and which knowledge it attracts from. This transparency builds belief by making AI conduct comprehensible. It additionally offers groups an opportunity to evaluate whether or not knowledge and workflows are structured and reliable sufficient for automation.
Section 2: Check the waters with AI assistants
As soon as groups belief how AI behaves and perceive the way it makes selections, start making use of AI to actual—however low-stakes—duties. Assign AI assistants to repeatable work like drafting undertaking updates or answering inside FAQs.
That is the place principle meets execution. You’ll shortly see which processes are actually repeatable, the place AI struggles, and which workflows nonetheless want readability. Consider it as a strain check: By utilizing AI in on a regular basis operations, you may spot and repair issues earlier than scaling additional.
Section 3: Shift to agentic AI strategically
With predictable workflows and a staff able to collaborate with AI, you may start exploring extra autonomous instruments. Agentic AI affords compounded worth, but it surely additionally raises the stakes. When AI begins taking motion, it wants clear knowledge, steady programs, and clear oversight.
However even the very best AI brokers want people within the loop to course appropriate, add real-world context, and maintain AI aligned with precise enterprise objectives. The objective isn’t hands-off automation, however smarter collaboration between folks and AI.
This phased method to agentic AI adoption reinforces your basis at each step, supplying you with the construction and insights to enhance as you go.
That’s the distinction between utilizing AI and being prepared for it. AI-ready groups don’t rush adoption. They ask sharper questions on what instruments ought to do, what work issues most, the place human judgment is crucial, and what ought to by no means be automated within the first place.
What AI wants from you
Agentic AI can streamline work and release your groups to deal with what issues most, however provided that your operations are organized, your knowledge is clear, and your programs are related.
With out that basis, automation doesn’t remedy issues. It simply scales them. So whereas the way forward for work could also be automated, success nonetheless relies on how effectively you outline, join, and handle the work itself.
