Enterprise AI projects can move quickly from an incredible demo to unexpected costs, implementation delays, low adoption and uncomfortable ROI questions. Welcome to the AI Circle of Sorrow.
If you have been involved in an enterprise AI purchase, pilot or rollout, there is a good chance you have already experienced some version of what I call:
The AI Circle of Sorrow.
It reminds me a lot of the CRM Circle of Sorrow I wrote about a few years ago.
Different technology.
Different promises.
Same organizations.
Same humans.
And many of the same mistakes.
Due to the highly sensitive nature of our completely unofficial in-field market research, names, companies, C’s, AI vendors, consultants, developers, implementation partners and recently unemployed executives have been hidden to protect the innocent.
And perhaps a few of the guilty.
This is how the AI Circle of Sorrow begins.
Why Do Enterprise AI Projects Fail?
The short answer:
AI projects often struggle because organizations buy the technology before fully defining the business problem, workflow, data requirements, integrations, governance, success measures and user adoption strategy.
An impressive AI demonstration proves that the technology can do something.
A successful enterprise AI implementation proves that it can do the right thing, using your information, inside your workflows, securely and reliably enough that employees actually want to use it.
Those are two very different things.
And that difference is where our story begins.
It Starts With the Amazing AI Demo
An energetic, enthusiastic AI salesperson rolls into the boardroom and shows you the future.
Their AI finds prospects.
It researches accounts.
It writes emails.
It summarizes meetings.
It updates the CRM.
It identifies buyer signals.
It creates proposals.
It answers customer questions.
It coaches your salespeople.
It forecasts opportunities.
It reads documents.
It talks to your systems.
And now, thanks to AI agents, apparently it can perform half the work in your company while everyone else sleeps.
At this point, you are reasonably certain that somewhere on the product roadmap it also makes a Starbucks-worthy oat milk latte.
Well heck yes, I want one!
Here is my CFO.
Where do we sign?
The AI salesperson congratulates you on a wise, visionary and potentially promotion-worthy decision.
You aren’t buying software.
You’re transforming the company.
As the ink dries, visions begin dancing through your head.
Productivity is going through the roof.
Administrative work is disappearing.
Revenue is going up.
Costs are going down.
Customer response times are collapsing.
Management is singing your praises.
The board finally thinks you understand AI.
There may even be a conference keynote in your future.
Here we go!
Stage 1 — This Isn’t What We Saw in the Demo
You log in.
You ask the AI to do something useful.
And…
Hmm.
Where is our customer information?
Where is our pricing?
Where are our products?
Why doesn’t it understand our sales process?
Why can’t it access our CRM?
Why can’t it find the documents it needs?
Why did it just confidently give me an answer that isn’t correct?
Why can it see something it probably shouldn’t?
And why is Legal suddenly standing behind me?
You quickly discover something important.
The demo had AI.
What you bought has AI too.
But the demo also had carefully selected information, prepared examples, predetermined workflows, configured integrations, permissions, instructions, testing and a use case specifically chosen to make the technology shine.
Your company has…
Well…
Your company.
Twenty years of documents.
Three CRMs.
Two ERPs.
Several acquisitions.
Fourteen versions of the price list.
Customer information spread across email, shared drives, collaboration tools, databases and the brain of Dave in Operations.
Half the company calls the same product by three different names.
Nobody knows which policy document is current.
And everyone assumed someone else was responsible for cleaning all of this up.
Welcome to:
Stage 1 of the AI Circle of Sorrow.
The difference between an incredible AI demonstration and an incredible AI implementation has just become painfully clear.
Stage 2 — Trapped!
No worries.
A quick call to your amazing AI salesperson will straighten this out pronto.
You explain that the AI doesn’t seem to understand anything about your company.
They reassure you.
Absolutely normal!
You just need to connect your enterprise data.
And configure the workflows.
And integrate your systems.
And define permissions.
And establish governance.
And determine what the AI is allowed to see.
And determine what the AI is allowed to do.
And determine what requires human approval.
And test whether its outputs are actually good enough.
And perhaps clean up a little data.
You ask:
“How much data?”
Silence.
So you try another question.
“Can your team make the AI we purchased actually work inside our business?”
Of course!
That will require Professional Services.
And perhaps an implementation partner.
And maybe a data specialist.
Possibly an integration specialist.
Definitely a security review.
Legal will need to be involved.
IT will need to be involved.
Operations will need to be involved.
The business teams will need to be involved.
Someone will eventually use the phrase:
AI governance framework.
Depending upon what you purchased, you may also discover additional consumption costs, integration costs, model costs and infrastructure costs.
Your CFO looks at you.
You look at the AI salesperson.
The AI salesperson looks at the implementation partner.
The implementation partner says:
“We should schedule a discovery workshop.”
Excellent.
Welcome to:
Stage 2 of the AI Circle of Sorrow — Trapped!
This is the moment when you discover that buying access to AI and successfully deploying AI are two completely different things.
Stage 3 — Eureka! Now We Know What to Do!
After several sleepless nights, a few conversations with the family dog and one discreet update to your LinkedIn profile, you arrive at an epiphany.
We need an AI strategy!
Or perhaps, more accurately:
We need to figure out what business problem we are actually trying to solve.
That distinction matters.
Because somewhere along the way the objective changed from:
“We need to reduce the amount of time our salespeople spend researching accounts.”
to:
“We need AI.”
Those are not the same objective.
AI is technology.
It is not a business outcome.
So now you return to the CRO, CIO, CFO, CEO and whichever other C’s have become involved and explain that making AI useful will require more than purchasing licenses.
We need to identify the right use cases.
We need measurable outcomes.
We need access to the right information.
We need integrations.
We need security.
We need governance.
We need workflow redesign.
We need testing.
We need people who understand how the work actually gets done.
We need to know where humans remain in the process.
And yes…
We may need some more money.
There is grumbling.
Quite a lot of grumbling.
But the alternative is continuing to fund an AI initiative nobody can clearly explain and few people are actually using.
So a decision is made.
We will do this properly!
Woo hoo.
Houston, we have a plan.
Let’s hit the road!
Stage 4 — Assemble the AI Avengers
Now the real work begins.
The business people explain the process to the AI people.
The AI people explain the technology to the business people.
The data people explain that the required information is spread across seven different systems.
The security people explain why neither group is allowed to do half of the things they just proposed.
Legal joins the meeting.
Everyone becomes considerably less enthusiastic.
Eventually, a proper AI implementation team begins to emerge.
You need:
A business owner.
A process expert.
AI expertise.
Data and integration expertise.
IT.
Security.
Governance.
Change management.
Actual users.
And preferably somebody from Finance, because at some point it would be useful to establish whether this thing is creating economic value.
The team maps the workflow.
They identify where AI genuinely helps.
More importantly, they identify where it doesn’t.
Information gets connected.
Permissions are established.
Instructions and prompts are developed.
Processes are redesigned.
Guardrails are added.
Outputs are tested.
Human approval is inserted where human judgment still matters.
And the AI finally gets tested against the ugly, messy situations employees and customers encounter in the real world rather than only the beautiful examples everyone saw in the sales demonstration.
Weeks become months.
There are meetings.
There are revised meetings.
There are meetings about why there are so many meetings.
But eventually…
It works!
The AI is ready.
Now all we have to do is give it to the employees.
What could possibly go wrong?
Stage 5 — Thud
Launch day!
The CEO sends an email.
There is an AI Town Hall.
Training videos have been created.
An internal AI Champion has been appointed.
Someone has invented an acronym.
There may even be T-shirts.
Management expects productivity to explode.
Week one looks promising.
Everyone tries it.
Week two is quieter.
Week four arrives.
Usage begins falling.
Some employees don’t trust the answers.
Some don’t understand when they are supposed to use it.
Some have discovered their own unofficial AI workflows using completely different products.
Others discover that using the approved company AI requires six additional steps, so they quietly return to doing things the old way.
Managers ask:
“Why aren’t people using the AI?”
The implementation team says they need more training.
The users say it doesn’t fit how they actually work.
IT says the requested integrations were not in scope.
Security says several capabilities had to be disabled.
Finance asks where the ROI is.
The AI vendor sends an invitation to discuss expanding the contract.
Excellent timing.
And then comes the question every executive sponsoring a major technology initiative eventually hears:
“Who is responsible for this?”
Uh oh.
Welcome to:
Stage 5 of the AI Circle of Sorrow.
Fired
An epiphany takes place sometime between cleaning out your office and explaining to your spouse why you suddenly have considerably more availability for weekday lunches.
What could we have done differently?
Quite a lot, actually.
And this is where the AI Circle of Sorrow becomes useful.
Because the AI itself was not necessarily the problem.
How we approached AI was.
Here is what was learned.
1. Enterprise AI Does Not Magically Understand Your Business
Modern AI can be astonishingly capable.
That does not mean it automatically understands:
Your customers.
Your products.
Your terminology.
Your pricing.
Your policies.
Your permissions.
Your processes.
Your exceptions.
Or what a good answer actually looks like inside your company.
That context has to come from somewhere.
The AI may need company knowledge.
It may need access to business systems.
It may need retrieval.
It may need APIs.
It may need carefully designed instructions.
It may need business rules.
It may need approval steps.
And it needs to be evaluated against the outcomes that actually matter.
Buying the AI is the beginning of the implementation, not the end of it.
2. AI Will Not Fix a Bad Process
This lesson survived perfectly from the CRM era.
Technology does not magically fix a broken business process.
AI can make a good process dramatically faster.
It can also help you execute a bad process dramatically faster.
Automating unnecessary work does not suddenly make that work valuable.
Before asking:
“Where can we use AI?”
Ask:
“What are we trying to improve?”
What takes too long?
Where is the friction?
What is expensive?
Where is repetitive work being performed?
Where is useful knowledge difficult to find?
Where do errors occur?
What business outcome would improve if this process changed?
Then determine whether AI is actually the right tool.
3. Build the AI Implementation Team Before You Buy the AI
Do not select an enterprise AI platform and then start wondering how you are going to implement it.
Before you buy, know who owns:
Business Discovery
What problem are we solving?
What business outcome should improve?
How will success be measured?
Process and Workflow Design
Where does AI belong?
What changes when AI enters the workflow?
What work disappears?
What new work appears?
Data and Integrations
What information does the AI need?
Where does that information live?
Is it accurate enough?
Can the AI access it securely?
Security and Governance
What can the AI see?
What can it say?
What can it do?
What information remains restricted?
What actions require human approval?
AI Configuration and Evaluation
How should the AI behave?
What does a good result look like?
How will quality, usefulness and reliability be evaluated?
Change Management and Training
Why will employees want to use it?
How does it improve their jobs?
How will established habits change?
One partner may cover several of these areas.
You may use multiple specialists.
Either way, somebody needs to own the complete outcome.
Otherwise your AI project becomes a relay race where everyone runs their section brilliantly…
and the baton gets dropped at every handoff.
4. Give Employees a Reason to WANT to Use AI
This may be the biggest lesson of all.
You cannot mandate your way to transformational AI adoption.
If using the AI means:
Another application.
Another login.
Another browser tab.
Another workflow.
Another set of steps.
And another task employees must perform…
Congratulations.
You have invented more work.
The best enterprise AI should make work easier.
Remove repetitive tasks.
Reduce research.
Find information faster.
Eliminate unnecessary administration.
Improve decisions.
Help employees create better work.
Help customers get better answers.
And ideally, do these things inside the workflows employees already use.
AI should become part of the workflow rather than another workflow.
Make the benefit to the employee crystal clear.
When people discover that something genuinely saves them time or helps them become more successful, adoption becomes a very different conversation.
Fear is a terrible long-term adoption strategy.
Value works much better.
5. Your AI Project Will Never Really Be Finished
CRM implementations evolve.
AI implementations may evolve even faster.
Models change.
Capabilities change.
Costs change.
Information changes.
Business processes change.
Policies change.
Regulations change.
Employees discover new use cases.
Customers behave differently.
New risks appear.
Better technology arrives.
AI therefore requires ongoing:
Evaluation.
Measurement.
Governance.
Optimization.
Training.
Workflow improvement.
Cost management.
Experimentation.
This isn’t necessarily a disadvantage.
It is simply the nature of the technology.
The companies that become great at AI will not be the ones that successfully complete an AI project.
They will be the companies that build the capability to continually improve how humans, workflows, information and AI work together.
How Do You Avoid the AI Circle of Sorrow?
Start in this order:
- Define the business problem.
- Define the measurable outcome.
- Map the existing workflow.
- Determine whether AI is actually the right tool.
- Understand the information and integration requirements.
- Define security, permissions and governance.
- Design the future workflow around humans and AI.
- Test the AI against real-world situations.
- Give employees a compelling reason to use it.
- Measure the results and keep improving.
Notice what is missing from Step 1?
Buy AI.
That comes later.
AI Transformation Is Not the Same as Buying AI
AI can absolutely transform a business.
But buying AI is not AI transformation.
Running a pilot is not AI transformation.
Giving everyone access to a chatbot is not AI transformation.
Putting an AI button inside your software is not AI transformation.
And putting the letters AI into twenty-seven slides of the corporate strategy deck definitely is not AI transformation.
Real AI transformation happens when technology changes how useful work gets done and produces a measurable improvement for employees, customers or the business.
The winners will:
Start with the problem.
Design the workflow.
Understand the information.
Choose the technology.
Establish the guardrails.
Measure the outcome.
Win employee adoption.
Keep improving.
Do that and AI can become one of the most important productivity and competitive advantages your organization has ever deployed.
Do it in the opposite order…
and I will probably see you somewhere inside:
The AI Circle of Sorrow
Frequently Asked Questions About AI Project Failure
What is the AI Circle of Sorrow?
The AI Circle of Sorrow is a five-stage pattern in which an organization becomes excited by an impressive AI demonstration, buys the technology, discovers significant hidden implementation work, invests more time and money integrating it, launches it and then struggles with employee adoption and measurable business value.
Why do enterprise AI projects fail?
AI projects can fail when organizations start with technology rather than a clearly defined business problem. Other common challenges include poor access to business information, missing integrations, unclear ownership, inadequate workflow design, governance issues, unrealistic expectations and weak user adoption.
What should a company do before buying enterprise AI?
A company should define the business problem, measurable outcome, target workflow, information requirements, security requirements, implementation ownership and adoption strategy before selecting the AI technology.
Can AI fix a bad business process?
AI can accelerate or automate parts of a process, but it does not automatically make a poorly designed process valuable. Organizations should determine what work should be removed, simplified or redesigned before deciding what to automate.
Why is AI adoption difficult?
AI adoption becomes difficult when the technology creates extra work, sits outside existing workflows, produces results employees do not trust or fails to provide an obvious benefit to the people expected to use it.
Does enterprise AI need company data?
Many enterprise AI use cases require access to relevant business information or systems. The exact requirements depend on the use case, but the information should be sufficiently accurate, current, relevant and appropriately permissioned for the task.
How should a company measure AI ROI?
Measure AI against the business outcome the project was intended to improve. Depending on the use case, this might include time saved, costs reduced, revenue improved, response times shortened, errors reduced, productivity increased or customer outcomes improved.
