Why Most AI Adoption Fails
You started an AI project for your department or deployed an AI tool to your teams, but something’s not right. Maybe:
- They gave up on using the tool after a few weeks
- You can’t tell if the tool increased or decreased productivity
- Employees get inconsistent answers or results
- People aren’t sure what information they are allowed to paste or upload into AI tools
- The project sounded simple in the demo, but became messy in reality
- You’re thinking about ditching the tool for a similar-sounding one
If this sounds familiar, the problem may not be the AI tool. It may be how you rolled it out.
Common AI implementation failures and how to solve them
AI tools can sound promising. In many cases, they can deliver on that promise. Before you blame the tool, ask yourself whether you rolled it out to solve a clear business problem, and how you expected it to solve that problem.
- Did you define the workflow AI was supposed to improve?
- Did you put someone in charge of a plan to drive adoption of the tool?
- Did you define success for the tool, and is your team in alignment about that definition?
- Did you check that the information the AI will be relying on has a consistent architecture?
Without a clear plan for rollout, most AI implementation goes nowhere. Your employees are used to working a certain way. Unless they understand how to use an AI tool and how it can improve their workflow, they’ll see no reason to change. You’ll be left paying for a new AI tool with the same problems as before.
AI adoption failure 1: no clear workflow
If you drop an AI tool in your employees’ laps and ask them to fiddle until they become more productive, you’re signing up for a failed AI project. You’ll see sporadic and inconsistent use of the AI tool, and your teams won’t know how to use the output they get. Over time, they’ll likely give up using the tool entirely.
To fix this issue:
- Choose one specific workflow to improve
- Define the current issue or pain point the tool could help with, drawing on feedback or complaints from your employees
- Decide which aspect of the issue could be improved, such as: speed, clarity, safety, consistency or compliance
- Choose a metric to measure improvement in the issue
- Communicate to your team how the AI tool fits in their workflow, what issue it addresses and how to use it
AI adoption failure 2: messy information architecture
AI can’t effectively surface or analyze information about your business if that information is scattered, outdated, duplicated or poorly governed. You need good information architecture to correctly enable AI tools. Otherwise, you’ll lose productivity when your team has to track down hallucinations from an AI tool searching for information it can’t see or that doesn’t exist.
To fix this issue:
- Clean up the source material the AI tool draws from
- Remove duplicates and stale documents
- Fix permissions to match current roles, and give the AI the permissions it needs
Healthy information architecture makes your files and information easier to use for humans and for AI tools. Read our series about information architecture if you’d like to learn more.
AI adoption failure 3: untrained users
AI tools aren’t always intuitive. The way they fit into your employees’ workflows certainly won’t be without adequate training. If your employees are finding AI output too vague, accepting incorrect answers from AI tools because the AI tool “sounds confident”, or getting frustrated and giving up, they need training.
To fix this issue:
- Teach your teams how to write practical, effective AI prompts
- Create reusable prompt templates customized for your workflows
- Give your team reference examples of good and bad AI outputs for your use case
- Train users to review, challenge and refine AI-generated work
AI adoption failure 4: poor review and exception handling
AI output needs a review process, especially when the work affects clients, finances, compliance, security or reputation. If your workflow copies unreviewed AI-composed drafts into client communications, no one knows who’s accountable for AI mistakes, or there’s no process for identifying edge cases, you need to improve your AI review and exception handling policies. Otherwise, your team could find themselves regulatorily noncompliant…or just in an embarrassing situation.
To fix this issue:
- Define what AI can suggest versus decide
- Require human review (a “human in the loop”) for high-risk and client-facing work
- Create rules that escalate to a human for exceptions
- Start with AI-assisted workflows, not fully AI-automated ones
AI adoption failure 5: automating too much too soon
AI-based automation can be a powerful tool, but only if the workflow and team are prepared. You can’t connect AI to an unstable process or messy system and expect useful results.
To fix this issue:
- Start with a small number of tightly controlled use cases in a single workflow
- Use AI to summarize, draft, classify or retrieve, rather than giving it autonomous access
- Expand your AI usage only once your team trusts and understands the tool – and has cleaned up your processes and system to work with AI
Ask yourself these questions before you buy another AI tool
It’s tempting to blame a failed AI project or AI implementation failure on the AI tool itself, especially since those tools aren’t usually designed for small businesses. Sometimes AI is to blame. But before you toss out the work you’ve done, consider the following questions.
- What business problem are we trying to solve? Be as specific as possible.
- What happens if we do nothing?
- Who is affected by the current friction in this process? Have I talked to them about what they need or how they think AI or automation could make their work easier?
- What have we already tried?
- Why did the last tool, process or rollout stall or fail? Would switching to a different tool prevent this from happening again?
- What information does AI need to do useful work in this workflow?
- Who reviews the output of AI tools in this workflow?
- Who owns the decision if AI gets it wrong?
If these questions are hard to answer, another tool will not solve the problem. You need to uncover and resolve the underlying issue before AI deployment will succeed. Otherwise, you’ll be back where you started.
What successful AI adoption looks like
Successful AI adoption is usually practical, focused and boring. AI marketing makes you expect a flashy paradigm shift, but it’s more realistic to start with small but meaningful improvements.
Successful AI adoption starts with selecting one workflow to add AI to.
You define a clear business outcome to determine whether the AI tool added value.
You clean up the source knowledge before connecting AI to anything.
You review the permissions you’re giving to the AI tool and assess the risks of using the tool.
You teach the people who will be using the tool on how it works, what to expect and who to escalate issues to.
Your team thoroughly tests the AI tool’s output before deploying the tool.
You run the workflow with the new tool until you’re sure it works as expected and your team is comfortable with it. Only then do you expand AI deployment.
How to recover a failed AI project – or plan a new AI deployment to succeed
If you’re having an AI issue and are not sure whether the problem is the tool, the rollout or the information architecture underlying it, stop before you spend more on a new AI tool or abandon AI entirely. Reach out to Wellington Street Consulting for a consultation. We’ll look at your workflow, source knowledge, permissions, user training and review process. We’ll find the root cause of your AI project issues and create a plan to fix them.
If you’re planning to add AI to a workflow at your business and want to ensure success, WSC can help with that too.
Reach out today for help AI implementation, preparing for AI, AI troubleshooting and more.
