AI Implementation Diagnostics for Businesses

AI Project Hit a Roadblock? Take a Test to See Why

Not all AI implementation projects are successful. In fact, a 2025 MIT study found that only 5% of gen AI pilots make it to production. To help companies better understand the reasons behind their unsuccessful AI implementations, we created a self-diagnostic quiz. It will help you pinpoint the key mistakes made during the planning and development, as well as provide actionable insights on how they can be fixed.

4 min

Average Completion Time
6

Assessment Areas
100%

Actionable Insights

START THE ASSESSMENT

Tell us a bit more about your AI Strategy

AI Strategic Alignment Diagnostics

Were business success metrics defined before development began?

Yes. We set clear KPIs (accuracy, savings, retention, etc.).

Partially. We discussed goals but didn’t identify quantitative or qualitative success metrics.

No. We focused on building, not measuring.

Choose one option from the list

Now let’s talk about your data

AI Data Foundation Diagnostics

How would you describe the data you used for your AI Project?

We centralized and structured data before using it.

We had scattered data, but it was partially usable.

Our data was mostly unstructured or incomplete.

We used external datasets only.

Choose one option from the list

Give us more technical details, please!

AI Technical Execution Diagnostics

How did you deploy and monitor your AI product/feature after launch?

Full MLOps pipeline (CI/CD, monitoring, retraining).

Manual checks and occasional retraining.

Static deployment, no monitoring.

Still in prototype stage.

Choose one option from the list

Let’s discuss your post-launch activities

AI Product Integration Diagnostics

How far did the AI reach in your product lifecycle?

Our AI product/feature was launched and used by users daily.

We made a pilot and launched it for selected users.

AI was an internal demo only.

We never integrated the AI with our main system.

Choose one option from the list

Time to analyse your team

AI Organization & Ownership Diagnostics

Who led and coordinated the AI initiative in your company?

We had a cross-functional internal team (product owner, data team, and developer team).

Dedicated R&D group, but no product owners.

External vendor or contractor.

No clear owner; leadership shifted.

Choose one option from the list

Let’s discuss strategic alignment

AI Product Sustainability & ROI Diagnostics

How often do you review model performance metrics?

Continuously with dashboards.

Quarterly or semi-annually.

Only when problems appear.

Never reviewed after launch.

Choose one option from the list

One Final Step