AI Implementation Diagnostics for Businesses

AI Project Failed? Take a Test to See Why!

95% of gen AI pilots do not make it to production for a plethora of reasons. Learning what went wrong is the first step to making it better. Complete our AI Implementation Failure Test to uncover the hidden reasons and get valuable insights.

9 min

Average Completion Time
6

Assessment Areas
100%

Actionable Insights

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understand your AI project better

Before we start tell us more about your business and AI project

Let’s start easy! Tell us about your AI Strategy

AI Strategic Alignment Diagnostics

What was the main goal of your AI project?

Innovation experiment. We wanted to “try AI” without a defined outcome

Operational efficiency. We aimed to automate processes or cut costs

Customer experience. We wanted to add new capabilities for our users

New revenue line. We planned to monetize a premium AI feature

Choose one option from the list

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

Tell us about your data approaches

AI Data Foundation Diagnostics

How was your training data labeled or validated?

We systematically labeled and QA-checked with domain experts.

We partially labeled the data or crowd-sourced.

We had auto-labeled or synthetic data.

Our data was not labeled. We used unsupervised methods.

Choose one option from the list

Time to look under the hood of your AI

AI Technical Execution Diagnostics

What type of AI solution did you implement?

Custom model built from scratch.

We fine-tuned an open model (LLM, vision).

We used API-based integration (e.g., OpenAI, AWS, Google).

Vendor-black-box solution.

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

Share a bit more about your AI project post-launch

AI Product Integration Diagnostics

How did you gather the feedback after AI product/feature deployment?

We built an automated feedback loop connected to model retraining.

We gathered manual feedback from a limited number of users.

We carried out occasional qualitative reviews.

We had no feedback process.

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

Give us a few more details about your team

AI Organization & Ownership Diagnostics

How many people were actively dedicated to AI delivery?

6 + specialized roles (DS, MLOps, PM).

2–5 generalists juggling roles.

1 full or part-time contributor.

Outsourced completely.

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

Last, but not least…

AI Product Sustainability & ROI Diagnostics

Which of these best describes your current pain point?

Unclear ROI.

Data or accuracy issues.

Integration or adoption failure.

Team capacity and skills.

No pain points.

Choose multiple options from the list