Data Health Check shows the extent of the problem.
Data Assessment pinpoints the cause and remediation.
Discover identifies the hidden work outside systems - what both ERP and AI need.
Data Migration builds the new governed data foundation.
Fix the foundation → Both ERP success and AI success accelerate.
Common + Joint Benefit Factors of AI and ERP Projects
AI and ERP initiatives are often treated as separate programs… But
they fail for almost the same reasons and succeed when the same foundational elements are in place.
Why AI Projects Fail
- Poor Data Quality – inconsistent, incomplete, duplicated, outdated.
- Unclear Data Ownership – no stewards, no accountability.
- Shadow Processes – work done in Excel/SharePoint/side systems AI cannot learn from.
- Undefined Business Use Cases – AI built without measurable outcomes.
- Model Training on Bad Data – “garbage in, garbage out.”
- Lack of Governance – no standards for accuracy, privacy, lineage.
- Integration Complexity – AI can’t access the structured transactional data it needs.
Why ERP Projects Fail
- Bad Legacy Data – causes rework, delays, and misconfigured processes.
- Unmapped or Broken Processes – ERP configured to match broken workflows.
- Shadow ERP – side spreadsheets and access databases ignored, so the ERP doesn’t work.
- Poor Requirements Gathering – generic demos, wrong-fit solution, unrealistic plans.
- Lack of Data Governance – unclear owners, unvalidated migration data.
- Underestimated Migration Effort – manual mapping, cleansing, validation explode costs.
- Low User Adoption – because the data is wrong or processes don’t match reality.
If a company isn’t ready for ERP, it isn’t ready for AI either.
Because
data health,
process clarity, and
governance maturity drive both.
What Make Big Impacts in Solving This Challenge:
🩺 1. Data Health Check
Purpose: Identify the hidden, systemic data issues that will break both ERP and AI projects.
Finds:
- duplicates
- aging master data
- inconsistent codes
- multiple truth sources
- missing governance
- shadow databases
This gives you the
true risk picture.
2. Data Assessment
Purpose: Pinpoint the exact functional areas where data is causing failures.
Delivers:
- Data quality scoring
- Fit-gap of legacy vs future needs
- Mapping issues
- Standards to fix the problem
Outcome:
→ Clear action plan for
data cleansing + quality uplift before ERP or AI is attempted.
3. Discover (Shadow ERP Mapping)
Purpose: Reveal the real workflows—the 30–70% of work happening outside the system.
Finds:
- Excel accounting workarounds
- Manual production planning
- Offline approvals
- Duplicate entry systems
- Missing features employees filled in themselves
Outcome:
→ ERP design becomes accurate
→ AI assistant can automate the “shadow” work
This connects perfectly with the next step and true value:
AI assistants and agents can now fill and automate the shadow processes.
4. Data Migration (The New World)
Purpose: Build a clean, governed, integrated dataset that both ERP and AI can rely on.
Delivers:
- Clean master data
- Standardized transactional history
- Governed data structures
- Validated mappings
- Repeatable migration process
Outcome:
→ ERP goes live on time and on budget
→ AI has a pristine dataset to work with on Day 1
Don’t Wait for the Perfect Moment. Create It.
ERP and AI failures don’t happen at go-live. They happen months or years earlier when data issues, shadow processes, and unclear workflows are ignored.
The good news? Every one of these risks is fixable before you spend a dollar on new software or AI tools.
The companies winning right now aren’t the ones buying technology first, they’re the ones preparing their data foundation now.
If you want your next ERP or AI initiative to actually deliver value instead of surprises, start with the steps above.
You don’t need to wait for a budget cycle or a new project kickoff.
You can take the first step today.
Book a Discover Assessment or Data Health Check now, and get clarity long before vendors start selling you solutions.
Your future ERP and AI success depends on what you do next. Not someday, but
now.
