Riseplan from Roast & Rise
AI-Ready Data Foundation
Make your data strong enough to power— not sink—your AI ambition.
This RisePlan shows founders, ops/IT leads, and AI champions how to audit, score, and strengthen the data that matters. You'll walk away with a practical system for spotting AI-killing weak points, targeting the few fixes that truly count, and building a data foundation you can trust.
Course thesis
Unready data is the unglamorous reason most AI projects quietly die. Citing Cloudera and Harvard Business Review Analytic Services' 2026 findings that only 7% of enterprises claim data is fully AI-ready, while Gartner warns 60% of unsupported projects will be abandoned, this RisePlan delivers a hands-on method to audit, score, and fix the operational data at the root of AI success.
What you leave with
By the end, you’ll know exactly which of your systems fuel AI, see where your data fails—or holds up—and act on a plan that lifts your AI chances from dead-on-arrival to durable.
For
Founders, ops/IT leaders, and executives about to launch or fund an AI initiative inside a fast-moving enterprise or scaleup; anyone facing live decisions about systems, data, or investments for AI.
Workflow
Operational data audit and improvement: from system mapping, quality scoring, and prioritization, to action plan, quick wins, and review cadence.
Change
Audits data systems and records for AI readiness, scores quality and access using a simple, team-friendly method, selects and acts on the most urgent fixes, then sets a cadence to prevent decay.
What you can do
Use these as checks while you move through the plan.
Map which systems and record types actually feed your AI projects.
Score data quality and access on a pragmatic, non-technical scale.
Spot where low-quality or inaccessible data will kneecap AI outcomes.
Prioritize the two or three highest-leverage improvement actions.
Maintain data readiness with a simple ongoing review rhythm.
Chapters
01
See the Ground: Map Your Data Inputs
Learn to build a real map of every system and record feeding your AI projects—so nothing critical hides in the dark.
Why this matters in the workflow
Cloudera and Harvard Business Review found only 7% of enterprises rate their data as truly AI-ready. Most learn, too late, that ‘unseen’ systems and shadow records can sink the project after funding and fanfare. Gartner’s warning is direct: 60% of AI projects unsupported by ready data will be abandoned. The first move? See the ground. You must know _exactly_ which operational systems and record sets actually feed, or will feed, your planned AI.
No map means black boxes. Black boxes mean blind spots, hidden dependencies, and trouble. Fixing data without a live, trusted map is guesswork—expensive and slow.
The working model
Quality checklist
Every system and record type actually used by the AI project is listed, not just the official ones.
Both technical and business owners have reviewed and confirmed the map.
Shadow/Unofficial sources are clearly identified.
Each entry lists a real access contact.
Data flows are described simply (API, export, manual).
Critical and non-critical sources are distinguished.
Common mistakes
Leaving off unofficial or manual data flows.
Assuming the architecture diagram matches reality.
Listing systems without noting the exact table/extract used.
Skipping confirmation with business users.
Hiding doubts—mark unconfirmed sources openly.
Checkpoint
Can you show a stakeholder which systems and records feed your AI—down to shadow or manual sources?
Exercise
Draft Your AI-Input Systems & Records Map
Steps
- Select one live or soon-to-launch AI project.
- List every system, integration, and record set the project pulls data from—directly or via manual workaround.
- For each, note:
- System or data source name
- Who owns or manages access
- How data flows in (API? manual upload? scheduled export?)
- Critical? (Yes/No)
- Shadow or unofficial? (Yes/No)
- Confirmation status (Confirmed/Unconfirmed)
- Share the draft with at least one technical and one business stakeholder. Ask: “What’s missing, and what’s here that isn’t actually used?”
- Update the map with feedback.
You can use a table, spreadsheet, or simple diagram—whatever ensures nothing stays hidden.
Use this at work tomorrow
Ask your team, "What’s the ugliest workaround or shadow data our AI actually consumes? Let’s map it."
02
Score What Matters: Assess Data Quality and Access
Build a simple, clear scorecard to gauge your mapped AI data inputs—accessible to any team. Use evidence, not jargon, to name shaky foundations before they collapse a project.
Why this matters in the workflow
Unready data is why most AI projects die, not hyped algorithms. Cloudera and HBR found only 7% of enterprises call their data fully AI-ready. Worse, 27% say it's not very or not at all ready. Per Gartner, six in ten AI projects without solid data will be abandoned—quietly. Yet leaders still push ahead, guessing their data is 'good enough.'
Assumption kills. Quality and access gaps in your source data don’t just lower accuracy—they can make AI fail, hallucinate, or, more likely, fizzle out. The point isn’t to punish; it’s to find weak spots before they send your spend and credibility off a cliff.
The working model
Quality checklist
Scores reflect real, recent evidence—not just gut feel.
Notes are specific: they name actual gaps, incidents, or blockers from the past quarter.
Non-technical reviewer understands and can question each score.
Every mapped input eventually receives a scorecard entry, not just the 'easy' ones.
Team visibility: the scorecard is shared back to both business and technical teams for comment/workshop.
Common mistakes
Scoring inputs based on vibe or reputation, not demonstrated issues.
Evidence is generic or outdated—"it’s usually fine".
Scorecard is kept private, making mistakes invisible.
Non-technical reviewers are skipped, reducing cross-team trust and clarity.
Jargon or technical terms make it unreadable to business leads.
Checkpoint
Can you hand your AI Data Readiness Scorecard to a business owner—and have them both understand and challenge the scores, using your notes?
Exercise
Run the AI Data Readiness Scorecard on a Real System
You will:
- Take one mapped system or record set from your AI-Input Map.
- Score its Data Quality and Data Access, using the plain 0–3 scale and supporting notes.
- Share your findings (even rough) with a trusted colleague outside IT—request their punchiest pushback or clarifying question.
Steps:
- Select a mapped input (e.g., HR time-tracking data, eCommerce orders, marketing events log).
- For Data Quality, ask: How accurate, complete, and consistent is this data—what’s the latest evidence?
- For Data Access, ask: How easily can the right people and systems get at this data now—and what blocks them?
- Assign your 0–3 score for each, with a 1–2 sentence evidence note.
- Copy the template below. Fill in. (Don’t polish.)
- Send/share your draft to a non-technical peer and invite blunt feedback: Does your score and evidence make sense to them?
You have 15 minutes. The aim is clarity, not perfection.
Use this at work tomorrow
Run a quick AI data scorecard session on your most plugged-in system. Share the result with a peer outside IT—and let them challenge your findings.
03
Spot the Sabotage: Surface the AI Killers
Focus on the true showstoppers. Pinpoint the minimal set of data flaws—by evidence, not gut feel—that could quietly destroy your AI project. Build a short threat list leadership can't ignore.
Why this matters in the workflow
Most AI projects don’t crash with a bang—they stall in slow motion. The big reason: silent, ugly gaps in the data running beneath. It’s almost never because someone forgot a dashboard metric or missed a system. It’s one or two killer flaws, hiding in plain sight, that quietly sabotage all downstream results.
Cloudera and Harvard Business Review (2026) put it simply: only 7% of enterprises rate their data as fully AI-ready. And in Gartner’s model, 60% of AI projects unsupported by sound data end up abandoned. Most teams try to fix everything—a recipe for inertia. You only need to name and act on the top threats.
The working model
Quality checklist
List includes no more than three threats.
Each threat has specific, live evidence (not hypothetical).
Impact is clearly scored and explained.
At least one non-technical stakeholder reviewed the list.
Every threat maps back to a real AI project outcome—no cosmetic filler.
Common mistakes
Listing every minor issue as a top threat.
Lacking concrete supporting evidence.
Over-complicating language—losing business readers.
Skipping review by a project sponsor or non-technical owner.
Using generic risks (‘bad data’, ‘slow system’) with no link to AI outcomes.
Checkpoint
Can you name two or three evidence-backed data issues that would genuinely derail your AI project, and would your leadership agree?
Exercise
Draft and Share Your AI Data Sabotage Threat List
Steps
- Review your AI-Input Systems & Records Map and your last quality/access scorecard.
- For each mapped record/system, ask: If this failed—would our AI project still be safe, valuable, and compliant?
- Write down concrete threats that would ruin the AI outcome. Use evidence (recent data, known gaps, provable access issues).
- Score Impact (High/Medium/Low) and add a short note of evidence for each.
- Select the most serious two or three. Ignore or archive the rest.
- Share your list with your project lead or sponsor. Ask: "Are we willing to go live with these risks as-is?"
- Capture their response and any new threats they see.
Output:
- A one-page AI Data Sabotage Threat List, evidence-backed, short, readable by both technical and business leadership.
Use this at work tomorrow
Draft a one-page threat list that names only the worst AI-killing data gaps—review with your sponsor before you even think about fixes.
04
Make the Fix: Deliver High-Leverage Improvements
Turn your threat list into concrete, visible progress. Build a prioritized fix plan, act on the ugliest blockers first, and prove the improvement where your AI needs it most.
Why this matters in the workflow
Cloudera and Harvard Business Review found that only 7% of enterprises see their data as AI-ready. Gartner warns 60% of AI projects will be abandoned if data issues are not solved. Most fail quietly—not because people don’t care, but because fixes are scattered, slow, or invisible. The antidote: focused action, not hand-wringing. This is where theory meets visible change.
High-leverage improvements are not about cleaning everything. They’re about knocking out the two or three broken data flows most likely to kill your AI hopes. It’s how you create wins the business can see and trust.
The working model
Quality checklist
Covers only 2 or 3 high-impact blockers, not a generic clean-up list.
Each fix names a clear, human owner—no groups, no "TBD".
Done criteria are concrete, measurable, and can be checked in <30 days.
Pilot proof ties directly to an actual AI workflow outcome, not a vague technical metric.
The plan is shared with both technical and business sponsors, visible for feedback and accountability.
Common mistakes
Listing more than three fixes—diluting focus and causing stall.
Leaving owners as "IT", "Data team", or blank.
Writing vague outcomes ("data improved", "better quality") that can’t be tested.
Skipping the measurement step—so no proof the fix mattered to AI.
Failing to publicly share the plan—leading to lack of follow-through.
Checkpoint
Can you name two urgent data fixes, assign a real owner, and show how success will appear in your live AI project? If not, revisit your threat list and clarify the business impact before moving forward.
Exercise
Build Your Prioritized AI Data Fix Plan
Steps:
- Bring up your AI Data Sabotage Threat List. Select the two or three highest-impact data blockers.
- For each, fill in the provided structure: issue, owner, what ‘done’ looks like, and how improvement will be measured in your target AI workflow.
- Share your draft plan with project stakeholders (IT, business leads, sponsors) for reality check and owner commitment.
- Pick one fix to implement today (even if it’s just the first step—assigning an owner or sending a kickoff message).
Output:
A ready-to-use, team-visible AI Data Fix Plan covering the top 2–3 urgent data blockers for your pilot or live AI project.
Use this at work tomorrow
Draft your AI Data Fix Plan for your biggest workflow gaps, assign owners now, and put it in front of your sponsors—no silent files.
05
Keep It Clean: Set the Data Readiness Cadence
Make data health regular. Install a recurring review so your AI foundation doesn’t rot after launch. This chapter builds the muscle to revisit your data, catch drift, and make the next fix before the cracks show up in your AI.
Why this matters in the workflow
Even a strong data foundation can decay—new systems go live, old ones drift out of spec, and forgotten changes break the pipeline. Cloudera and Harvard Business Review Analytic Services report only 7% of enterprises feel fully AI-ready, in part because most treat data prep as a one-off. Gartner warns that 60% of unsupported AI projects get abandoned—often because problems only become visible after the project fails. Data readiness is a living process, not a single audit. If you don’t set a cadence, readiness vanishes quietly. Teams fall back to old habits. Decay sets in. The project stalls, or worse, your newest AI quietly starts making bad calls on rotten data.
The working model
You need a lightweight, recurring review—a pulse that checks your data inputs, quality, and fixes, and ties them back to live AI ambitions. This isn’t a bureaucracy. It’s a short, regular session (monthly or quarterly for most) with visible next actions and owners. The cadence works if stakeholders expect it, anticipate the questions, and see that decisions are getting made from it. Public, predictable, and visible beats perfect.
Quality checklist
Review is recurring and visible in calendar/invite.
Owner is named and committed for at least the next review.
Next review date is public to the team.
Standing agenda covers map changes, gaps, fixes, and next actions.
Sharing plan is clear—team knows how to find outcomes.
Common mistakes
Failing to actually schedule a recurring session—just talking about it.
Not naming (and informing) a responsible owner.
Agendas too broad or vague—turns into a generic team status meeting.
Updates and actions are hidden in private email or stuck with IT only.
Letting the cadence slip because "there’s nothing urgent right now."
Checkpoint
Can you show a public, calendar-backed data review schedule—with a clear owner and next date?
Exercise
Schedule and Announce Your Data Readiness Review
In the next 15 minutes:
- Open your calendar. Block 30 minutes for an AI Data Readiness Review, repeating every month or quarter.
- Name the owner—who will drive each session and post the outcomes?
- Draft the standing agenda (copy/adapt the template below).
- Write and send a short message or email publicly announcing the cadence: when it happens, what gets reviewed, who’s driving, and how results will be shared.
- (Optional: Post the first review date and owner in your team’s public channel or dashboard!)
Output: Data Readiness Review Schedule + Announcement
Copy the template—fill in for your own cadence, owner, and sharing plan.
Review After: Did you actually block time, name the owner, and put a notification where the team can see it?
Use this at work tomorrow
Block the next review in your team calendar, name the owner, and post one simple announcement by the end of the day.
30-day path
Week 1: Map actual AI data sources—clarify with owners.
Week 2: Apply quality scoring—focus on gaps, not averages.
Week 3: Select and drive the most urgent two or three fixes—track completion.
Week 4: Lock in a lightweight, recurring review schedule—document next actions.
Month 2 and beyond: Review, refresh, and repeat as AI footprint grows.
Success signals
100% coverage of actual, not assumed, AI input systems and records mapped.
Scorecard used and understood by at least one non-technical stakeholder.
At least two prioritized, evidence-supported data issues fixed or in flight within 30 days.
Ongoing review cadence agreed and visible with an assigned responsible party.
Tangible improvement in data quality or access as demonstrated in a pilot AI use-case.
Reflection prompts
Where does this topic show up in real work?
What behavior should change first?
What evidence would prove this Riseplan worked?
Manager checklist
Choose one owner for the behavior change.
Use the exercise on live work.
Review the output before scaling the habit.
Decide what changes after 30 days.
Want this shaped around your company?
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