Read This Before Scaling: Why 95% of AI Efforts Fail After Pilot (and How to Avoid Being One of Them)
Step 3 of the 🪜MAKR AI Transformation Staircase 🪜
You’ve mapped the opportunities. You’ve run the pilots. You’ve got at least one win (and hopefully more).
MIT’s latest research is clear: 95% of enterprise AI pilots fail to deliver measurable business outcomes.
Not because the tech doesn’t work. But because teams never turn the wins from pilots into repeatable, measurable operations.
If you’re a CCO or CRO, this is the moment where AI stops being a science experiment and starts being a line item with ROI expectations. And that’s where MAKR AI Transformation Staircase’s Stage 3, Keying Up AI Operations, comes in.
Why Many Fail at Stage 3
Based on the MIT study + other research, here are the most common breakdowns, with specifics:
Workflow Misalignment
MIT found many failed pilots were using generic AI tools without tailoring to existing workflows. The tools didn’t connect to how teams already work, so adoption dropped off.Poor Governance & Data Inputs
Without guardrails or defined data sources, AI outputs are inconsistent. This leads to trust issues. MIT notes that in-house builds often underperform compared to vendor-led ones (roughly 33% vs ~67% success rate).Lack of Meaningful Metrics
Many pilots are judged on “adoption” or “usage” rather than on impact. That’s a red flag. As one case study in “AI for Customer Success Automations” showed, only when they tracked reduction in operational costs and customer retention did the business case sustain.Scaling Without Standardization
Success in one pod or team doesn’t automatically generalize. When there are no standardized playbooks or shared processes, each team reinvents, which undermines efficiency gains. MIT found cross-organization variance in success was large.
That’s where keying up operations comes in.
However, when you successfully scale Customer Success with AI, the outcomes stop being about “adoption” and start showing up in retention curves, save rates, and dollar math. Stage 3 of the MAKR AI Transformation Staircase, Keying Up Operations, is where you build repeatability, discipline, and measurable business returns. Just look at these successful Customer Success examples:
PayPal + H2O.ai: Machine-learning churn prediction models cut model runtime from 6+ hours to minutes, enabling near-real-time churn scoring and proactive save motions. Case study
Audiobooks.com + Provectus: AI-driven churn prediction on AWS pipelines segmented customers into risk cohorts with high accuracy, powering proactive CS outreach and boosting retention in premium cohorts. Case study
CallHippo + Enthu.AI: Conversation AI flagged churn-risk signals in customer calls, enabling CSMs to act early. Results: 20% reduction in revenue churn and 13% increase in new revenue. Case study
Sigmoid ML Retention Campaigns: Predictive churn models across 15+ data sources powered targeted CS-led campaigns that improved customer retention by 70% vs. baseline. Case study
These are the kinds of curves Keying Up Operations is about: churn decreasing in double-digits, intervention speed coming in minutes not quarters, and save plays that move millions in ARR.
Three Critical Metrics to Embed in Stage 3
Notice the pattern: every example zeroes in on churn. That’s not a coincidence. Churn is one of the most P&L-sensitive levers in Customer Success—and one of the first places AI delivers fast, measurable wins. Think predictive saves, live risk scoring, and interventions that bend the ARR curve, not just more dashboards.
In the MAKR Transformation Staircase, Stage 3 is where AI has to graduate from “interesting activity” to hard financial outcomes. That means tying every pilot, model, and playbook back to the system metrics your board already watches:
NRR: Don’t just report adoption. Show the delta between predicted and actual renewal/expansion, and quantify how AI closed the gap.
GRR: Track lift in save rates—e.g., percentage of “at risk” accounts successfully retained after AI-triggered interventions.
Cost-to-Serve: Measure AI’s impact on marginal efficiency: time per CSM, cost per retained dollar, scale of accounts handled without headcount.
The metrics themselves aren’t new. What’s new, and what proves operational success, is demonstrating how AI changes their slope.
What You Can Do: A Specific Operating Plan
Here’s how you make Stage 3 real, with specificity and numbers:
Pick 2 High-Impact Pilots That Show Clear Metrics
E.g.An AI risk-alert pilot in renewals aimed to reduce time to risk detection by 40%.
A customer feedback summarization pilot to reduce manual summary time by 80%.
Set Up a Unified Dashboards with Leading Indicators
Example KPIs (world-class):Hours saved / CSM / month
Save rate for at-risk accounts (compare pilot vs control)
Time to detection/resolution
Retention lift (NRR or churn rate variance) + cost savings
Establish AI Governance & Playbook
Define allowed data sources, human oversight, tone/brand guardrails.
Build playbooks that live in tools your teams use already (Slack, Salesforce, Gainsight).
Use pre-mortems: “What could go wrong if we scale this without cleaning the data or standardizing the model?”
Measure & Communicate “Dollar Value” Wins Quarterly
Example: If a pilot yields 50 hours saved / month across 20 CSMs and those 50 hours are used to handle 10 more accounts, translate that into the revenue those 10 accounts bring.
Report savings or revenue impact in actual dollars in leadership meetings (not just % improvements).
Ensure Cross-Org Alignment
Because scaling often fails when CS AI moves ahead in isolation. Pull in Revenue Operations, Product, Support so workflows, data pipelines, customer feedback loop across teams are all integrated
The Bottom Line
Public perception, MIT reports, case study after case study: AI doesn’t fail because the models are bad. It fails because organizations scale mess, not method.
If you’re scaling before fixing process, governance, metrics, and workflow alignment, you’re handing your team a liability, not a growth lever.
Codify. Measure. Govern. Align. That’s the difference between an AI pilot that gets a headline and an AI transformation that delivers millions on the bottom line.
