This webinar walks through Backstory's go-to-market AI maturity model, showing what each stage of the curve looks like in practice and why most organizations get stuck in experimentation. Katie Stanforth and Haya Kamal use account planning as a real-world example to illustrate each level of maturity and explain what it takes to build the data foundation needed to scale AI workflows with confidence. This article includes time-stamped explanations of key moments in the video so you can quickly jump to the topics most relevant to you.
Note: This webinar was recorded prior to the company’s rebrand from People.ai to Backstory. As a result, presenters and on-screen materials refer to the company as “People.ai.”
Why Most AI Investments Are Falling Short
Video: 02:22–04:47
Despite widespread investment in AI, most organizations have not been able to move from experimentation to production at scale.
Key findings include:
70% of enterprises have AI pilots underway, yet fewer than 25% have moved a pilot into production at scale
64% of sales leaders report being disappointed in their AI tool outcomes — not skeptical, but disappointed
The gap between experimentation and scale is where most organizations are getting stuck
Understanding where your org sits on the maturity curve is the first step to closing that gap
The Go-to-Market AI Maturity Curve
Video: 04:48–12:00
Backstory developed this maturity model to help organizations orient where they are today and understand what it takes to move to the next level.
The five stages of the curve:
Not Started: Awareness of AI exists but time and bandwidth haven't allowed for meaningful investment
Awareness: AI tools are in use individually or informally, but there is no coordinated organizational approach
Experimentation: Specific use cases or teams are piloting AI with more invested effort to find what works
Scaling: AI workflows move from one-off to recurring — embedded into forecast calls, QBRs, and other rhythms of the business
Transformation: Go-to-market organizations are restructured to be AI-first, running proprietary models with multi-step, fully autonomous workflows for higher-level decision-making work
Where Most Organizations Are Today
Video: 12:01–13:26
Based on Backstory's view across its customer base, most organizations are somewhere between awareness and early scaling.
What this looks like in practice:
The "not started" bucket has shrunk significantly over the last six months
Many customers are attempting to scale but finding that certain workflows aren't getting adopted or aren't delivering value
Those organizations often fall back into experimentation — and that cycle is where most are getting stuck
Breaking out of experimentation is the focus of this session
Why Breaking Out of Experimentation Is So Hard
Video: 13:27–16:40
Scaling AI workflows in go-to-market and revenue operations requires overcoming a unique set of headwinds.
Key barriers include:
Cultural change management alongside a rapidly evolving AI landscape
Difficulty measuring adoption and quantifying outcomes in a space that has little history of doing so
A tendency to successfully scale low-value, AI-augmented tasks while struggling to move higher-value work forward
Trust erosion: when AI outputs lack accuracy or specificity, adoption suffers and value realization stalls
Nearly 40% of AI projects cite poor data quality as a root cause of failure
Over 45% of leaders identify data accuracy as the top barrier to AI adoption
The core challenge: AI output looks polished on the surface, making it hard to identify where data quality issues exist until trust has already broken down.
Account Planning Across the Maturity Curve
Video: 16:41–21:33
Account planning is a familiar use case that illustrates what each level of AI maturity actually looks like in practice.
How maturity shows up across stages:
Not Started: Reps manually fill out one-off PowerPoint or slide deck templates, pulling data from Salesforce individually; account plans are point-in-time and often go unreviewed after quarterly or annual planning cycles
Awareness: Account plans move into a centralized, reportable system — embedded in Salesforce or a platform like Backstory — so completion rates, freshness, and strategy can be tracked across key accounts
Experimentation: AI begins automating common account plan sections using publicly available data (company background, go-to-market context), providing marginal efficiency gains for reps completing account plans at scale
Scaling: Additional data sets are layered in — CRM fields, 8-K and 10-K filings, and revenue stories from actual customer interactions — to generate richer account strategy sections that reflect real engagement across the account team
Transformation: Account plans become evergreen documents; AI tracks progress against the strategy, surfaces what is on track versus at risk, and recommends prioritized next steps — without requiring manual updates from the account team
Demo: AI-Powered Account Plan Progress Updates
Video: 21:34–31:30
Live walkthrough of how Backstory uses its own platform and MCP integration to deliver real-time account plan updates.
What the demo shows:
Within Backstory's Claude instance, the Backstory MCP connector provides access to account plans, Salesforce data, and revenue stories
A natural language question — "Give me an update on the progress of my account plan for [account]" — triggers a lookup against the live account plan and all associated engagement
The output breaks down: action items discussed, what is in progress, what is at risk, and what is on track
Top next steps are prioritized by impact and potential in the account
The result can be shared asynchronously with the account team as a prompt for alignment and execution
This workflow transforms account plans from static quarterly documents into continuously updated strategy tools any team member can query at any time.
The Noise of Nuance: What Gets Missed Without the Right Foundation
Video: 31:31–34:57
Poor data quality doesn't always look like missing data — it often looks like correct data that lacks the context to be meaningful.
Example: a $2M deal with high engagement scores, no flagged red flags, and weekly rep check-ins slips at quarter end because:
A CSM conversation revealed that budget had moved away from the economic buyer — but that information never reached the AE
Transcripts showed strong validation of the value proposition, but only from low-level managers without decision-making authority
A key persona required for this specific product type was absent from the deal, but no pattern recognition existed to flag it
The "noise of nuance" — the difference between what looks true and what is actually true and meaningful — is the core reason AI initiatives stall after experimentation.
Building the Right Foundation
Video: 34:58–41:50
Getting the data foundation right before scaling is what separates organizations that build lasting AI trust from those that cycle back into experimentation.
What not to do:
Context dumping — loading large volumes of raw information into a project or skill — dilutes quality, increases misinterpretation, wastes resources, and makes it hard to trace where outputs went wrong
What to do instead:
Ensure context is complete and specific: all activities should be matched to the correct account or opportunity so nothing is missing from the picture
Organize business context into time-bound, timestamped revenue stories so AI can distinguish what was discussed, what was completed, and what risks still persist
Learn from your own data: group similar scenarios from your own wins and losses rather than relying on generic benchmarks that don't reflect your business
Make answers transparent and traceable: outputs should link back to the source information, be customizable to your business, and never operate as a black box
Resources and Next Steps
Video: 41:51–End
To continue learning and moving up the maturity curve:
Backstory customers: reach out to your CSM or AE to assess where your org sits and what it would take to move to the next stage
Use the digital agent at help.Backstory for quick questions
Connect with Katie Stanforth and Haya Kamal on LinkedIn to continue the conversation
Provide feedback to help shape future webinar content