What Your Peers Decided: Anonymized Decision Patterns from AI Briefing Attendees

Executive Summary

• This document reports what happened after executives like you sat through the same briefing you just attended. It is updated after each workshop cohort. Every data point is anonymized. No company names, no identifying details — just the aggregate pattern of what people in your position decided, what they did first, and what they wish they had known sooner.
• The research predicts what you will find. McKinsey’s 2025 State of AI survey (n=1,993, November 2025) reports that two-thirds of AI-using organizations remain in experiment or pilot mode. Only 6% qualify as high performers. The briefing attendees mirror this distribution — and the ones who move fastest share three characteristics.
• Social proof matters more than benchmarks. 92% of B2B buyers trust peer recommendations over vendor claims (Trustmary, 2025 aggregation of 65+ studies). A Gartner-scale maturity model tells you where the market is. This document tells you what the person sitting two chairs away from you actually did Monday morning.
• This is a living document. The data below includes placeholder structure for the first five workshop cohorts. As each cohort completes, the anonymized patterns replace the baseline projections.

How to Read This Document

This is not a benchmark report. It is a decision mirror.

Each section shows the distribution of choices across briefing attendees — where they self-identified on the maturity spectrum, which actions they prioritized, which concerns dominated their questions, and what they reported back 30 and 60 days later. The value is not in the averages. The value is in seeing which decisions correlated with progress and which correlated with stalling.

The baseline projections below draw from six major 2025-2026 surveys: RSM Middle Market AI Survey (n=966, March 2025), McKinsey State of AI (n=1,993, November 2025), NTT DATA Global AI Report (n=2,567, September-October 2025), Bain Executive AI Survey (n=1,300+, Q3 2025), State of the CIO 2025 (Foundry/CIO.com, 2025), and the HBR/NewVantage AI & Data Leadership Benchmark (January 2026). These baselines will be replaced with actual attendee data as workshops proceed.

Section 1: Where Attendees Self-Identified on the Maturity Spectrum

Baseline projection based on mid-market survey data:

Note: MIT CISR’s sample skews toward larger enterprises. Mid-market companies with 200-2,000 employees typically self-assess 1-2 stages lower than the national benchmark — meaning Stages 1-2 will likely dominate briefing cohorts.

After Cohort 1: [To be populated] After Cohort 3: [To be populated] After Cohort 5: [To be populated]

The Pattern to Watch

RSM’s 2025 mid-market survey (n=966) found 91% report using generative AI — but 62% said implementation was harder than expected, and 70% admitted needing outside help. The gap between “using it” and “getting value from it” is where most briefing attendees will cluster. The attendees who self-identify as Stage 2 but have pockets of Stage 3 behavior are the ones who move fastest. They already have evidence that AI works in their organization. They lack the structure to scale it.

Section 2: Which Actions Attendees Prioritized First

Baseline projection — “What will you do first after this briefing?”

After Cohort 1: [To be populated] After Cohort 3: [To be populated] After Cohort 5: [To be populated]

The Pattern to Watch

The national data shows a clear sequence among companies that capture value. McKinsey’s high performers (n=~110, November 2025) do not start with tool selection or budget requests. They start with workflow redesign: 55% fundamentally redesigned workflows when deploying AI, compared to 18% of others. The briefing attendees who start with “understand what is already happening” (shadow audit) and “set the rules” (AUP) before “buy something” tend to report more progress at the 60-day check-in. This pattern — visibility, then governance, then investment — is consistent across every major survey.

Section 3: Which Concerns Dominated Q&A

Baseline projection — most frequently raised concerns:

After Cohort 1: [To be populated] After Cohort 3: [To be populated] After Cohort 5: [To be populated]

The Pattern to Watch

The concern that surfaces most often in the room is rarely the concern that matters most to the organization. Security and workforce anxiety dominate Q&A because they are emotionally urgent. ROI measurement and workflow redesign get fewer questions but predict more of the variance in outcomes. NTT DATA’s report (n=2,567, 2025) found that only 15% of organizations qualify as AI leaders — and the single most differentiating factor is not tool selection or security posture. It is strategic alignment: tightly coupling AI initiatives to business objectives. The briefing attendees who ask “how do I measure whether this is working?” tend to be further along than those who ask “which tool should I buy?” — even though both feel like reasonable first questions.

Section 4: What Attendees Reported at 30 and 60 Days

Baseline projection — expected 30-day outcomes:

Baseline projection — expected 60-day outcomes:

After Cohort 1: [To be populated] After Cohort 3: [To be populated] After Cohort 5: [To be populated]

The Pattern to Watch

The 60-day mark separates signal from noise. The RSM survey shows that 88% of mid-market executives say AI affected their organization more positively than expected — but McKinsey shows only 6% qualify as high performers. The gap is not enthusiasm. It is execution. The briefing attendees who report back at 60 days with specific questions — “the shadow audit found 14 tools, now what?” or “the pilot works in one team but the other two are resisting” — are the ones converting awareness into action. The ones who report nothing are not failing. They are experiencing the same inertia that keeps two-thirds of organizations in permanent pilot mode.

Section 5: The Emerging Profile of “Fast Movers”

This section will be populated after 3-5 cohorts provide enough data for pattern detection. Based on the national survey data, the expected fast-mover profile includes:

What the research predicts fast movers will share:

• They had at least one internal champion before the briefing. NTT DATA (n=2,567, 2025): AI leaders are defined by having senior leaders who demonstrate clear ownership. The briefing did not create their interest — it gave them the framework and evidence to act on it.
• They started with visibility, not purchases. McKinsey (n=1,993, 2025): companies that redesigned workflows outperformed those that bought tools and tried to fit them into existing processes. The shadow AI audit and the AUP are visibility moves.
• They treated AI governance as a business decision, not an IT decision. State of the CIO 2025: 75% of CIOs now collaborate closely with line-of-business leaders on AI strategy. The briefing attendees who bring a non-IT executive to the follow-up conversation tend to move faster.
• They did not wait for perfect data. RSM: 41% cited data quality as the top implementation challenge. The fast movers acknowledged the data problem and scoped their first pilot to a use case where existing data was sufficient — rather than launching a data cleanup project that delayed everything by six months.

After Cohort 3: [To be populated — replace projections with observed patterns] After Cohort 5: [To be populated — validate or revise fast-mover profile]

Key Data Points

What This Means for Your Organization

You just sat through a briefing that presented the same data every other attendee received. The research, the frameworks, the evidence — identical for everyone in the room. What separates the organizations that are measurably different in 60 days from those that are not is one thing: the first action taken in the week after the briefing.

The national data is clear on what that action should be. It is not buying a tool. It is not requesting budget. It is looking — rigorously, honestly — at what is already happening with AI inside your organization (the shadow audit), and then deciding what the rules are (the acceptable use policy). These two steps take less than a week, cost nothing, and create the foundation that makes every subsequent decision better.

This document will grow with each cohort. If you want to see how your decisions compare to the aggregate pattern as it develops — or if the briefing raised questions specific to your organization’s situation — I would welcome that conversation: brandon@brandonsneider.com.

Sources

1. RSM Middle Market AI Survey 2025. RSM US, March 2025. n=966 mid-market executives with decision-making authority. Independent survey of mid-market companies — highly relevant to this audience. https://rsmus.com/insights/services/digital-transformation/rsm-middle-market-ai-survey-2025.html

2. McKinsey Global Survey: The State of AI in 2025. McKinsey/QuantumBlack, November 2025. n=1,993 respondents. Independent consulting survey; sample skews toward larger organizations but methodology is rigorous. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

3. NTT DATA 2026 Global AI Report. NTT DATA, December 2025. n=2,567 senior executives across 35 countries and 15 industries. Large-sample vendor-adjacent report; NTT DATA is a technology services company, but the survey methodology and sample size lend credibility. https://www.nttdata.com/global/en/news/press-release/2025/december/120901

4. Bain Executive Survey: AI Moves from Pilots to Production. Bain & Company, Q3 2025. n=1,300+ commercial executives. Independent consulting firm survey; strong B2B sample. https://www.bain.com/insights/executive-survey-ai-moves-from-pilots-to-production/

5. State of the CIO 2025. Foundry/CIO.com, 2025. Survey of CIOs and IT leaders on priorities, budgets, and AI strategy. Independent media survey of technology leaders; annual benchmark with consistent methodology. https://www.cio.com/article/3974090/state-of-the-cio-2025-cios-set-the-ai-agenda.html

6. AI & Data Leadership Executive Benchmark Survey 2026. Randy Bean and Thomas H. Davenport, Harvard Business Review, January 2026. Survey of data and AI leaders at leading global companies. Academic-grade survey published in HBR; sample skews toward large enterprises. https://hbr.org/2026/01/hb-how-executives-are-thinking-about-ai-heading-into-2026

7. MIT CISR AI Maturity Model, 2025 update. MIT Center for Information Systems Research. Four-stage enterprise maturity model with financial performance correlation. Academic research; gold-standard methodology but sample skews large. Referenced in existing corpus analysis.

8. Brafton AI Policy Survey 2025. Brafton, 2025. Survey of AI policy adoption by company size. Marketing services firm survey; smaller sample but rare mid-market segmentation. Referenced in existing corpus analysis.

9. ADP Research Global Workforce Survey 2025. ADP Research Institute, July-August 2025. n=39,000 workers across 34 countries. Large-sample independent workforce survey; strong methodology. Referenced in existing corpus analysis.

10. Slack Workforce Index. Slack/Qualtrics, August 2024. n=17,372 desk workers across 15 countries. Vendor-funded (Salesforce/Slack) but administered by Qualtrics with rigorous methodology; large sample. Referenced in existing corpus analysis.

11. Social Proof Statistics aggregation. Trustmary, 2025. Compilation of 65+ studies on peer influence in purchase and decision behavior. Aggregation source; individual study quality varies but directional finding (92% trust peers over vendors) is consistent across sources. https://trustmary.com/social-proof/social-proof-statistics-that-may-surprise-you/

Brandon Sneider | brandon@brandonsneider.com March 2026