State of AI

Brandon Sneider | March 2026

Executive Summary

Someone in your C-suite just became the AI sponsor — often a CIO or COO who did not ask for the assignment. The first 90 days determine whether the organization’s AI program succeeds or joins the 95% that stall (MIT NANDA, 2025, n=800+ companies)
Projects with sustained CEO-level sponsorship achieve 68% success rates versus 11% for those that lose executive engagement within 6 months (Pertama Partners, aggregated from multiple studies, 2026). The sponsor’s job is not to manage AI — it is to keep organizational attention and resources pointed at it
73% of failed AI projects lack clear executive alignment on success metrics before the first dollar is spent (Fair Observer, citing MIT and HBR analysis, 2025). The most common mistake is launching a pilot before defining what success looks like
Mid-market companies have a structural advantage: they can move from pilot to production in 90 days versus 9 months at enterprise scale (V2 Solutions, 2025). But only if the sponsor builds the coalition and clears the path in the first 30 days
42% of companies scrapped most AI initiatives in 2025, up from 17% in 2024 (Pertama Partners, 2026). The difference between programs that survive and programs that get cut is almost always the quality of executive sponsorship, not the quality of the technology

Days 1-30: Assessment and Coalition

The instinct is to launch a pilot immediately. Resist it. The data is unambiguous: 73% of AI failures trace back to misaligned expectations and missing governance, not technology problems (Fair Observer, 2025). The first 30 days are about building the foundation that makes everything after it work.

Week 1-2: The Honest Assessment

Before making any commitments, answer five questions:

What AI already exists in the organization? Shadow AI is present in 77% of companies (Stack Overflow, 2025). Employees are already using ChatGPT, Copilot, and other tools — the question is whether anyone knows which tools, on which data, under what governance. A shadow AI audit takes 5-10 days and typically reveals 3-5x the expected AI footprint.
What is the organization’s data readiness? Only 7% of enterprises report data completely ready for AI (Cloudera/Harvard Business Review, n=230+, October 2025). If the data is not inventoried, classified, and governed, most AI tools will produce unreliable results. This is not a technology question — it is a “do you know what data you have and who can access it” question.
What has already been tried? Most mid-market companies have at least one failed or stalled AI experiment. Understanding what happened — and who got burned — is critical context. The employees who participated in the last failed pilot are your biggest risk of resistance and your biggest opportunity for credibility if you acknowledge what went wrong.
Who are the 3-5 people who will make or break this? Not the CEO (whose support you need but cannot monopolize) — the department heads, IT leads, and informal influencers who will either champion or quietly sabotage adoption. Identify them. Meet with each one individually in the first two weeks.
What does the board expect? If the board has been promised “AI transformation,” and management is delivering a $50K pilot, there is a gap that will surface at the worst possible time. Align expectations upward before committing downward.

Week 2-4: The Coalition

The AI sponsor cannot succeed alone. The minimum viable coalition:

Role	Why You Need Them	First Ask
CFO or finance lead	Controls budget, needs to approve full-cost model (not just license fees)	Agree on a 3-year cost framework, not a pilot budget
CISO or security lead	AI governance and data protection are non-negotiable prerequisites	Co-author a 2-page AI acceptable use policy
General Counsel	IP, liability, regulatory compliance — especially if client data touches AI	Review vendor contracts for AI-specific terms
1-2 department heads (highest-impact use cases)	Business ownership of use cases, not IT-driven deployment	Identify the 2-3 workflows where AI would save the most time
1-2 informal champions (respected practitioners)	Peer credibility, ground truth on what actually works	Pilot participation, honest feedback loop

The two-page AI governance charter — co-signed by the CEO, CFO, and the sponsor — is the single most important deliverable of Days 1-30. It does not need to be perfect. It needs to exist. It establishes that AI is governed, not just encouraged.

Days 31-60: The Disciplined Pilot

Choosing the Right First Win

The pilot selection determines the program’s survival. The criteria that matter:

Choose a Tier 1 use case. Autocomplete, test generation, documentation, boilerplate — tasks where every controlled study shows 25-35% speed gains. Do not start with a complex, novel application. The first pilot’s job is to produce an undeniable win that earns the right to do more.

Choose a visible team. A pilot buried in a back-office function produces data but not organizational momentum. Choose a team whose results will be visible to the coalition members who need to stay engaged.

Choose a measurable outcome. “We tried AI and people liked it” is not a result. Define the metric before the pilot starts: hours saved per week, tickets resolved per day, documents produced per month. The 68% of successful AI programs that maintain executive engagement do so because they can point to specific numbers (Pertama Partners, 2026).

The 30-Day Pilot Framework

Week	Activity	Deliverable
Week 5	Tool configuration, license deployment, security review	Working tool in hands of pilot team
Week 6	Training (4-8 hours, role-specific, not generic)	Team using tool on real work
Week 7-8	Supported daily use with weekly check-ins	Usage data, friction log, early results
End of Week 8	Pilot retrospective with quantified outcomes	Decision brief: expand, adjust, or kill

The pilot should cost $10K-$30K (licenses + training + management time). If the pilot costs more than this, it is too ambitious for a first attempt.

Days 61-90: Expand or Kill

The Honest Evaluation

At day 60, three outcomes are possible:

1. Clear win — expand. The pilot team shows measurable improvement on the target metric, adoption is above 60%, and the friction log is manageable. Action: plan the second wave (2-3 more teams), begin governance formalization, present results to the board.

2. Mixed results — adjust. Some value is visible but adoption is uneven or the use case was not quite right. Action: interview the pilot team individually (not in a group — people are more honest alone), identify the specific blockers, and run a second 30-day cycle with adjustments. Do not expand yet.

3. Failure — learn, do not hide it. The pilot produced no measurable improvement or created more problems than it solved. Action: document what went wrong with specificity (not “it didn’t work” but “the tool produced unreliable outputs because our data was not structured for the use case”). Present this honestly to the coalition. Credibility comes from the willingness to call a failure a failure.

61% of failed AI initiatives were treated as IT projects rather than business transformation (Fair Observer, 2025). If the pilot failed because business context was missing — the tool worked but the workflow around it was not redesigned — that is a process problem, not a technology problem. Adjust accordingly.

The Day 90 Board Update

By day 90, the board (or leadership team) should receive:

AI footprint status: what exists, what is governed, what is shadow
Pilot results: specific metrics, not sentiment
Cost actuals versus plan: including the hidden costs (training, review overhead, governance)
Next 90-day plan: specific next steps with budget and timeline
One honest risk: the single biggest thing that could derail the program

Key Data Points

Metric	Value	Source
AI pilots that fail to deliver P&L impact	95%	MIT NANDA, n=800+, 2025
Companies that scrapped most AI initiatives in 2025	42% (up from 17% in 2024)	Pertama Partners, 2026
Success rate with sustained executive sponsorship	68%	Pertama Partners, 2026
Success rate when sponsorship lapses within 6 months	11%	Pertama Partners, 2026
Failed projects lacking aligned success metrics	73%	Fair Observer / MIT / HBR, 2025
Failed projects that lost C-suite sponsorship within 6 months	56%	Fair Observer, 2025
Failed projects that treated AI as IT (not business transformation)	61%	Fair Observer, 2025
Enterprises with data “completely ready” for AI	7%	Cloudera/HBR, n=230+, 2025
Companies with CAIO appointed	26% (up from 11% in 2023)	IBM IBV, 2025

What This Means for Your Organization

The 90-day window is not arbitrary. It is the period during which organizational attention is highest, skeptics are reserving judgment, and the budget has not yet been questioned. After 90 days without visible results, AI programs lose momentum in ways that are very difficult to recover — the 56% of programs that lose executive sponsorship within six months almost all show the first signs of drift between days 60 and 90.

The playbook is specific because the failure modes are specific. Not “get executive buy-in” but “co-sign a two-page governance charter with the CEO and CFO in week 3.” Not “start a pilot” but “deploy a Tier 1 use case with a visible team and a pre-defined metric by day 45.” Not “show results” but “present quantified pilot outcomes to the board by day 90.”

The executive who just got this assignment did not choose it, but can choose how to approach it. The difference between the 68% success rate and the 11% success rate is not talent or technology — it is discipline in the first 90 days. If you are in that seat right now and want to pressure-test your plan against the patterns that predict success, that is exactly the kind of conversation that pays for itself — brandon@brandonsneider.com

Sources

CIO.com — “AI That Ships: A CIO’s 90-Day Operating Model” (2026). Credibility: HIGH — major industry publication, practitioner-focused
Cloudera/Harvard Business Review Analytic Services — Data readiness survey (n=230+, October 2025). Credibility: HIGH — HBR research arm, disclosed methodology
Fair Observer — “Why 95% of Enterprise AI Projects Fail: The Pattern We’re Not Breaking” (2025). Credibility: MEDIUM — analysis aggregating MIT, HBR, and consulting data
Fortune — “MIT Report: 95% of Generative AI Pilots at Companies Are Failing” (August 2025). Credibility: HIGH — citing primary MIT research
IBM Institute for Business Value — CAIO appointment data (2025). Credibility: HIGH — large-scale survey
MIT NANDA — “The GenAI Divide: State of AI in Business 2025” (n=800+, 2025). Credibility: HIGH — academic institution, large sample, primary research
Pertama Partners — “AI Project Failure Statistics 2026: The Complete Picture” (2026). Credibility: MEDIUM — consulting firm, aggregated from multiple studies
StackAI — “The CIO’s Playbook for Enterprise AI Strategy in 2026” (2026). Credibility: MEDIUM — vendor, but well-structured framework
Umbrex — “Chief AI Officer Playbook for Enterprise AI at Scale” (2025). Credibility: MEDIUM — consulting network, practitioner framework
V2 Solutions — “Why AI Pilots Fail in Mid-Market Firms & How to Succeed” (2025). Credibility: MEDIUM — vendor, mid-market specific data

Brandon Sneider | brandon@brandonsneider.com March 2026

You Just Got Handed AI: The Executive Sponsor’s First 90 Days