The AI Hangover
The Morning After Everyone Bought AI
This Week in 30 Seconds
Companies rushed to adopt AI in 2025. Now they’re discovering the painful truth: having AI and using it well are two completely different things. Five major research reports dropped this week showing 88% of organizations use AI but only 39% see business value. That’s a 49-point gap representing billions in wasted investment. Meanwhile, 25% of your search traffic is fragmenting to AI platforms, your employees expect ChatGPT-level AI at work, and VCs are quietly saying some companies use “AI investment” as PR cover for layoffs. The year ahead is about execution, not adoption.
Five stories this week tell the same story from different angles: the gap between AI adoption and AI value just became impossible to ignore. The data is brutal, the implications are strategic, and the companies getting it right are doing something fundamentally different than those who rushed in. Here's what matters.
Story 1: Companies Spent 2025 Adopting AI. They’re Not Getting Value.
The News: Five major research reports dropped this week showing organizations are failing to realize AI’s promised value. The numbers are brutal: 83% of nearly 500 organizations scored in the lowest two categories for AI maturity (Phenom study). Only 7% of organizations give employees any guidance on what to do with the time AI saves them (Gartner). And 88% of HR leaders say their organizations haven’t realized significant business value from AI tools (Gartner survey of 114 HR leaders).
The Noise: Everyone’s celebrating adoption rates and employee enthusiasm. Headlines scream about how many companies have deployed AI. Look at all this innovation.
The Signal: Companies are treating productivity gains as the finish line instead of the starting point. MIT’s Project NANDA (July 2025) analyzed over 300 public AI implementations and surveyed 153 senior leaders. Despite $30-40 billion in global enterprise investment, 95% of GenAI pilots fail to deliver measurable returns. Ninety-five percent.
Side note: Look, the 95% stat has its critics. Fair questions about methodology and what counts as "failure." But whether it's 95% or 80% doesn't really matter — the core issue is the same. Most organizations struggle to move AI pilots into production. That's the problem worth solving, regardless of the exact percentage.
The McKinsey data is equally sobering: 88% of organizations now use AI in at least one business function, but only 39% report measurable EBIT impact. That’s a 49-point gap between adoption and value. Project abandonment rates hit 42% in 2025, up from 17% in 2024—a 147% year-over-year increase in companies giving up on AI initiatives.
Why? Because you saved your team 1.5 hours a day with AI (the average reported by Gartner), but only 7% of companies tell employees what to do with that time. You’ve created expensive confusion, not business value. The gap runs between adoption and strategy, not between AI and humans.
Organizations with documented AI policies report 82.5% confidence using AI responsibly versus 58.5% at companies without policies. Yet only 37% have formal policies in place. You’re spending on tools while ignoring governance. That’s backwards.
Your Move: Audit before you expand. If you’ve adopted AI tools, pause new purchases and answer three questions:
Do we have written guidelines for AI use?
Can we measure actual business outcomes, not just “usage rates”?
Do employees know what to do with the time AI saves?
If you answered no to any of these, your next investment should be governance and strategy, not more subscriptions. The data is clear: Gartner predicted 60% of AI projects would be abandoned due to lack of AI-ready data. Don’t be part of that statistic.
Story 2: Most Small Businesses Are Failing at AI. Here’s Why.
The News: An analysis of 1,000+ AI training programs across UK and Ireland SMBs reveals the core failure pattern: businesses start with a cool AI tool and then try to find problems it can solve. Successful businesses do the opposite. They start with actual problems and determine if AI is the best solution. According to ProfileTree founder Ciaran Connolly, that sequence matters enormously.
The Noise: AI tools are getting better, cheaper, more accessible. Just adopt and iterate. Everyone’s doing it. The tools will save you time and money automatically.
The Signal: The data shows a stark divide: 83% of growing SMBs use AI versus stagnant and declining businesses. The successful 83% follow completely different patterns than the failures.
Four patterns keep showing up in failed implementations.
First, the tool-first mistake: buying exciting AI without connecting it to actual workflows. You see a demo, get excited, purchase access, and then wonder why adoption is low.
Second, the training gap: handing people tools without teaching them to use them well. You think the tool is “intuitive” but your team produces inconsistent outputs because they don’t understand effective prompting or quality review.
Third, unrealistic expectations: thinking AI replaces human judgment instead of assisting skilled operators. AI doesn’t make bad writers good. It makes good writers faster.
Fourth, integration challenges: standalone tools that don’t connect to existing systems create more friction than value.
Successful adopters achieve positive ROI within six weeks (Swfte AI research). They report 27% productivity increases and 23% cost reductions. And 91% report revenue growth (Salesforce research). But they get there by starting with problem identification, not tool selection.
The successful pattern looks like this: Pick one process that consumes significant staff time and produces variable quality. Customer email responses, meeting summaries, initial content drafts. Something contained. Define success criteria in advance (not “we’ll figure it out as we go”). Run a four-week trial. Review honestly. Did it hit the success criteria? If yes, expand systematically. If no, analyze why before trying again.
The entry cost is lower than you think. Swfte AI data shows $100-300/month starts viable AI implementation. The fastest ROI comes from customer service automation (40% of inquiries can be automated), not moonshot projects.
Your Move: Problem-first, tool-second. This week, document one process: high time consumption plus variable quality output. Write down what success looks like (specific, measurable). Don’t buy anything yet. Just establish the baseline. Next week, if you still think AI is the solution, then evaluate tools. That order matters enormously.
Story 3: Your Employees Are Using AI at Home. That’s About to Be Your Problem.
The News: 2026 is the turning point where consumer AI adoption starts forcing enterprise change. Matt Britton (Suzy founder) compares it to the early iPhone era: people used smartphones freely at home while companies resisted them at work. Eventually, consumer expectations forced organizational change. The same pattern is happening with AI. Your employees are already using AI for personal health, family support, finances, and everyday decisions, building real confidence through hands-on use, not theory.
The Noise: Enterprise AI adoption is the story. Companies deploying tools. IT departments evaluating solutions. Governance frameworks being developed.
The Signal: The real adoption is happening in living rooms, not boardrooms. And the numbers are staggering. ChatGPT alone has 800-900 million weekly active users across platforms (a16z, December 2025). That’s 66 million daily active users. Total AI tool usage hit 378 million people worldwide in 2025, up 64 million from 2024. In the U.S., 61% of adults use AI, with Gen Z at 76% and even Baby Boomers at 45%.
85% cite personal use as their primary AI application. They’re using it for health questions, financial decisions, travel planning, learning new skills. They trust AI with personal decisions because there are no approval layers slowing them down. They experiment freely. They learn what works. They develop real fluency.
And then they show up at work expecting the same speed, personalization, and capability they get at home.
This creates bottom-up pressure on organizations that are still debating governance frameworks. Your employees have already formed expectations about what AI should do and how it should work. If your workplace AI is clunky, restricted, or slower than what they use at home, you’ve created friction. The talent advantage goes to companies that reduce that friction, not add to it.
The BYOD moment for AI is here. Remember when employees started bringing their personal smartphones to work and IT departments scrambled to accommodate them? Same pattern. Menlo Ventures found 27% of all AI application spend now comes through product-led growth motions (individual users, not executive procurement). That’s nearly 4x the rate in traditional software.
Gen Alpha (entering the workforce soon) expects AI to understand context, respond personally, and evolve with them. That’s your new hiring baseline. Companies still running approval processes for basic AI use will lose talent to competitors who embrace it.
Your Move: Ask, don’t assume. This week, survey your team: What AI tools are you using in your personal life? What do you wish you could do at work that those tools enable at home? Make it anonymous if needed. You’ll learn more from ten honest answers than from any vendor pitch. Then ask yourself: are we adding friction or reducing it? The gap between consumer AI and enterprise AI is your talent risk.
Story 4: SEO Just Got 10x Harder. Here’s What Changed.
The News: Up to 25% of traditional organic search traffic could shift to AI chatbots and answer engines by 2026 (ProfileTree research). Google’s AI Overviews feature is already reducing click-through rates by 25-60% for many queries. Even businesses ranking in the top three positions are getting significantly fewer visitors than twelve months ago. The search landscape has fragmented: you now need to appear in AI Overviews, be cited by ChatGPT, show up in Perplexity results, and be referenced by Claude and Gemini.
The Noise: Keep doing traditional SEO. Optimize for keywords. Build backlinks. Rank on page one. Google’s still dominant.
The Signal: SEO just became ten times harder, and most businesses haven’t noticed yet. Over 30% of desktop searches on Google now surface an AI Overview, up from just 23% in April 2025 (Comscore 2025 AI Intelligence Report). That’s 30%+ growth in eight months. Different trackers report anywhere from 10-55% of queries showing AI-generated results. The variance doesn’t matter. The trajectory does.
Google’s AI Overviews can reduce organic clicks by 18-64% for affected queries (Exploding Topics analysis). That means even if you rank #1 for a valuable keyword, if Google answers the question in an AI Overview, your traffic drops by half or more.
But the change runs deeper than Google. When someone asks ChatGPT or Perplexity for a recommendation, the AI provides a direct answer and cites two or three sources. If you’re not one of those sources, you’re invisible to that user entirely. The game shifted from “rank in search results” to “be quotable and citeable by AI systems.”
This has created a new discipline called Generative Engine Optimization (GEO). The core principle: AI systems favor structured, authoritative, quotable content. Your vague marketing speak doesn’t get cited. Your “About Us” page written in corporate jargon doesn’t appear in AI answers.
Professional response is accelerating: 82% of enterprise SEOs plan to invest more in AI (Digitaloft 2025). And 86% of SEO professionals now use AI in their workflows (up 1,900% in search interest over five years). Companies with 200+ employees report 83% see improvements in SEO performance since using AI, saving an average 12.5 hours per week.
Your Move: Audit your AI search presence this week. Search for your business name, products, and services in ChatGPT, Perplexity, Google’s AI Overviews, and Claude. What comes up? Is it accurate? Is it current? Many businesses are shocked to find AI systems have outdated or wrong information. Then review your About Us and Why Choose Us pages. Are they written in a way AI can easily understand and quote? Or are they vague marketing speak? The businesses that win in 2026 will be the ones AI systems trust enough to cite.
Try This Prompt:
For ChatGPT/Claude:
You are an AI-powered Business Audit Assistant. Your primary task is to analyze how your client's business is represented across the internet and provide actionable recommendations for improvement.
Begin by gathering data. Perform comprehensive web searches using the following search queries: "[Client Business Name]", "[Client Main Product/Service]", and "[Client Specialty/Niche]". Focus on the top 20 search results for each query, prioritizing websites that are likely to be used as sources of information by AI models (e.g., company directories, review sites, news articles, blogs). Record the URLs of these search results in a structured list, noting the specific search query that led to each result.
Next, analyze the content of each identified web page. Extract all information pertaining to the client's business, including name, address, contact information, products/services offered, mission statement, values, history, and any other relevant details. Summarize this information concisely, indicating the source URL for each piece of information. Pay close attention to how the business is described and positioned in each source. Note any discrepancies or inconsistencies between different sources.
Identify and flag any inaccuracies, outdated information, or gaps in the information found online. For each identified issue, provide a detailed explanation of the problem and its potential impact on how AI models perceive and represent the client's business. Prioritize issues that could lead to misrepresentation, reputational damage, or lost business opportunities. Specifically look for inconsistencies in: Business Name, Address, Contact Information, Product/Service Descriptions, Pricing, Key Personnel, and Awards/Recognition.
Finally, based on your analysis, provide three specific, actionable recommendations for improving the client's "About Us" and "Why Choose Us" pages to make them more "AI-quotable". These recommendations should focus on enhancing the clarity, accuracy, and completeness of the information presented on these pages. Suggest specific wording changes or additions that would make it easier for AI models to extract and summarize key information about the business. Each recommendation should include a clear explanation of why the suggested change would be beneficial. For example, focus on improving sections that describe: Core Values, Unique Selling Propositions, Target Audience, and Competitive Advantages.For Perplexity:
Step 1:
Search for these three queries and capture the top 20 results for each:
"[Client Business Name]"
"[Client Main Product/Service]"
"[Client Specialty/Niche]"
Focus on the kinds of sources AI models actually pull from — business directories, review sites, news articles, industry blogs, etc. Give me the URLs organized by which search query surfaced them.Step 2:
Pull everything these pages say about my business:
Basic info (name, address, contact)
What we do (products, services, positioning)
Who we are (mission, values, history, team)
Anything else relevant
For each detail, cite the source URL. I want to see how we're being described across different platforms — and where things don't line up.Step 3:
Call out anything that's wrong, outdated, or missing. For each issue, explain:
What the problem is
Why it matters (especially for how AI represents us)
What risk it creates (misrepresentation, credibility hit, lost opportunities)
Pay special attention to inconsistencies in:
Business name variations
Address/location info
Contact details
How we describe our services
Pricing (if mentioned)
Key people
Awards or recognitionStep 4:
Based on what you found, give me three specific ways to improve our "About Us" and "Why Choose Us" pages so they're more "AI-quotable" — meaning easier for AI models to accurately extract and cite.
For each recommendation:
Suggest the actual wording or structural change
Explain why it'll help AI (and humans) understand us better
Focus on making these sections clearer:
Core values
What makes us different
Who we serve
Why we're the right choice
What I'm looking for: A clear picture of how AI sees my business right now, what's broken, and exactly what to fix to control that narrative better.Story 5: VCs Are Bullish on AI Adoption. Read the Fine Print.
The News: TechCrunch surveyed 24 enterprise-focused VCs. They overwhelmingly predict 2026 will be the year enterprises meaningfully adopt AI and increase budgets. But the cynical part buried in the responses: “Many enterprises, despite how ready or not they are to successfully use AI solutions, will say that they are increasing their investments in AI to explain why they are cutting back spending in other areas or trimming workforces” (Antonia Dean, Black Operator Ventures). Translation: some companies are using “AI investment” as PR cover for layoffs and cost cuts, not actual transformation.
The Noise: VCs are optimistic about AI adoption. Enterprise budgets are growing. The future is bright. Innovation is accelerating.
The Signal: Even the optimists are hedging. VCs who make money betting on AI are publicly saying: yes, adoption will increase, but watch out for companies using AI as a scapegoat for past mistakes. The market is consolidating fast. Anthropic now commands 40% of enterprise LLM spend, up from 24% last year and just 12% in 2023 (Menlo Ventures survey of 495 U.S. enterprise AI decision-makers). OpenAI fell to 27% from 50% in 2023. The top three players (Anthropic, OpenAI, Google) control 88% of enterprise spend.
The most important insight from the VC survey comes from Harsha Kapre (Snowflake Ventures): “For AI startups, the strongest moat comes from how effectively they transform an enterprise’s existing data into better decisions, workflows, and customer experiences.” What you do with your data matters more than which model you use.
The VCs are clear: budgets will increase for AI that delivers results and “decline sharply for everything else.” Concentration is coming. Winners will be companies that prove value, not just claim potential. Remember: an MIT survey from August 2025 found 95% of enterprises weren’t getting meaningful ROI on AI investments. That was four months ago. The companies that fixed that problem are pulling ahead. The ones still chasing shiny tools are falling behind.
Your Move: Be skeptical when you hear “we’re investing in AI” used to justify headcount cuts or budget reductions. Demand specifics: What problem is AI solving? How are we measuring success? What happens if it doesn’t work? And if you’re considering cuts elsewhere to fund AI, flip the question: are we using AI to justify decisions we already wanted to make? The VCs know companies do this. Make sure you’re not one of them.
The Pattern
Every story this week points to the same shift: the experimental phase is over. Companies that threw AI at problems without strategy are now facing the consequences. The data is unforgiving: 95% failure rates, 42% abandonment, 49-point gaps between adoption and value. Meanwhile, consumer expectations are accelerating (378 million users, 61% of U.S. adults), search landscapes are fragmenting (25% traffic shift, 30% AI Overviews), and even the VCs are hedging their bets (budgets decline for everything that doesn’t deliver).
The winners in 2026 won’t be the ones with the most AI tools. They’ll be the ones who figured out governance before scaling, who matched consumer expectations instead of fighting them, who made their content AI-quotable, and who demanded proof instead of promises. The gap between AI adopters and AI winners is widening every week.
The Contrarian Corner
Everyone’s getting AI adoption wrong, including the people writing about it. The narrative: companies need to adopt AI faster. Move faster, experiment more, don’t get left behind. But companies that adopted slowly and strategically are getting better results than those that rushed.
Look at the numbers: 83% of growing SMBs use AI, but they’re growing because they followed problem-first strategies, not tool-first excitement. They defined success criteria before purchasing. They piloted narrowly before scaling. They invested in governance proportionate to their ambitions. Meanwhile, the companies that rushed to “check the AI box” are now part of the 42% abandonment rate or the 95% with no measurable ROI.
The ROI crisis, the maturity gap, the failure rates all point to one thing: think before you adopt. Maybe the companies that “fell behind” in 2025 because they were asking hard questions about governance, integration, and measurement are actually ahead of the ones that rushed to deploy ChatGPT to every employee without guidelines.
The laggards might be the leaders in twelve months. Speed without strategy is just expensive chaos.
Your One Move This Week
Run the three-question governance test. Before you add another AI tool, expand usage, or increase spending, answer these honestly:
Do we have written guidelines for how AI should and shouldn’t be used in our business? Not informal “we talked about it” guidelines. Documented policies that employees can reference.
Can we measure actual business outcomes from AI, not just “people are using it”? Can you point to specific revenue increases, cost reductions, time savings, or quality improvements? With numbers?
Do our employees know what to do with the time or capacity AI creates? If AI saves each employee 1.5 hours per day (the average), what are they supposed to do with those 7.5 hours per week?
If you answered no to any of these, your next AI investment should be in governance and strategy, not more tools. The research shows organizations with formal policies have 82.5% confidence versus 58.5% without. That confidence translates to better outcomes, faster scaling, and actual ROI.
Building the foundation lets you speed up sustainably. The companies winning in 2026 did this work in 2025 while everyone else was chasing demos.
Good Luck - Dan