Conefer, Inc.AI consulting · veteran-led
Albuquerque, New MexicoClients across the country
Corey FrasureThe AI Mad Genius · founder
OpenAI Select Partner

Field notes

Notes on AI thatactually works.

What's real, what's hype, and what's worth your time. No breathless hot takes — just what I'm seeing in real businesses.

0226 posts

  1. · 8 min read How do I keep my AI win from fading out? Keep your AI win alive with a monthly 30-minute proof file. It's a running log of what changed, who actually uses the tool, what broke, and what got fixed. Most AI wins fall apart at renewal because nobody can prove the tool still works. A short record turns your renewal from a guess into a decision. Read it
  2. · 9 min read How do I stop random AI requests all week? Most AI ideas die in the handoff, not the build. Fix that with a simple intake and triage lane: one place to drop every AI request, a weekly review that ranks them, and each survivor gets an owner, a test, and a kill switch. A kill switch is a rule that says when you shut the thing off. This turns a week of drive-by asks into one ranked list you can actually work. Read it
  3. · 8 min read How do I prove AI helped without fancy numbers? You prove AI helped by writing down the work steps before and after, not by quoting a percentage. Capture one page: the handoffs, the wait times, and the rework on a single task. If the task now has fewer handoffs, shorter waits, and less redoing, that is proof. Most AI success stories skip this. That is why they read like ads. Read it
  4. · 7 min read What can we do with AI that rivals can't copy? Your AI edge is the knowledge you already have. Take the phrases your best salesperson uses, the checklist your lead tech follows, and the fixes your shop has learned the hard way. Write them down. Feed that into an AI helper. A competitor can buy the same model you use, but they can't buy the way you do the work. Read it
  5. · 12 min read The Escalation Layer: Making AI Customer Support Safe Enough for Production TL;DR “Human-in-the-loop” only works in production when escalation is treated as a designed system , not an emergency button. Build an Escalation Layer : clear … Read it
  6. · 11 min read From Seats to Results: Building Outcome-Based Pricing with AI in 2026 TL;DR AI makes outcome-based pricing viable by turning service delivery into a measurable, contractible performance layer : instrumentation → prediction → contr… Read it
  7. · 11 min read The AI Partnership Contract Playbook: Data Rights, Model Drift, and the Exit You’ll Wish You Had TL;DR Put “control of behavior” into the contract : define measurable model behavior, drift thresholds, and who can change prompts, policies, tools, and routing… Read it
  8. · 13 min read Job Architecture for the Copilot Era: Re-Leveling Roles Without Breaking Pay Equity TL;DR When AI copilots absorb 20–40% of task work, job architecture breaks first: leveling signals blur, pay equity risk rises, and career paths become incohere… Read it
  9. · 10 min read Why AI pilots stall (and what "scale" actually means) TL;DR Most AI pilots fail to scale because they optimize a demo, not a durable product: unclear value, weak data foundations, missing operating model, and unman… Read it
  10. · 10 min read Why market shifts feel different in 2026 TL;DR Markets are shifting faster because software, data, and distribution loops now move at “AI speed.” Strategic foresight with AI is less about predicting a … Read it
  11. · 10 min read Why AI adoption at scale fails (even when the model works) TL;DR Scaling AI is less about models and more about operating change: roles, workflows, governance, and measurement. Start with a repeatable “use-case factory”… Read it
  12. · 11 min read Why AI partnerships matter more in 2026 than they did in 2024 TL;DR Enterprise AI is shifting from “buy a model” to “build an ecosystem”: models, data, security, governance, and delivery partners operating as one system. P… Read it
  13. · 11 min read AI Cost, ROI & Investment Frameworks TL;DR AI ROI is easiest to prove when you separate prototype value from production value and measure both. Model costs are only part of the bill. The bigger dri… Read it
  14. · 12 min read AI Risk, Trust & Governance TL;DR Trust is an outcome of governance, not a feature you “add” to AI. AI risk is both functional and technical ; treat them differently and avoid one-size-fit… Read it
  15. · 10 min read Why pilots succeed—and enterprise rollouts stall TL;DR Most organizations can pilot AI; far fewer can scale it into repeatable, governed, enterprise value. Scaling requires a product mindset: clear outcomes, r… Read it
  16. · 12 min read AI in Real-World Applications TL;DR Real-world AI succeeds when it is treated as an end-to-end product: data, workflow, governance, monitoring, and change management—not just a model. The hi… Read it
  17. · 11 min read Why AI adoption stalls after the pilot TL;DR Most organizations can launch AI pilots; far fewer can operationalize AI across teams because adoption is a people-and-process problem, not a tooling prob… Read it
  18. · 10 min read Why strategic foresight is getting harder (and more necessary) TL;DR Market shifts rarely arrive as a single “event”—they show up first as weak signals across customers, competitors, capital, and operations. AI-driven strat… Read it
  19. · 12 min read AI Metrics That Matter & Executive Dashboards TL;DR Most organizations can’t prove AI value because they measure model performance but not business outcomes, risk signals, and adoption. Build an executive d… Read it
  20. · 11 min read AI Ethics, Brand Value & Customer Trust TL;DR Trust is now a primary driver of brand value , and AI can either compound it or erode it at scale. AI ethics is operational : it shows up in data practice… Read it
  21. · 11 min read Why AI transformation stories matter (and why many don't land) Date: 2026-02-23 TL;DR AI transformation succeeds when you can show repeatable proof points: faster cycle times, fewer defects, better decisions, and safer oper… Read it
  22. · 10 min read Beyond the Hype: Using AI as a True Competitive Differentiator in 2026 Beyond the Hype: Using AI as a True Competitive Differentiator in 2026 TL;DR: In 2026, AI is no longer a novelty—it's the baseline. Competitive advantage doesn'… Read it
  23. · 7 min read A Practical Guide to Evaluating AI Solutions: 7 Criteria for Business Decision-Makers Artificial intelligence (AI) promises efficiency and innovation, but not all solutions deliver. This guide provides a straightforward 7-criteria framework to help business practitioners and decision-makers evaluate AI tools objectively. The criteria cover problem alignment, data needs, integration, scalability, explainability, total cost, and vendor health. Use the framework to avoid costly missteps and select AI solutions that truly fit your organization's needs. Read it
  24. · 8 min read Operational Transformation & Efficiency Gains through AI AI-driven operational transformation delivers measurable efficiency gains through process automation, data-driven decision making, and intelligent resource allocation. Organizations typically see 20-40% improvements in operational efficiency when implementing AI strategically across key business functions. Success requires careful planning, employee training, and gradual implementation rather than wholesale system replacement. Read it
  25. · 4 min read AI-Enabled Business Models: 6 Strategies for 2025 Discover 6 AI-enabled business models transforming industries in 2025. Learn implementation strategies, revenue streams, and real examples for decision-makers. Read it
  26. · 4 min read How AI Marketing, Targeted Ads, and Operations Drive Small Business Growth If you’re running a small business today, you’ve probably come across terms like AI marketing automation, targeted advertising, and operational efficiency. They might seem like buzzwords, but together Read it

03Past reading about it

Reading is the cheap part.

If you want to know which of this applies to your process, that's a conversation, not an article.

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