Guillermo Flor’s Post

The “Consulting as GTM” move is smart. It’s the right play for this moment. The real challenge will be delivering reliable and reproducible outcomes. OpenAI excels at solving problems in the moment, but consistency isn’t its strength. That inconsistency is exactly what traps clients in pilot purgatory. 👆 Comment written by Frank Fernandez

We've been trialing ChatGPT as a shadow consultancy to help us improve our internal processes and it's demonstrating how powerful AI tools can be when used for business development rather than tactical/one-shot questioning.

Is it the end of the consulting firm as we know it? Maybe not the industry—but definitely the model. The future belongs to high-EQ account executives who actually care about helping businesses succeed, not just selling decks. Clients are tired of $250K PowerPoint presentations that don’t move the needle. The new expectation is simple: “show me.” Firms like PwC, Deloitte, and Accenture will have to evolve fast. I have family who’ve spent years in the consulting world, and the writing’s on the wall—AI isn’t replacing them, but it’s rewriting the rules. ChatGPT and other AI tools will soon become core to how these firms deliver value. The winners won’t be the ones defending the old playbook; they’ll be the ones who use AI to scale expertise, insight, and execution. It’s not RIP—it’s reinvention in progress.

The AI Consulting Paradox: Why "Lifestyle Business" Just Became the Smartest GTM Strategy Everyone said consulting doesn't scale. Build product, raise capital, 10x ARR, exit. But AI just rewrote the playbook entirely. AI was supposed to commoditize everything. Instead, it made *customization* the only defensible moat. Every company has: → Unique data structures → Legacy workflows that don't map to SaaS templates → Pain points too specific for off-the-shelf solutions. Services let you charge premium rates to solve the *actual* problem. Still hard. The New Playbook (Especially for Solo/Small Founders): **Year 1:** Get paid to discover patterns You're doing R&D, but customers are funding it. Build cash flow + domain expertise simultaneously. No runway anxiety. **Year 2:** Those patterns *become* your product You've already validated it with paying customers. You're not guessing—you're codifying what already works. Why this works now: ✅ You're default alive from month one (no fundraising treadmill) ✅ You stay close to real problems (not hypothetical ones) ✅ The insight from services IS your product moat ✅ You can pivot fast without board pressure The old VC model: "Product scale or die." The new reality: In AI, the knowledge you gain from implementations is more valuable than the code. You're building something battle-tested across real deployments. My take: This wave will run strong for 2+ years. The founders who resist the urge to "productize too early" and instead get paid to learn will build the most defensible businesses. Now the question is how to get that consulting gig?

Yes, OpenAI is coming for the consulting giants, but the reason they're doing so is to create deep penetration. --> They are less consumed with ChatGPT licensing for the enterprise. Deeper integrations = better revenue as well as potentially value creation (on the surface) --> The moment is ripe to run after $B's in revenue by capturing the slower moving, secondary value additions of major consultancies. The definition of erosion of revenue. (Maybe they buy one on the cheap?) --> The missing piece here is still the Jobs that the human workforce is engaged with today. There is a need to start there because not everything is solutioned with an LLM. No matter how much you pay up front, and arguably enterprises that try to "over use" the same hammer, looking for nails will feel the impact sooner than later. Start on the right food with an #AIReadinessAssessment that focuses on the core business or department and the #JobsBeingDone. What's to lose? ADAPT@altir.co

What’s more important — the benefit or the dependency? Indeed, companies get tangible, turnkey #AI solutions — but the risks are clear: strong dependency on the #OpenAI ecosystem and a high entry cost.

<<Sam Altman is killing Mckinsey >> OpenAI just launched an enterprise consulting arm and it’s moving fast to capture a multi-billion dollar gap in the market. Here’s the playbook: 1. Consulting as GTM → Start with services, stay with product. 2. Force upfront commitment → OpenAI charges $10M to skip “pilot purgatory.” 3. Embed into workflows → Once AI runs inside your systems, it’s impossible to rip out. 4. Escape platform lock-in → Consulting revenue doesn’t flow through Microsoft. 5. Deploy experts fast → Engineers inside client orgs, like Palantir’s FDEs. I broke down the step by step strategy in my latest article. Write "CONSULTING" in the comments for the link https://lnkd.in/dBxfh2Wr

Sam Altman just declared war on McKinsey !! and he’s not using PowerPoints. OpenAI is quietly building a consulting powerhouse, blending AI expertise with enterprise execution. The goal? Replace traditional management consulting with AI-native strategy and implementation. The genius move: 1. Start with services, stay for the product - consulting as the new GTM. 2. $10M buy-in - no free pilots, only real partners. 3. Deep embed -AI becomes part of your company’s nervous system. 4. No Microsoft tax - direct value, direct control. 5. Deploy fast - engineers on-site, Palantir-style. This isn’t just consulting - it’s the new operating system for enterprise decision-making. #OpenAI #SamAltman #AIConsulting #McKinsey #EnterpriseAI #Strategy #Innovation

Brendan Falk spent 6 months building "AI-native Palantir" for Fortune 500s. The shutdown wasn't about tech. It was about a gap nobody talks about. Here's what Falk, ex-Amazon Global AI Lead, discovered in the trenches of enterprise AI: The reality check: • Board rooms demand AI strategies • Operational teams lack technical skills to execute • Companies default to big consultants • Result? Lots of POCs, few production solutions The numbers didn't add up: • $500K project = Same effort as $10M project • Small deals = Unsustainable economics • Big deals = Need deep specialization • Competing vendors = Hundreds of millions focused on single solutions The real kicker? Data chaos. Every enterprise had: • Messy databases • Conflicting information • Unique edge cases • Integration nightmares Falk's pivot tells the story. He transformed Zeus into Hercules.app - an AI website builder for SMBs. Simple, focused, scalable. The lesson? Enterprise AI isn't failing because of technology. It's failing because of the gap between boardroom ambition and operational reality. What's your experience with enterprise AI implementations? Have you encountered similar challenges? #EnterpriseAI #StartupLessons #DigitalTransformation

Brendan Falk spent 6 months building "AI-native Palantir" for Fortune 500s. The shutdown wasn't about tech. It was about a gap nobody talks about. Here's what Falk, ex-Amazon Global AI Lead, discovered in the trenches of enterprise AI: The reality check: •Board rooms demand AI strategies •Operational teams lack technical skills to execute •Companies default to big consultants •Result? Lots of POCs, few production solutions The numbers didn't add up: •$500K project = Same effort as $10M project •Small deals = Unsustainable economics •Big deals = Need deep specialization •Competing vendors = Hundreds of millions focused on single solutions The real kicker? Data chaos. Every enterprise had: •Messy databases •Conflicting information •Unique edge cases •Integration nightmares Falk's pivot tells the story. He transformed Zeus into Hercules.app - an AI website builder for SMBs. Simple, focused, scalable. The lesson? Enterprise AI isn't failing because of technology. It's failing because of the gap between boardroom ambition and operational reality. What's your experience with enterprise AI implementations? Have you encountered similar challenges? #EnterpriseAI #StartupLessons #DigitalTransformation 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gQb8FtKD