Bodaty Blog

  "Sounds great, but I don't have a technical team." I hear this from small business owners every week. They WANT AI automation. They understand the value. They've seen the demos. But they don't have: - A technical team to configure it - Weeks to learn a new platform - Budget for expensive consultants So they do nothing. That's why every AICtrlNet Business tier includes expert hours. Not an add-on. Not an upsell. Built in. 2-8 hours per month depending on your tier. Use them for: - Initial configuration - Workflow optimization - Strategy sessions - Whatever you need We call it DWY — Doing With You. Not DIY self-serve where you figure it out alone. Not expensive SI firms charging $500/hour. Expert guidance, built into the subscription. The control spectrum meets you where you are. Expert guidance gets you there. **[See our pricing with built-in expert hours →]( https://hitlai.net/pricing )** **Hashtags**: #SMB #AI #SmallBusiness #Automation

  he AI automation market isn't segmented by company size. It's segmented by comfort level. **Cautious adopters (60% of market)** - Want AI value - Not ready for full automation - Will adopt if there's a safe path **Supervised adopters (25% of market)** - Want AI doing work - Need human approval - Comfortable with oversight **Full automation seekers (15% of market)** - Ready to hand over control - Want AI running operations - Aggressive adopters Every AI platform I've seen targets that 15%. "Full automation!" "AI does everything!" "No human bottlenecks!" And they wonder why adoption stalls. Meanwhile, 85% of the market is saying: "This sounds great but I'm not ready." The opportunity isn't convincing the 85% to become the 15%. It's building a platform that serves ALL of them. AI insights and suggestions for the cautious. Supervised automation for the middle ground. Full autonomy for the aggressive. One platform. Entire...

  Your AI is impressive. Your demo is killer. The pilot went great. And then the enterprise deal stalls. Not because the AI isn't good. Because somewhere between the demo and the purchase order, someone asked questions you couldn't answer. The data tells the story: - **Only 15%** of IT application leaders are even *considering* fully autonomous AI agents (Gartner, 2025) - **Only 13%** strongly agreed they had the right governance structures for AI - **Only 31%** of organizations have a formal AI policy — despite 83% believing employees use AI (ISACA) Here are the deal-killing questions and what buyers actually want to hear: **"What happens when the AI is wrong?"** Bad: "It rarely makes mistakes." Good: "High-risk actions require human approval. Full audit trails. Error rates monitored in real-time." **"Can you prove what the AI decided and why?"** Bad: "Sophisticated machine learning algorithms." Good: "Every action logged ...

  There's a moment in every AI project that feels like pure magic. You wire up the API, send a prompt, and the AI just... does the thing. But magic has a shelf life. Google's research team called machine learning "the high-interest credit card of technical debt." In mature ML systems, the actual ML code is roughly 5% of the codebase — the other 95% is everything that keeps the magic alive. **The three stages every AI project goes through:** **Stage 1 (Week 1-4): "Holy Shit, It Works!"** Everyone's impressed. Champions get promoted. Blog posts get written. But Gartner says 30%+ of these projects will be abandoned after proof of concept. **Stage 2 (Month 2-6): "Wait, What Did It Do?"** Customer complaints. Unexplainable decisions. Prompts that worked in testing fail on production data. And 91% of ML models suffer from model drift — your model isn't broken, the world changed and it didn't keep up. Just ask Zillow ($528M lesson). **Stage 3 ...

  "Just trust the AI." This phrase kills more AI projects than any technical failure. Because trust isn't given. It's earned. And here's how it gets earned: **Week 1-2**: AI makes suggestions. You notice 85% match what you'd decide. **Week 3-4**: You enable approvals. One-click to confirm AI actions. You start approving 90% without changes. **Month 2**: AI handles the routine stuff. You review exceptions only. **Month 3+**: You realize the AI hasn't made a mistake in weeks. You expand its autonomy. **Month 6**: AI anticipates needs. You're involved only for strategic decisions. This journey doesn't happen overnight. And platforms that demand full trust on day one? They get cancelled at month 3 when the first mistake happens. The platforms that win aren't the ones that say "trust us." They're the ones that say "let's build trust together." Start supervised. Grow into autonomy. That's how real AI adoption works. **...

A CIO asked me last week: "How do I give Marketing full AI automation while keeping Legal on human approval?" Most platforms: "You can't. Pick one mode for the whole org." Our platform: "Easy. Set autonomy policies per department." Here's what enterprise AI control actually looks like: **Marketing: Near-full autonomy** - AI runs campaigns autonomously - Human approval only for budget > $10K - Weekly review, not daily oversight **Legal: AI-assisted only** - AI drafts all documents - Attorneys approve everything - Full audit trail for compliance **Sales: Supervised automation** - AI handles quotes < $50K - Escalates above $50K - Learns which deals need human touch **Support: Full automation (Tier 1) / Supervised (Enterprise)** - Full automation for Tier 1 tickets - Supervised for enterprise customers - Exception-based human involvement One platform. One policy engine. Per-department autonomy. This is what enterprise AI governance actually requi...