FCamara

Most companies are asking the wrong questions about AI "Is it accurate enough?" "When will it match my best people?" "Are my competitors moving faster?" These questions focus on the technology's intelligence trajectory. They miss the only question that actually matters strategically: how do we apply it differently than everyone else in our value chain? Harvard Business School published a framework that cuts through the noise, and it's the clearest task-level decision model I've seen since we started deploying AI at scale with clients. The core insight: suitability for gen AI depends on two dimensions, not one. → The cost of errors if the model is wrong → The type of knowledge the task actually requires (explicit data vs. tacit human judgment) This gives you four operational zones. "No regrets" tasks: low error cost, explicit data, are where you deploy today and don't overthink it. Screening, summarizing, routing, drafting responses. The benchmark isn't perfection; it's whether it's faster and cheaper than the current way. "Quality control" tasks: high error cost, explicit data, are where AI drafts and humans verify. Legal contracts, software code, due diligence records. Speed from the machine, accountability from the person. "Creative catalyst" tasks, low error cost, tacit knowledge, are where AI generates options and humans select. Taglines, product concepts, sales scripts. The right question here isn't "is it as creative as a human?" It's "does it expand how many people can participate in creative work?" "Human-first" tasks, high error cost, tacit knowledge, are where AI assists but never decides. Strategy, enterprise integrations, disciplinary decisions, executive hiring. The model can synthesize market data or model reputational risk. The human owns the call. What I find most compelling in their argument isn't the quadrant model itself. It's the warning underneath it. Everyone has access to the same tools. If you and your competitors apply gen AI to similar tasks in similar ways, the gains don't accrue to you . That's exactly what happened with internet 1.0 and with ERP in manufacturing. Competitive advantage won't come from adoption. It will come from applying it distinctively: which tasks you delegate, how you pair human expertise alongside it, and what new possibilities you unlock that were never economically viable before. The organizations that recognize this distinction and move deliberately to build that differentiation, are the ones that will still be capturing value in five years. The rest will have made everyone else more efficient. Worth reading in full. Link in comments. #ai #adoption #hbr

IBM knew this in 1979 We seem to have forgotten it in 2025. Their training manual had one core insight: accountability requires a human. So decisions must stay with humans. But here's the problem nobody talks about. We assumed that keeping humans "in the loop" was enough to protect decision quality. Behavioral science says otherwise. Human decisions aren't rational by default. They never were. We know this from decades of research: → We anchor on the first number we see → We favor information that confirms what we already believe → We make different choices depending on how options are framed → We delegate harder when we're tired, overwhelmed, or uncertain Now add AI to that equation. An AI system trained on historical decisions inherits the biases baked into those decisions. It surfaces recommendations through interfaces that prime certain choices. It creates an illusion of objectivity that actually reduces critical thinking, leading to automation bias. The human is still technically "deciding." But the decision architecture is doing most of the work. This is the gap I see constantly with organizations investing in AI: they focus on the model. They ignore the environment around the decision. Bad decision architecture doesn't just fail to fix human irrationality. It amplifies it, at scale, at speed, with a veneer of data-driven legitimacy. IBM was right about accountability. But accountability without decision design is just liability without protection. The question isn't "who decides?" It's "what environment are they deciding in?" That's the work we need to do with data & AI. #decisionintelligence #behaviourscience #AI

Most AI governance conversations start in the wrong place. They start with regulation. With frameworks. With compliance checklists. The problem? None of that lands until you actually understand the risk landscape you're navigating. MIT just made that much easier. The MIT AI Risk Navigator ( airi-navigator.com ) maps the entire AI risk universe into 7 domains, from discrimination and privacy to system failures and socioeconomic disruption. Cross-referenced with real incidents, governance documents, and mitigation strategies in one place. The numbers tell the story: → 1,511 documented risks → 1,366 real-world incidents → 5,238 governance documents But what struck me isn't the scale. It's the architecture. This is exactly the kind of tool that closes the gap I see constantly with clients: executives who are making AI investment decisions without a shared language for what can go wrong. When you can't name a risk, you can't manage it. When you can't connect an incident to a governance response, you're reactive by default. Decision Intelligence starts here: with the ability to see the full landscape before you act. If you work in AI strategy, governance, or risk — this is a resource worth bookmarking. 🔗 airi-navigator.com #AI #governance #Risk #Decisionintelligence

Your vendor's agent approved a $2.3M contract with a customer that went bankrupt three weeks later. The agent had access to a tool. It never checked it. We built a financial services platform where an approval agent could invoke a credit check, a fraud screening service, and a contract repository. On paper, clean separation of concerns. In practice, the agent's training data included examples where the credit tool was optional for certain deal sizes. The rule changed six months into production. No one told the agent. The tool contract — what it accepts, what it guarantees, when it must be called — drifted away from the agent's mental model. By the time we caught it, the agent had approved deals without running mandatory checks on seventeen separate occasions. The cost wasn't just the loss. It was the audit. It was the compliance review. It was the two weeks of engineering time reverse-engineering decision logs to prove intent. This is the hard truth about agentic systems at scale: you can't automate a decision without automating the verification that the decision followed policy. Your infrastructure must enforce tool contract boundaries at invocation time — not as a suggestion, but as a gate. The agent should not be able to call a tool outside its constraints any more than a process should be able to write to read-only memory. Build tool contracts as executable constraints. Version them. Log every deviation. Make breaking a contract expensive enough that it surfaces immediately, not in an audit three quarters later. This adds latency. It adds operational overhead. It means your agent sometimes fails loudly instead of gracefully. But an agent that fails loudly and remains auditable is infinitely cheaper than one that fails silently and leaves you explaining to regulators why a machine made a decision you can't reconstruct. Who owns the contract between your agents and their tools — engineering, or the team that owns policy governance? #Agentic #Infrastructure #Governance

Your AI Deployment Strategy Is Outdated Most organizations deploy AI wrong. Not because the technology is bad. But because they're treating it as a technology problem when it's actually a system problem. Here's the pattern: Build accurate model ✓ Hope people adopt it ✗ Get disappointed ✗ Blame "resistance to change" ✗ Invest more in training ✗ Still fail ✗ The problem isn't step 1. It's that steps 2-6 ignore how organizations actually work. Five Things I've Learned from deploying AI in operations 1. Prediction ≠ Decision Accurate models don't automatically create better outcomes. 78% of firms use AI. 80%+ see zero EBIT impact. That gap is the problem. 2. You're Optimizing the Wrong Level Organizations treat adoption as one problem. It's three: individual biases, organizational misalignment, and interface design. You need all three. 3. Change Without Measurement = Wasted Money You can't improve what you don't measure. Most orgs measure adoption, not decision quality. Different metrics. Completely different outcomes. 4. Your Org Doesn't Have a Framework Most organizations have no system to improve decisions over time. They have AI. They have change programs. But they're separate. Disconnected. Siloed. 5. You're Not Alone in the Gap 78% of AI deployments underperform. Not because AI is bad. Because the integration is missing. The fix isn't better AI. It's a Decision Intelligence system that integrates behavioral science, organizational design, AI capability, and change management as one thing. Not separate. Not siloed. One system. do you have a structured Decision Intelligence system?

Your agent approved a $180K vendor contract yesterday. No human signed off. No legal review gate. The procurement agent had a tool called "execute_contract" and a policy document that said "approve vendors rated above 75." The vendor scored 76. The tool executed. And now your finance team is untangling a deal with terms that violate your MSA template and discount tiers that undercut regional pricing agreements. This happened in manufacturing. The agent reasoned through vendor selection correctly. The escalation framework didn't exist. Most teams building production agents focus on accuracy — can the model understand the task? — and miss the harder question: what happens when the model is right but the policy is ambiguous, or the policy changed six months ago and no one told the agent? The shift isn't to smarter models. It's to policy-aware tool invocation. Before a tool executes, the agent must reconstruct the applicable policy, flag conflicts between policies, and escalate when confidence in policy scope drops below a threshold. That escalation isn't a failure. It's the design working as intended. The cost: latency increases. Some approvals now take hours instead of minutes. Engineering complexity rises — you need a policy version-control system, a conflict-detection layer, audit traces for every escalation decision. The alternative: let agents execute autonomously and manage the compliance and financial exposure afterward. At what scale does your current approval framework break — before or after the first $500K mistake? #Agentic #TechLeadership #Governance

Most design tools are about to become features, not products. This week, Anthropic integrated Claude Design into its toolset. Without fanfare. Just a discreet update that allows Claude to generate production-level user interfaces from a conversation. I saw a junior developer deliver a dashboard in 40 minutes. No Figma. No responsibility transfer. No that "design review" thread on Slack that dragged on for three days. The old workflow: Product manager writes the brief Designer opens Figma Stakeholders comment Developer recreates the dashboard anyway Four tools. Three responsibility transfers. Weeks of latency. The shift to a more assertive approach is brutal: the tool doesn't design the interface. It reasons about it. Layout hierarchy, accessibility contrast, component reuse, state management—all decided in the context of the code it will be delivered with. Cost stable. Iteration cycles reduced by approximately half in our last sprint. The downside no one mentions: you lose the artifact. No .fig file. No source of truth outside of the prompt history. For regulated industries or large-scale design systems, this represents a real governance problem—not a deal-breaker, but not a trivial one. Figma and Adobe aren't going away. They're going to become more specialized. The way forward is Vertical AI, design agents specializing in compliance interfaces for fintech, accessibility in healthcare, and industrial control panels. Generic models lose. Domain-based reasoning wins. Your competitive advantage in SaaS was never the pixels. It was the opinion embedded in them. Is your product defensible when the interface becomes a commodity? #aiagents #verticalai #productstrategy #anthropic

Your 2026 Enterprise Won't Have Apps. It Will Have Decisions. Most teams shipping agentic systems today are automating the wrong thing: the task, not the judgment call underneath it. A financial services firm built an agent to approve vendor contracts under $50K. Looked good in staging. In production, the agent approved a contract for a vendor already on the exclusion list—one the procurement team had flagged three weeks prior. The agent's tool integration was static. The policy source was moving. No one owned the delta. Cost: $180K in unauthorized spend, plus the audit lag to detect it. The real problem was not the agent. It was the assumption that a single integration point between the agent and the policy layer could stay synchronized without governance. Here's the shift: Stop thinking of agents as task executors. Think of them as reasoning engines constrained by policy gates. This means: → Policy versioning becomes a first-class problem, not a data team afterthought → Tool invocation requires a pre-flight check against the latest policy state → Every decision is logged in a format an auditor can reconstruct, not a format that satisfies the engineer The trade-off is latency. You cannot invoke a tool without checking policy. That check has cost. At scale, it adds 200–400ms per decision. If your SLA is sub-second, you have a real problem to solve. But the alternative—discovering policy drift six months into production—costs more. Who owns the gap between the agent's tool contracts and the policies that constrain them? #Agentic #Infrastructure #Production

Lovable just shipped a native Databricks connector. The architecture is clean: Lovable is the interface layer, Databricks remains the source of truth, data queried at runtime. No ETL, no sync jobs, no shadow copies. Business teams describe what they need in plain English and get a working app in hours. This matters because the bottleneck in most enterprises was never the data warehouse. It was the translation layer between a business question and a tool that answers it. That translation used to cost weeks of engineering cycles and ticket queues. Now it costs a conversation. The companies that win this cycle won't be the ones with the best infrastructure. They'll be the ones that let the entire org build on it. https://lnkd.in/dMuqRZVk #AI #Lovable #Databricks #EnterpriseAI