How a “Hammer Mentality” Undermines AI’s promise and purpose.
In the summer of 2024, I was hired as a UX/AI strategist at a Fortune 150 pharmaceutical company to help them move beyond random AI experiments toward something more intentional. During my presentation to senior leadership, I explained how we could and should build toward ‘Organizational AGI’ — AI systems that have the general intelligence to understand a user’s specific context, culture, and goals.
A VP interrupted me mid-sentence.
“AGI is not going to happen in our lifetime. AI is just a hammer looking for a nail.”
That dismissive comment wasn’t just wrong, it was revealing. He’d diagnosed our industry’s dominant and misguided approach: building impressive tools without understanding what we’re actually trying to construct. What he saw as a “hammer” is more like concentrated potential — a tool that is capable of becoming whatever we’re wise enough to make it.
Seven patterns and the orchestration gap
The Project Management Institute identifies 7 distinct patterns of AI: hyper-personalization, autonomous systems, predictive analytics and decisions, conversation/human interactions, patterns and anomalies, recognition, and goal-driven systems. For years, these existed in isolation — recommendation engines here, image recognition there, predictive analytics elsewhere.
Then ChatGPT changed everything. Suddenly, the conversational pattern could be layered on top of all the others, creating unprecedented possibilities for integration–and equal potential for fragmentation. Conversation now promises to unify capabilities even as it risks scattering their impact when deployed without systemic intent.
Key evidence of our struggle:
- 80% AI failure rate
- 70% failures trace to people/process gaps
- 42% of initiatives scrapped mid-flight
But here’s what’s crucial to understand: this chaos is both predictable and necessary.
The natural evolution pattern
Every transformative technology follows a similar arc. What Gartner calls the “hype cycle” — and what we’re experiencing now with AI — has predictable phases:
| Phase | Characteristics | AI Example |
| Emergence | New capabilities appear | GPT-3 demonstrations |
| Experimentation | Widespread tinkering | Current ChatGPT integration attempts |
| Disillusionment | Reality doesn’t match hype | 80% failure rates |
| Enlightenment | Best practices emerge | ←We are here |
| Productivity | Systematic implementation | The goal … Organizational AGI |
The scattered experimentation we’re witnessing isn’t a failure — it’s how organizations learn. The problem isn’t the tinkering; it’s the lack of systematic thinking about what we’re trying to build. ChatGPT’s breakthrough revealed our failure to thoughtfully orchestrate the 7 established patterns. Instead of designed integration, we got random acts of intelligence.
Turing’s systematic vision
This is where history offers crucial guidance. Alan Turing, the pioneering computer scientist who broke Nazi codes during World War II and conceived the Turing Test — imagined his thinking machine before the computers needed to run it even existed. His compass? Crossword puzzles — those devilish tests of linguistic nuance, cultural references, and contextual understanding.
Turing’s systematic framework:
- Define the outcome (machines that think via language)
- Design evaluation methods (the Turing Test)
- Engineer solutions
His vision for machine intelligence was fundamentally about creating systems that could comprehend and work with language the way humans do — remarkably prescient now that conversational AI has become the breakthrough transforming how we interact with all other AI capabilities.
We’ve inverted his process. Like chefs obsessing over knives while forgetting recipes, we ask “What can this AI cut?” before “What nourishment should we create?”
The seven AI patterns already exist and are well-established. The breakthrough isn’t new capabilities emerging — it’s learning how to systematically orchestrate these existing patterns into coherent systems that serve designed outcomes.
Babel’s digital rebirth
Systematic orchestration with agentic AI faces a fundamental challenge that the Tower of Babel story illustrates perfectly. The Biblical tower failed due to communication errors, not engineering failure. Today’s digital Babel manifests when we underestimate the complexity of human communication itself:
- Healthcare chatbots missing vocal urgency in patient messages.
- Recommendation engines violating cultural dietary codes.
- HR bots misreading resignation subtext as engagement.
- Customer service AI escalating frustrated users instead of de-escalating.
The data reveals why: In high-stakes communication, words convey just 7% of meaning. Tone (38%) and body language (55%) carry the rest (Mehrabian, 1971). Yet we deploy AI agents as if text alone can capture human communication complexity.
Large language models offer powerful tools for bridging communication gaps, connecting back to Turing’s original fascination with language understanding. But the orchestration challenge is designing systems that account for the full spectrum of human meaning-making, not just the 7% that’s easiest to digitize.
This is where random experimentation becomes insufficient. We need systematic approaches that preserve context, understand nuance, and integrate multiple AI patterns thoughtfully.
Designing the invisible orchestra
Robb Wilson and Josh Tyson’s book “Age of Invisible Machines,” which was released before ChatGPT made its entrance into our common consciousness, paints a systematic picture of what AI orchestration looks like. Rather than showcasing individual capabilities, their book focuses on orchestration ecosystems where multiple AI agents work together seamlessly to create experiences so well-integrated that the technology becomes invisible.
Here are the principles they lay out for moving beyond random acts:
- Outcomes Before Outputs: define the problem you are trying to solve first (e.g., ‘reduce patient anxiety’, not ‘build chatbot’).
- Context Preservation: design systems that remember across interactions (How did the user really feel last time?).
- Ethical Emergence: don’t just test whether a solution works or not; ask if it is somehow improving outcomes for humans.
| Approach | Random Acts | Orchestrated Systems |
| Starting Point | “What can AI do?” | “What experience should this enable?” |
| Context | Isolated interactions | Preserved meaning across touchpoints |
| Integration | Standalone features | AI patterns enhancing human workflows |
| Learning | Technical metrics | Human outcome measurement |
This represents the systematic thinking that emerges as technologies mature — moving from capability-driven development to purpose-driven design.
The conductor’s baton
The VP at the pharmaceutical company I left wasn’t wrong about tools — he just missed the symphony. When we trade hammers for conductor’s batons, the 7 patterns transform:
- Predictive analytics → Foresight that prevents crises
- Chatbots → Conversational de-escalators
- Goal-driven systems → Ethical co-pilots
- Recognition systems → Context-aware interpreters
This isn’t about new tools — it’s about intentional orchestration of existing capabilities into systems that enhance rather than fragment human experience.
We’re approaching a transition point. The trough of disillusionment will eventually give way to what Gartner calls the “slope of enlightenment,” where best practices emerge and systematic implementation becomes possible.
The ambition of the Tower of Babel ultimately ran aground because people couldn’t coordinate. We’re building something similar with AI, but we have both Turing’s systematic sequence and the orchestration vision of trailblazers like OneReach to help us navigate. The question isn’t whether AI will transform the human experience — it already is. The question is whether we’ll approach that transformation with the intentionality and wisdom to meet the challenge.
Featured image courtesy: Yves Binda.
