How soon before AI-focussed ‘tiny teams’ create $100m outcomes?

Blink and you missed it - in barely three years, the AI economy has moved from a curiosity to the operating system of businesses.

After the pandemic, the FOMO (fear of missing out) factor shifted from social media trends. People and companies stopped chasing likes and started rebuilding - faster. Generative AI arrived at exactly that moment, with tools like ChatGPT catapulting AI into everyday workflows while advances in computing power quietly turbocharged what these systems could do.

The result: less time on ‘stuff work’. More time on creative, high-value work.

The next leap is already here: agentic AI. Rather than single prompts producing single outputs, autonomous agents chain tasks, call tools, and coordinate across systems.

Only a minority of firms have embraced this shift, but those that have are turning AI from an assistant into an orchestrator - automating workflows, enforcing quality, and closing loops across platforms from OpenAI to Google, Microsoft, and more.

This isn’t about novelty; it’s about throughput, reliability, and speed.

Agents and super-agents are redefining product development. They don’t just generate ideas; they trigger experiments, run evaluations, and ship improvements. Our team at RaffleTech is applying agentic AI to the hardest part of retail - retention and loyalty. We’re building an AI-first platform that learns from minimal data, personalizes offers in real time, and coordinates campaigns end-to-end.

The promise of AI has always been democratization; orchestration makes that promise tangible by turning fragmented tools into a coherent engine for growth.

If you listen closely, the language of business has already changed. We’ve moved from ‘What’s trending?’ to ‘What problem are we solving today?’

The pace can feel uncomfortable. AI automation isn’t new, but today’s convergence of models, data, and compute has collapsed timelines. What used to take months to prototype now takes days. Rapid A/B testing and data-led iteration are the default.

Products keep learning after launch, whether you’re using large language models (LLMs) or smaller, efficient models tailored to your domain.

This shift recasts the role of people. The new buzzword isn’t automation, it’s AI–human collaboration. The human in the loop as conductor, editor, and ethicist. Agents are moving beyond administrative chores into strategic territory: predictive marketing from sparse signals; creative diversity to avoid performance plateaus; and structured prompting that yields truly engaging customer interactions.

Above all, a super-agent can orchestrate tasks, enforce guardrails, and escalate decisions to humans only when it matters.

AI is becoming a form of literacy. ‘AIliteracy’ will be career-limiting. Organizations that invest in practical fluency rather than slide-deck theory will outpace those that don’t.

One of the most exciting patterns in the AI economy is the rise of tiny teams. Five highly skilled people, amplified by agents, can credibly tackle problems that once required departments.

We’re already seeing small, focused teams build to $100 million outcomes - and the first billion-dollar ‘tiny team’ isn’t far-fetched. The playbook: pick a stubborn, valuable problem; wire up an agentic stack; ship relentlessly; let data drive the roadmap.

What’s next? Embodied and on-device AI will push intelligence closer to the point of action, while enterprise-grade orchestration will make agents safer, auditable, and compliant. The imperative for leaders is clear: move from pilots to production, from experiments to owned capability.

Start with a real business problem, not a showcase. Put a human in the middle, a super-agent on top, and metrics at the core.