Steal These 5 Ideas From Bees for Better AI

This article was written by Mark Palmer, host of Executive Programs for Dataiku. Mark is a data and AI industry analyst for Warburg Pincus and a board member for six AI, data management, and data science companies. Time Magazine named him “A Tech Pioneer Who Will Change Your Life.” Mark is a LinkedIn Top Voice in Data Analytics.

AI makes us smarter, busier bees. Research from MIT found that AI makes humans 44% faster and 20% better in critical thinking tasks through its ability to summarize, reason, code, engage in dialog, and make decisions. But as AI-infused apps and agents fly around the enterprise like bees around a hive, a new challenge emerges: how to knit insights together from several, dozens, or even hundreds of AI agents. 

It’s an important question to address. Like a team of bees, AI becomes more powerful when it composes insights from thousands of sources — customers, products, and partners. This intelligence comes from a metaphorical beehive of enterprise software systems — CRM, HR, ERP, and more — each instance has its own AI “worker bee.” 

Bees can show us how to harness this swarming intelligence. Like each AI, each bee in a hive is intelligent and autonomous yet cleverly orchestrated by a sophisticated communication, organization, and leadership system. Here are five ways you can think like a bee for better AI.

1. Waggle Dance Your Way to Collective Decision-Making

Bees make collective decisions through a democratic process that involves scout bees and waggle dances. The duration of the dance communicates the distance to the resource from the hive, the angle indicates direction, and the dance's vigor conveys the resource's richness. Each bee understands these “standard” dance moves to ensure clear communication throughout the hive. 

As AI agents multiply throughout the enterprise, software systems need similar standard communication techniques. A promising one is Anthropic’s Model Context Protocol (MCP), a standard protocol that allows AI agents to communicate the data resources it contains, allows other applications to read that data, or make calls to its resource, like Slack, Gmail, or a database. 

Agentic development tools can help as well. Low-code tools like Dataiku help analysts and programmers build apps that collect and organize responses from multiple agents and create logic that uses each response, handles errors, and takes action depending on responses from each AI.

Another popular waggle-dance-like form of communication with AI is semantic data. Semantic data describes the meaning of information, such as how sales categories relate to products or terminology, idioms, and analogies like “heart attack” are the same as “myocardial infarction.” Research shows that generative AI answers are 92% more accurate when converting natural language to the language of database (SQL) when they have a semantic data layer that bridges this gap in understanding.

So, the first idea to steal from bees for AI is: Do a little waggle dance for better collective decision-making.

2. Organize AI Teams by Caste

Honey bees divide roles into castes: queens, workers, scouts, and drones. Further stratification is made based on age and colony needs. Business leaders should consider doing the same to lead cultures into the AI age. For example, in his recent book, Reid Hoffman identifies demographic groups that embrace AI differently: Doomers, Gloomers,  Bloomers, and Zoomers. Zoomers (millennials aged 35-44) are catalysts — they’re optimistic about AI and act as a good source of ideas. Like scout bees, they help find new ideas and lead research. Those with good communication, team building, and business skills are good candidates to be the next queen bee of AI.

Bloomers and Gloomers are optimistic yet pragmatic about the capabilities of AI. These are your drones. Tap them to sequence roadmaps, lead projects, operationalize projects, poke holes in plans, identify risks, and accept AI project testing. Their skepticism is valuable to ensure AI gets buy-in and proves its business value.

Also, consider a brand new cast of “workers” — agents — when planning work in the enterprise hive. Agents can perform specific tasks in customer service, supply chain management, and document processing. While AI agents are becoming increasingly powerful, they also require oversight, organization, and management. 

Technologists are morphing into AI castes as well. Agents make it easier for business analysts to build agentic code, which democratizes access and forms a kind of “citizen” worker-bee use of data. These less technical users require more guardrails and oversight than traditional analysts and engineers. 

In a beehive, young bees observe and imitate older bees. They watch how tasks are performed and learn by participating alongside experienced workers. Citizen analysts can benefit from observing and imitating experienced programmers and analysts who understand data more deeply. AI leaders systematically mentor and train new AI analysts by pairing them with more skilled, data-savvy workers.

So, the second idea to steal from bees is to think about the classes of workers and how they view AI to develop and harness new sources of labor, capability, and insight.

3. Employ Swarm Intelligence

• Particle Swarm Optimization (PSO), inspired by swarm behavior, helps with tasks like parameter tuning — the process of adjusting the settings of an algorithm, model, or system to optimize its performance, efficiency, or accuracy. 
• Swarm Robotics helps groups of robots work collaboratively to complete tasks like search-and-rescue or exploration. Each robot follows simple rules, leading to emergent intelligent behavior without central control.
• Traffic flow optimization, network routing, and autonomous vehicles employ swarming techniques to update routing based on vehicle location, weather, and congestion.

So, look towards ideas based on swarm intelligence to help agents collectively analyze data and make decisions. This mutual learning can lead to more effective decision-making and adaptation.

4. Nurture New Queen Bees

A queen bee leads her hive through reproduction, pheromones, and egg policing. Enterprise leaders should think the same way.

When selecting new queen bees, leaders should look for intrapreneurs. An intrapreneur is an entrepreneur who drives innovative projects inside (intra) a larger organization. Like a queen bee laying eggs, intrapreneurs generate ideas, protect them, and even abandon them to promote hive growth.

A queen can lay up to 2,000 eggs daily during peak seasons. As Gifford Pinchot explained in his seminal paper on Intrapreneuring, intrapreneurs rapidly generate, experiment, and evolve ideas. They operate differently than traditional corporate execution, which favors long, tedious planning cycles and careful, conservative action. Intrapreneurs move more quickly. They’re autonomous, passionate, bold risk-takers. They set audacious goals, experiment with AI, make mistakes, replan, act, learn, and change direction until they succeed.

Queen bees aggressively police eggs. She destroys unfertilized eggs laid by workers, maintaining control over colony size and stability. AI entrepreneurs do the same. In a private session with C-level analytics leaders and customers of Dataiku, 58% of executives said they get lawyers involved earlier in AI projects to help maintain the safety and efficacy of AI early as they become fertilized and grow. And when you pair legal teams with intrapreneurs, the pace of innovation increases because ideas are vetted and adjusted before they become codified.

Queen bees emit queen pheromones that signal their health and presence, unifying the colony and preventing the creation of new queen cells until necessary. AI intrapreneurs do the same — they evangelize use cases and promote their use. 

So, as you build your AI culture, develop intrapreneurial queen bees  — they’re catalysts of AI innovation.

5. Find Busy Bees

Bees, indeed, are busy. They work up to 12 hours a day and take as many as 50 naps a day. Similarly, highly effective AI leaders use AI vigorously. A recent McKinsey study found that high-performing teams at AI companies are twice as likely to use AI at work than laggards.

In other words, AI excellence depends on hard work from all corners of the hive. But many fear AI. The MIT study on how AI made participants more effective also revealed a disturbing tendency: human laziness and fear. Sixty-eight percent of participants used AI’s responses without modification. Healthy companies, like healthy hives, are driven by busy bees. Encourage the broad use of AI. Ask new hires how they use AI for their daily work, their favorite AI apps, how they think about prompting AI, and what they think about the business implications of using AI in their workflow. 

So, the fifth idea to steal from bees to achieve excellence is to build a team of busy AI bees (and become one yourself).

Waggle Your Way to AI Success

Unlock AI's full potential by embracing bee-inspired strategies: collective decision-making, caste-based thinking about leadership, swarm intelligence, and busy bees that engage with AI and act as intrapreneurial catalysts to nurture new ideas. 

And enjoy your waggle dance of innovation with AI!