Run your AI team like a Premier League Champion

Liverpool is now 5 points ahead at the top of the table after a solid victory over Aston Villa (COYR!). Watching the Reds work seamlessly together is an excellent example of how specialization can help a team achieve its goals.

The AI Revolution Needs a Game Plan

Recent stats show that 83% of company executives view AI as a strategic priority, but only 1/3 of businesses use artificial intelligence (or at least think they do). Why the gap? Like in soccer (football?), success isn't just about having talented individuals—it's about how they work together.

We can build a team of AI agents in much the same way that Arne Slot puts together a winning starting 11 each week.

Building Your AI Starting Lineup

Goalkeeper - (Foundation Models)

The goalkeeper leads the team from the back. Your foundation models serve as your AI team's backbone. They need broad coverage of your domain and the ability to handle unexpected inputs. Think of them as your GPTs or Claudes - highly capable but requiring significant resources.

Defenders - Safety & Compliance Agents

Your defensive line consists of agents focused on security, bias detection, and compliance. They work tirelessly in the background, ensuring outputs meet regulatory requirements and company standards. Training agents on your company's standards and expectations will help them sift through other agents' output to ensure it is compliant and correct. One missed assignment can lead to a costly goal against your organization's reputation.

Midfielders - Orchestration Layer

The engine room of your AI architecture. These agents handle routing, prioritization, and the smooth flow of information between systems. Like great midfielders, they need excellent vision, which comes from workflow development and business process training, to see developing opportunities and the intelligence to distribute resources effectively.

Forwards - Specialized Task Agents

Your goal-scoring specialists. These are highly focused agents optimized for specific tasks like code generation, data analysis, or customer service. They thrive on clear objectives and consistent delivery in their specialized domains.

Building Championship Chemistry

Here's where many organizations stumble. Excellent individual AI capabilities aren't enough – you need them to work in concert. Consider these implementation steps:

Define Clear Roles

  • Document specific responsibilities for each agent
  • Establish clear handoff protocols
  • Set performance metrics aligned with business goals

Practice Makes Perfect

  • Implement robust testing environments
  • Run regular simulations of complex scenarios
  • Monitor and adjust based on real-world performance

Foster Team Intelligence

  • Build feedback loops between agents
  • Create shared knowledge repositories
  • Develop adaptive coordination mechanisms

The Future of AI Teamwork

As we look ahead, the most successful organizations will be those that master this team-based approach to AI. We're moving from the era of individual AI capabilities to orchestrated AI systems that function like championship teams.

A key trend to watch is the emergence of "AI managers" - systems explicitly designed to coordinate other AI agents, much like a captain on the field. Swarms is one of the latest attempts to make orchestration of multi-agent systems more accessible for everyone. These systems will become increasingly crucial as AI deployments grow in complexity.

So what do we do now?

Take a hard look at your AI strategy. Are you building a collection of talented individuals, or are you creating a championship-caliber team? Start by mapping your current AI capabilities to these roles and identify where you might have gaps in your lineup.

I'd love to hear your thoughts on how you are approaching AI orchestration in your organization. What challenges have you faced in getting different AI systems to work together effectively?