Adopting AI in Business

Crawl: Leveraging AI-Enhanced Tools

This is the most accessible level, where your teams use existing software with powerful AI features already built-in. It requires no specialized skills and offers immediate productivity gains.

• Automated Insights: BI dashboards that use AI to automatically surface key trends.
• Smarter Search: Next-gen search engines that use LLMs to understand context and intent.
• Instant Communication: Apps that use AI agents to instantly and accurately translate text or speech.

Walk: Empowering Individuals with LLMs

At this level, employees consciously use standalone AI tools to augment their personal productivity. This phase fosters a culture of AI literacy with minimal investment.

• Accelerating Content Creation: Using an LLM as a writing assistant to draft and refine reports.
• On-Demand Creative Ideation: Leveraging an LLM as a brainstorming partner.
• Rapid Research and Synthesis: Using an LLM as a knowledge assistant to synthesize information.

Run: Deploying AI Agents & Integrating AI

The Shift in Agency: In the "Walk" phase, the human has the agency and uses AI as a consultant. In the "Run" phase, agency is transferred to the AI, giving it the tools to execute work on your behalf. That is the definitive shift from Chatbot to Agent.

Here, AI moves beyond individual use to become a fundamental component of core business workflows, creating significant operational efficiencies.

• Automated Reporting: Analyzing complex datasets with an AI agent to provide actionable insights.
• Predictive Logistics: Integrating learning agents into the supply chain to optimize inventory.
• Personalized Marketing at Scale: Utilizing AI to generate personalized marketing copy.

Fly: Building Enterprise-Scale AI Solutions

The highest level involves building custom, enterprise-wide AI solutions that create a significant competitive advantage or transform business models.

• Dynamic Financial Modeling: An agent that analyzes market data for adaptive financial forecasts.
• Proprietary Fraud Detection: A custom learning agent that monitors transactions in real-time.
• Autonomous Operations: A network of multi-agent systems coordinating robots and drones.

The AI Delivery Team (Run & Fly)

During the Crawl and Walk phases, existing IT staff and business leaders can typically manage adoption. However, as you graduate to the Run and Fly stages—building agents and custom integrations—specialized roles become critical. We organize these critical functions into three core teams.

1. The Strategy Team (Vision & Value)

This group defines the "Why." They ensure the AI solves a real business problem rather than just being a technology demo.

• Business Stakeholders & SMEs: The domain experts who define the goals, provide the context, and validate the results.
• Governance & Ethics Lead: The "conscience" of the project, ensuring data privacy, fairness, and compliance are baked in from day one.

2. The Build Team (Engineering & Data)

The builders who turn raw data into intelligent insights. In modern workflows, these roles often overlap.

• AI Solution Architects: A hybrid role combining the skills of Data Scientists and ML Engineers to design, train, and prompt the models.
• Data Specialists: They build the pipelines to ensure the AI is fed with clean, reliable, and secure data.

3. The Operations Team (Adoption & Maintenance)

A great model is useless if no one uses it. This group ensures the solution survives in the real world.

• Project & Product Lead: The bridge between the technical team and the business goals, managing the roadmap and timelines.
• Change Management & Ops: They focus on the human side—training staff to use the new tools—and the technical side, ensuring the system stays online and scalable.