Which AI Adoption Stage Are You At?

By the end of 2024, WSI, a global omni-channel tech consulting company, published a report on the AI initiatives implemented by SMB leaders.

Surprisingly, although nearly all respondents reported that AI adoption and AI-enabled growth are top priorities for executives, 69% admitted that they haven’t taken any concrete steps toward building their AI capabilities.

The report highlighted three main struggles SMB leaders face when trying to leverage AI:

1. Lack of internal AI talent, particularly senior-level AI leaders, to help develop a concrete strategy.

2. Limited understanding of AI’s value due to a lack of foundational AI knowledge. For many leaders, the term "AI" remains too vague to fully grasp.

3. Confusion around AI adoption—many struggle with how to implement AI and measure if they're on the right track.

   
   

The first struggle is fairly easy to identify, and in my previous article, I’ve explained the importance of acquiring business-level AI knowledge as a leader. Today, I’ll focus on the third challenge and break down the three stages of AI adoption.

Stage 1: Using AI Tools to Facilitate Specific Tasks

At this stage, companies begin using basic AI tools to address specific tasks. These might include tools like ChatGPT, AI-assisted email drafting, chatbots, or AI-based reception desk call agents. Typically, these tools are discovered and tested by individual team members, who then recommend them to colleagues. However, the use of AI has yet to be integrated into larger workflows, and employees tend to associate AI with individual tools. This can sometimes lead to misconceptions about what AI is and how it can be used across the organization.

Stage 2: Integrating AI Agents Across Workflows

At this stage, companies integrate AI agents—combinations of different AI tools—across workflows to facilitate a series of inter-connected tasks. For example, an AI-powered note taker could transcribe a meeting, share the notes with participants via email, generate tasks based on the meeting, and track progress. What used to be handled manually by project managers is now automated by AI, freeing up valuable time. At this stage, employees begin to see AI as a versatile technology that can significantly improve productivity.

Stage 3: Leveraging Business Data with Customized AI Models

This is the most advanced stage of AI adoption, where companies use customized AI models to analyze their own business data to drive strategy and discover insights. At this stage, leadership understands that the real power of AI lies in enabling data-driven decision-making. AI can help identify opportunities or risks early by analyzing patterns and making predictions based on business data. For example, companies might use AI to detect early market shifts by extracting customer behavior patterns in the marketing data, to personalize services using client feedback, or to predict equipment downtime based on historical maintenance data. Companies at this stage typically have a solid data infrastructure to support comprehensive AI analysis and may employ in-house AI talent to develop custom AI models.

Summary: The Three Stages of AI Adoption

In essence, these three stages of AI adoption reflect increasing value to the business and a deeper understanding of AI. These stages can coexist within the same organization depending on its needs.

As a business leader, it's important to understand where your company stands on the AI adoption journey. By aligning your resources and needs with AI, you can gain a competitive advantage and position your business for growth in an AI-driven future.

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