Scaling AI: How To Move Beyond Random Acts of AI

Coming out of MAICON 2024, one thing is very clear: organizations still cannot scale AI.  Multiple sessions covered the top barriers, biggest gaps, and hardest challenges we struggle to solve.  This brings us to the second key point – scaling AI is a serious business strategy.  (Big shout out to Gary Survis, Ashley Gross, and Jessica Hreha for their MAICON material that inspired this post).  

To scale AI effectively, you need to stop treating it like a series of disconnected experiments and instead take a serious, strategic approach. Here’s how to assess and overcome the common barriers to AI scalability like a well-run, mature organization: 

Assess Your AI Pilots and Workflow Experiments 

Take stock of your current AI pilots and where you’re experimenting with AI. Are these initiatives tied to clear business goals, or are they more “random acts of AI”? Review what’s working and what’s not by examining the outcomes and how they’ve integrated into broader workflows. 

Professional Take: Assess each initiative’s ROI and alignment with long-term business objectives. Determine if these AI tools are solving a significant problem or merely offering a short-term, surface-level fix. 

Identify the Barriers to Scaling AI 

Compare your existing AI initiatives against the common barriers to scale: 

• Do you have a clearly defined goal for AI, or is it just “nice to have”? 
• Is your data clean, accessible, and integrated across systems? 
• Are your current processes efficient enough for AI to be applied effectively? 
• Is there alignment across leadership and the organization on the AI strategy? 
• Are your teams ready and equipped to adopt AI in their workflows? 

Address Each Barrier Like a Mature, Scalable Business 

You can’t half-heartedly apply AI solutions if you want to scale. Here’s how to systematically solve these barriers with a serious, grown-up business mindset: 

• Set Clear, Measurable Goals: Define exactly what you want AI to achieve for your business. This is foundational. No goals, no scalability. 
• Fix Your Data: Conduct a full data audit. Clean, organize, and integrate your data across systems. No more siloed, fragmented data if you expect AI to be effective.  If this sounds impossible, our Mod Op Strategic Consulting group will help you. 
• Optimize Processes First: Don’t apply AI to broken processes. Streamline and document your workflows, then look for where AI can genuinely augment or automate tasks. 
• Align Leadership and Teams: Ensure leadership buy-in and clear communication around AI initiatives. This isn’t just about tools; it’s about transforming how your organization works. Bring teams along early, get their input, and ensure they have the skills they need to adopt AI at scale. 

Last: Commit to Iterative Improvements – This Is a Journey 

Scaling AI isn’t a one-and-done process. Even after overcoming these barriers, it’s essential to constantly review, refine, and optimize. Approach AI adoption with the same rigor you would apply to any other major business transformation. 

In short, treat AI like what it is — a high-priority business initiative: with a clear strategy, the right tools, and the commitment to doing the hard work required for true scalability.