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AI Strategy - middle market

How To Create an AI Strategy for Middle Market Companies  

Designing and developing machine learning models at an organization of any level should be a highly strategic endeavor. And for middle market companies, there is much less room for costly AI failures. 

To realize the full value and impact of AI for your organization, your AI strategy must align with business objectives, identify appropriate AI use cases, and set realistic expectations

Continue reading to uncover the key steps to developing an effective middle market AI strategy. 

1. Identify AI Use Cases

It’s not feasible (nor necessary) to design an ML model for every business process or decision. Instead, start by analyzing your current processes and pinpointing tasks or decisions that could be augmented with AI. When identifying these opportunities, ask yourself: 

  • Where are team members spending a lot of time
  • Are there areas of our business that deal with overwhelming amounts of data (e.g. customer reviews, survey responses)? 
  • What repetitive simple tasks could be automated or optimized? 

Taking the time to identify potential AI use cases not only narrows the focus of your project, but also enables you to prioritize initiatives that best align with business objectives.   

2. Prioritize AI Initiatives 

Once you’ve identified all areas of business that could be impacted by AI, it’s time to decide which initiatives make the most sense for your organization to pursue first. Comparing use cases against business goals and level of perceived impact is especially important for companies with limited budgets.

Some organizations will also start with “quick win” use cases—shorter, less expensive projects that demonstrate the value of AI to stakeholders and increase buy-in for future investments.

3. Estimate the Business Value of AI 

What does success look like for this AI project, and how will you measure it? Realistic expectations and reliable KPIs are nonnegotiable components of a sound middle market AI strategy. Your team should not only have a clear understanding of the anticipated model failure rate, but also its associated ROI if implemented properly. 

It’s best to narrow your KPIs down to one or two that can be clearly measured. While success metrics will vary from company to company and project to project, they may relate to: 

  • Data quality
  • Security
  • Privacy (with a metric like location entropy)
  • Fairness (with a metric like proportional parity) 
  • Customer satisfaction 
  • Speed or accuracy of decision making 

4. Assess Your AI Readiness

You likely already know of the gaps between your AI goals and what you’re internally positioned to accomplish. But, it’s still important to identify any areas that may impact your success rate with AI if left unaddressed. These might include: 

  • Quality of data and any existing data strategies 
  • Breadth of machine learning expertise 
  • Internal data scientist bandwidth
  • Current technology stack   
  • Level of team training required to make decisions using ML models / AI-powered predictions

Like any major business investment, your AI initiatives must be driven by a well-organized, thorough strategy. Not only will a strategy align your projects with business goals, but it will also position your team for the greatest path to success.

Learn More About Trustworthy AI Strategy 

A comprehensive AI strategy is just one factor that fuels a successful AI-powered company. To learn how developments in the AI industry impact your initiatives, and receive expert advice, subscribe to Pandata’s Voices of Trusted AI email digest. Each month, you’ll receive an email containing helpful trusted AI resources, reputable information, and actionable tips.

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Contributor: Nicole McCaffrey is the Chief People & Marketing Officer at Pandata.