As the public conception of AI has evolved since late 2022, mass adoption of large language models (LLMs) like ChatGPT and an influx of venture capital dollars into automation enablement has muddled the conversation around what “AI” is. It’s important we remember that, while LLMs are a subset of AI, the term “AI” is not limited to ChatGPT. Throughout modern history, “AI” has been a comprehensive term for technology that’s modeled after human intelligence. In today’s world, our AI models are trained by large amounts of data. When used strategically, these models can present businesses with tremendous opportunities to streamline operations, and these opportunities extend far beyond what LLMs alone can enable.

Identifying Opportunities to Use AI

“Many businesses have spent years collecting rich tranches of data that they still aren’t putting to work for their business,” says Mike Streams, Partner at W. Capra. This is, in large part, because they don’t understand the potential applications of how to use their data to unlock efficiencies in their working environment. Streams continues, “Organizations should begin by asking what functions their business is performing manually today to understand where opportunities to automate exist.”

To aid with this process, W. Capra has compiled the table below as a starting point:

While the opportunities listed in the table above have been included as examples only, you can tailor this list to meet your specific business objectives by examining each domain of your operations for manual processes.

Using AI to Solve for the Future

It’s one thing to identify opportunities for improvement, but without a strategic vision your AI will become another hype cycle for your organization. “Once you’ve identified potential opportunities, the next step is to tap into your proprietary data to align your organizational decisions to business outcomes,” explains Daniel Kahan, Delivery Lead at W. Capra. “It’s not enough to merely identify an opportunity to personalize product promotions. To put your data to work, you need to understand how personalization can help to optimize average order value, inventory management, and customer retention.”

Though the notion of implementing against measurable KPIs will be familiar to retailers, AI implementation differs from development-based initiatives in that it requires data pre-processing to effectively train the model. “AI is a tool to improve efficiency, but there’s still a human element required,” cautions Streams. “AI implementations require not only structured data, but built-in controls and a clear AI-ethics stance.”

When identifying opportunities to automate and streamline your business operations, it’s important that your team understands how the rapid evolutions of the AI landscape impact both your current operations and your aspirational initiatives. W. Capra serves as a trusted partner to organizations seeking to identify opportunities to automate their operations, ensuring that your data can enable measurable, valuable outcomes.

Mike Streams and Daniel Kahan are passionate about enabling their clients’ success through the opportunities and challenges that AI presents. For further discussion, please reach out to [email protected] and [email protected].