Artificial intelligence (AI) and machine learning (ML) technologies aren’t just nice to have for business. Today they’re considered essential competitive advantages. Reflecting this sentiment, a recent Revionics-commissioned study conducted by Forrester Consulting found that 76% of retail executives worldwide believe AI can help boost the bottom line.

That’s why we’ve put together a concise guide to practical uses of ML and AI, what they can do, the most common pitfalls to avoid, and how to choose the best partner and solution. Here’s a quick overview.

Harnessing the Data Explosion

The power of the cloud has let even small organizations apply ML and AI to massive amounts of data to derive new insights and enable autonomous decision-making.

ML algorithms can predict future outcomes based on past data, classify items or customers into useful categories, and uncover hidden insights. AI may include machine learning, but it also takes in a broader range of techniques, combining art and science, for making sense of data.

At Revionics, we define AI as technology that leverages data to help make decisions likely to produce beneficial outcomes. For retailers that may mean using AI to help cut operating costs or boost customer engagement.

Overcoming Challenges

Challenges to implementing AI include not having enough training data, relying on irrelevant, imperfect or ambiguous data, and basing solutions on flawed assumptions.

To overcome these challenges, ask some key questions about a given solution:

  • What guardrails and constraints can mitigate the risks of uncertain outcomes?
  • Are goals defined for the system reasonable?
  • How does the system adapt and overcome challenges when there is imperfect or sparse data?

Build or Buy?

As AI technologies mature, more and more organizations are turning to outside vendors rather than trying to develop their own applications.

That’s because AI development comes with significant challenges, including a shortage of talent and the steep maintenance and continued innovation costs associated with cutting-edge, rapidly evolving technology.

Fortunately, all that’s needed to successfully identify a vendor partner is some basic grounding in how ML and AI work, awareness of the pitfalls to avoid, and knowing what questions to ask vendors when evaluating competing solutions.

Check out the complete guide, “Evaluating Machine Learning and Artificial Intelligence Solutions”.

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