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Making AI and data strategies work together in 2024

artificial intelligence
AI is a crucial part of retailers' data strategies.

Artificial intelligence has been around for decades, but with the launch of ChatGPT last year, generative AI has become a tool that is impacting nearly every business. 

For CPG companies and retailers who are tasked with staying ahead of ever-evolving consumer behaviors and new economic pressures, AI can solve several challenges and turn data into actionable insights. It can transform systems and processes throughout the supply chain, elevate shopper experiences with highly personalized product content, and help companies more clearly understand consumer-buying behavior to create actionable strategies. 

However, AI is not magic. To fully take advantage of what it can offer, companies must first ensure they are working with high-quality data, use AI to address the right challenges and never underestimate the power of human touch. 

Big data and high-quality data are both critical to power strategic decision making

McKinsey has predicted that by 2025, data will be embedded in every interaction, and process. And as AI starts to impact numerous parts of a business, how the two components work together becomes key. AI requires a tremendous amount of data to effectively learn and make decisions, and big data needs to leverage AI for better analysis. 

However, you’re only as strong as your weakest data point. High-quality data is required to train AI models in order for them to be high quality models. Simply put: The more accurate the data, the more accurate the model prediction. Inaccurate data creates exponential errors. For example, if the AI is missing information, it will fill in the blanks with what seems correct, based on algorithms. It will also assume that it has all the data it needs. If the data is out of date, the decisions AI makes may leave out critical updates.

Companies embracing AI need to champion data quality. They need to ensure that datasets are complete, with no empty cells. The industry is also exploring ways to accurately fill in these gaps based on high-quality data. For example, generative AI may be used for predictive text, filling in missing receipt information to inform insights. The datasets should also include consistent variables. 

And of course, accuracy and validity, at every level, is critical. Successful AI implementations require iterations on the underlying data. To get the most out of your AI, you want to make sure you are pulling from a trusted source providing data that is updated regularly and carefully reviewed. 

Manage data sprawl to more efficiently act on insights  

In today’s dynamic retail and CPG landscape, data and analytics are critical to success. But vast ecosystems of disparate data formats and sources make it more difficult than ever to prepare, visualize, interpret, and act on insights. Incorporating AI into this analysis can be a game changer. For example, AI can enhance supply chains, helping companies more efficiently manage and optimize logistics. Another common application for retailers is using AI to automate item coding and classification, which is especially critical as the volume of e-commerce information skyrockets. 

AI can also provide automation on retailer-defined hierarchy definitions, generating automatic rules that can code characteristics a retailer is looking for. And the new generation of Intelligent Voice Assistants can deliver hyper-personalized responses by reviewing data analytics, previous conversations, location, and product information.

What once felt like an overwhelming amount of data can be quickly analyzed and acted on by AI. But frequently, this too is complicated by disconnected or isolated data stacks. Multiple datasets are only valuable when they can talk to each other. Retailers must connect the right sources of data to successfully analyze omnichannel behavior. 

They need to evaluate how and when data is collected across platforms and simplify and accelerate access to massive sources of data—ideally accessing and querying data instantaneously. And they also need to scale effortlessly as their business grows. 

A human touch is still a requirement 

While the possibilities may seem endless, AI is still often met with both excitement and concern. While the biggest transformation of generative AI is its ability to allow more non-technical people to effectively ask and answer questions, one of the most common concerns is the fear that AI will replace humans in the workforce. While AI can process data and identify patterns in amounts and at speeds that humans simply cannot, it can’t replace the human touch.  

For a successful future using AI, humans must continue to play a key role—in both the planning and the execution. The best results will come from combining AI with critical thinking. What humans bring to the table that AI cannot is real life experience, creativity, and intuition. And the ability to check the work and prevent dreaded AI hallucinations—outputs of confident and authoritative language which is nonetheless completely incorrect.​ 

The bottom line is that to successfully work at scale, CPG companies and retailers must embrace AI and prioritize high-quality data. As AI becomes an increasingly prominent part of the equation, agile platforms that can easily adapt to changing requirements will be essential. New tools democratize like never before, but to take full advantage, companies still must focus on structure, unified systems, and simplified approaches. 

And they must ensure they are partnering with trusted data and insights providers who are taking a deliberate approach to enhancing and operationalizing their models, thoughtfully integrating cutting-edge technologies, and continually assessing and improving performance.

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