Cleaning Up Your AI Strategy: Data Modernization and the Future of Retail
TL;DR by Tippy
The future of retail rests on generative agentic AI for faster, more efficient decision making
Despite AI’s capabilities, human intervention and involvement are paramount
The goal is - and should always be - to make better decisions for your customer
Connect with your data specialists and see how you can improve your datasets before implementing AI
Clean data is essential for results; your generative AI is only as good as the data it’s given
Credit: Niki Inclan | Pexels
We've talked about artificial intelligence (AI) several times over the last couple of months, and for good reason: it's the future of retail. AI is already being used behind the scenes with inventory management, staffing, loss prevention, and even price optimization. Then there's the in-person applications such as automatic checkouts, digital demos, and ad targeting.
These advancements are happening in real-time, and consumers are starting to expect AI at various touch points in their customer experience. And this goes beyond just shopping: they expect AI chatbots for everything from government websites to phone providers for quick solutions.
The big hurdle, however, is keeping things human during the process. Take the aforementioned phone providers. We've all experienced this: you just want to speak to a human customer service rep, but the AI bot doesn't let you. After yelling into your phone for five minutes and explaining your problem in 20 different ways, you're finally referred to a human, or told that someone will call you back.
What a waste of time... (we recommend a simple escape button like pressing 0 or #)
And this isn't a phenomenon. This is a real story that's played out thousands of times, an example of companies not listening to their customers, ultimately annoying them to the point of disavowing the brand.
But the major transition (and impact) is going to be the use of agentic AI to predict solutions. Now, of course, this doesn't mean to take the hands off the wheel - humans still need to be involved to approve the outcomes and ensure your data systems are secure and valid.
The goal is - and should always be - to make better decisions for your customer, while keeping their information safe. If it's faster or more efficient, fantastic. But the priority should lie in making decisions that enable you to focus on your customers. If not, you're missing the point.
"It’s absolutely an imperative that every organization have a strategy to deploy and utilize agents in customer-facing and internal use cases. But that sort of agentic AI strategy requires an understanding and systematic assessment of risks as well as business benefits in order to deliver true business value.”
Sinan Aral, Agentic AI, explained
How to Keep Customers at the Center of Artificial Intelligence
Consumers are becoming increasingly wary about the usefulness of AI – even tech overall. People are tired of being followed with little perceived benefit, especially with the shortage of semiconductors in 2020 and the current drought on memory. Speaking of droughts, consumers are also worried about the water consumption of AI data centers, how they pollute waterways, the increase in slop content, and the lack of transparency or accountability by AI giants.
Even with this in mind, it's important to remember that AI's integration in business is inevitable – imperative even. It's estimated that by the end of 2026, 40% of enterprise applications will use AI agents to automate tasks. So not integrating AI into your workflow will leave you behind.
But how you implement AI into your retail operations is important. Is it going to be customer facing or are you keeping artificial intelligence behind the scenes? That's dependent on if your customers are AI-averse or not.
One thing that you need to ensure regardless of which system you use is that you must have clean data for AI to run effectively.
“As agentic AI matures, standardized protocols and frameworks will enable seamless interoperability, allowing agents to sense their environments, orchestrate projects and support a wide range of business scenarios."
Source: Gartner
The Importance of Clean Data
“Dirty” Data Gives You Dirty Results
IBM says it best, “Bad data refers to information that compromises decision-making because it is inaccurate, incomplete, inconsistent, outdated, duplicate, invalid or biased.” Use this data and you’ll end up with outputs that don’t meet the standard you were hoping for.
“Dirty Data” leads to Underperformance
AI trains on the data you feed it. If it’s being given incomplete or inaccurate information, the results won’t be as comprehensive, and even inaccurate.
Fix It Again...and Again
A “band-aid solution” is temporary. If you don’t have clean data to begin with, you’ll be remedying outputs daily, wasting time that could be used more productively.
Using “Dirty Data” Wastes Money
Considering everything listed, using “dirty” data wastes time and money, which is the antithesis of what AI was promised to do.
Data Modernization for Retail and Agentic AI
Having clean data for AI is like having a comprehensive training program for your employees.
This also begs the question: How do I clean my data?
Go straight to the source. Leverage your data analysts. In retail, they can go by different names depending on the organization and even the department. There are inventory analysts, operations specialists, marketing managers, loss prevention associates, and many other variations.
They should all be pulling numbers based on their department. Understand where they're getting their numbers from and ask if there are ways to improve data quality and accuracy. The cleaner the data is at the beginning, the higher the chances will be that you'll have clean results.
Don’t rush this step. As we mentioned previously, "garbage in, garbage out." Your predictive results won't be accurate if the inputs aren't, resulting in solutions that won't hit the mark. The good thing is that you can use AI to start sifting through the data points once you start gathering information.
Once the data is clean, you can start getting to work with AI by triangulating multiple data sources in real-time. This is otherwise known as an omnichannel; we talked about it at length in a previous blog post. You can apply these results throughout your entire business, including scheduling, pricing, advertising, salesfloor planning, and inventory.
Source: Vitaly Gariev | Pexels
Staying Ahead of the Retail Game with Data Modernization
As the saying goes, "you only know what you know." So, keep an eye on the data sets and the automated outputs. Agentic AI is simply a tool, and it only does what it's programmed to do. As much as there are innovations in data modernization, a human eye is necessary to keep it on track. Regularly audit, assign someone to oversee your AI processes and the originating datasets, and continue to iterate as needed.
Agentic AI is the modernization of data. The industry is leveraging the findings of daily operations in new and innovative ways that are pushing boundaries so retailers can better serve our customers, on and offline.
As we create this utopia where retail management roles can be supplemented by agentic artificial intelligence, keep your customers at the center of your operations. Only then is implementing a new tool or system worth it.
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