How You Can Use AI to Grow Retail Business

Retailers are taking advantage of AI in a variety of ways, including:

Recommendation engines and personalized marketing
Demand forecasting
Chatbots and virtual assistants
Dynamic pricing
Delivery route optimization
Fraud detection
Customer behavior analytics

And these are just a few of the ways in which AI is revolutionizing retail.

In order to provide these benefits, AI systems need access to a great deal of data, and that’s where things get complicated. As Forrester Research points out, although AI initiatives are increasing, scaling those initiatives into widespread production across the enterprise remains low. Why? And what can be done to change it?

Data, Data Everywhere

Data can be located in a variety of locations within the corporate technology ecosystem, and in a variety of formats. Keeping track of all the available data, where it is stored, and how to access it can easily become a full-time job in itself. That’s not even counting all the data in local storage or trapped in the information black holes that are Slack, WebEx, Confluence, and other collaboration apps. As technology has advanced, our ability to find the right data has not kept pace with our ability to create data or develop ingenious ways of storing it—the race isn’t even close.

Highs, Lows, and Silos

As organizations grow and become more complex, data silos quickly proliferate. It’s not long before businesses have much more data than they know about, a state of affairs that might be described as Unknown Knowns. Geographically separated teams might not know about one another or have only the haziest idea of what the other groups are working on; different parts of the company might use different technologies, making interoperability difficult; or any combination. Knowing what is known becomes ever more difficult.

Forrester Research estimates that 70% of organizations miss valuable opportunities as they struggle to integrate data across systems, and knowledge workers waste 30% of their time searching for information trapped in data silos. Finding data fit for purpose is exhausting, expensive, and leaving knowledge workers frustrated and burned out.

But AI Can Help, Right? Right?

The good news is that yes, AI can help. The bad news is that simply plugging in an AI is only going to be useful for a narrow range of problems. More broadly, an AI isn’t going to just know the answers to your questions unless you’ve trained the AI on your data. While that’s a possibility, it’s also a lot of data movement, data preparation, and processing. If you could do that, you’d have already solved the problem and broken down your data silos.

You would also have to deal with maintaining multiple versions of your data and keeping them in sync. While there are tools that can help with that problem, once data is ingested into an AI figuring out which version of the data the AI is using becomes extremely problematic. Few, if any, existing data management tools were built for that scenario.

Assuming you address all of those issues, you would still need to address new data and real-time data.

Another serious problem is that what an AI knows an AI can leak. Training an LLM on your data means putting your data—the crown jewels of your business—into an AI. And that is a security nightmare. Sufficiently clever prompt engineering can trick the AI into divulging all sorts of information that the questioner should not be able to access.

Bringing the data to the AI is not a good solution. A far better solution is bringing the AI to the data.

Bringing the AI to the Data

The distinction between training an AI on your data and allowing an AI access to your data may seem minor, but in that distinction is a world of difference. In the former case, the data becomes part of the AI’s knowledge base. In the latter case, the AI can be used to analyze data and determine relevancy, but it doesn’t remember what it has seen. This second approach requires much less data movement, avoids data duplication, and doesn’t compromise data security. It also lets you leverage the power of AI to identify relevant data in any location, including newly generated real-time data—for example, capturing customer browsing and purchasing activity as it happens.

Bringing the AI to the data requires AI infrastructure software that provides several key capabilities.

Universal Connectivity

The AI infrastructure software must connect with numerous applications and data sources regardless of format or application type, including structured data from databases, content from email, office documents, and collaboration apps such as Teams, Webex, Confluence and Slack. Source agnosticism makes it possible to leave data in place, adopt existing security protocols, and search only those data sources that the user is permitted to view.

AI Flexibility

AI infrastructure software is not the One True AI that can solve everything. Rather, it is a framework that lets you connect to an arbitrary number of LLMs and AI models. You can take advantage of the power of any AI without designing your system around that AI. You can swap models in and out as the situation requires, whether that means using specialized models to handle specific, domain relevant tasks, or monitoring drift and swapping out models that are no longer delivering useful results.

Powerful, Unified Search

AI Infrastructure software should keep track of the data sources that a knowledge worker is permitted to access, so that you don’t have to keep track manually. It should enable searching of all permitted data sources and then use AI to help manage the results, pruning and ranking by relevancy so that you are able to quickly find the information that you are looking for. It should enable dynamic, interactive refinement of the search results, essentially enabling you to have a conversation with your data.

Prompt Enhancement

Because an AI is not a Generalized Omniscient Device, an AI can return incorrect, inaccurate, or hallucinatory results. AI infrastructure software should incorporate prompt enhancement and use of Retrieval Augmented Generation in order to minimize or eliminate these problems. Combining prompt enhancement, RAG, and other techniques for managing search, increases the validity and trustability of results and frees the user from the laborious process of attempting to take these steps manually.

Ease of Use

One of the biggest limitations on organizations adopting AI is the lack of trained experts. AI infrastructure software should be simple to install. It should be low-code and configuration based and shouldn’t require an army of computer science PhDs to make it work. A major benefit of AI infrastructure software is to make it easier for everyone in your organization to benefit from AI, not just a few experts.

AI Infrastructure Software Makes AI Work For You

AI infrastructure software may sound like wishful thinking, but it exists today and is available right now. SWIRL AI Connect is an innovative approach to AI infrastructure that provides a powerful, efficient, and flexible framework for incorporating AI into your business. SWIRL can evolve and scale with technological advancements so you can avoid being locked to a specific AI model.

Capabilities

SWIRL provides several key capabilities that help you maximize the return on your AI investments:

Connects to Everything: SWIRL connects with over a hundred (and counting) apps and databases, including email, Slack, WebEx, Confluence, Teams, and other collaboration apps, so you can access data no matter the location or format.
AI Agnostic: Avoid vendor lock-in and ensure flexibility by seamlessly swapping AI models to suit your evolving needs.
Specialized Models: Leverage state-of-the-art Large Language Models (LLMs) and easily integrate new, specialized models for specific use cases.
Low-Code and Configuration-Based: Accelerate AI-powered app development with minimal code, freeing you to focus on business results rather than complex infrastructure.
Granular Data Access and Firewall Protection: Maintain tight control over sensitive data and ensure top-level security by deploying AI models within your firewall.
Streamlined AI operations: SWIRL is the middle layer between applications and AI models, helping to manage complexity, optimize resource usage, and ensure the infrastructure adapts to the rapidly changing demands of AI workloads.
Scalability: SWIRL enables AI systems to handle complex and variable demands, such as large-scale model training and deployment, without compromising performance.
Powerful Search: SWIRL combines unified search, prompt enhancement, and Retrieval Augmented Generation to provide more accurate and contextually relevant results than generative AI models can provide on their own. SWIRL enables you to have a conversation with your data—it’s like having an intelligent, interactive data catalog or expert librarian to guide you.

Benefits

SWIRL provides significant benefits:

Better Decision-Making: Enhance strategic decision-making with accurate, real-time insights and predictions.
Increased Efficiency: Reduce expenses and increase overall workflow and productivity.
Improved Customer Satisfaction—RAG enhanced, context-aware AI responses enhance customer interactions and service quality, improves the user experience, and leads to higher customer retention and loyalty.
Enhanced Feedback—Break down silos and make key performance data accessible and available for analysis.
Easier Deployments— SWIRL simplifies enterprise-wide deployments, so you can easily scale AI initiatives across your business.
Faster Time-to-Market—Accelerate development and launch of new AI-powered products and services, enabling quick adaptation to market changes and customer needs.

Improving Business With AI

From recommendation engines to chatbots, dynamic pricing to fraud detection, timing promotions or optimizing deliveries, AI has the potential to be one of the most powerful tools for retail growth ever developed—provided you can balance ease of data access with data safety and security. Fully leveraging the power of AI requires powerful AI infrastructure software that is easy to use, data format and location agnostic, and secure. It must do the hard work of engineering and enhancing prompts to quickly and easily find the right data when you need it, while minimizing AI hallucinations and error.

SWIRL AI Connect is the AI infrastructure software that enables your organization to fully leverage the power of any AI you choose to use. SWIRL brings AI to the data, giving your fast, accurate results without compromising security. With SWIRL you can break down your data silos, energize knowledge workers, and successfully grow your business.

To find out how you can be an innovator in the use of AI, contact SWIRL today.

KM Magazine named SWIRL as one of the top 100 companies empowering intelligent knowledge management. Read the announcement here.