Associate Member Viewpoint: AI-Ready By 2026: Where To Begin Today

NBWA Associate Member Elevated BI discusses why laying an appropriate data foundation is an essential step for any organization aiming to integrate AI in its day-to-day operations and own its data destiny.

At the annual NBWA conference this past October, the 2-part session on AI was packed with wholesalers eager to learn where they could apply the century's most sought-after technology-and how long they had before falling behind.

The key takeaway? Walk before you run. However, industry leaders agree the window for competitive implementation is closing, and the time to act is now.

CLARIFYING EXPECTATIONS

The first step for leadership is answering: "What do we want to accomplish with AI?"
Broadly, wholesalers seem to be pursuing this in two tracks:

1. Boosting Productivity
If your goal is to increase workforce productivity, tools like GPT or AI copilots are a great place to start. These solutions excel at automating mundane tasks-drafting emails, generating code or formulas, and answering complex questions about existing data models. ChatGPT's latest model, o1, is particularly strong at reasoning through tough problems, and these capabilities will only improve.
Implementing a GPT solution is low-risk (with proper guardrails) and can free up your team to focus on revenue-generating activities. For example, spinning up a ChatGPT Team account will cost you a maximum of $60/month for two users and lets you create company-specific GPTs to test.
The caveat? Productivity gains need measurable ROI.

• Does increased productivity meaningfully impact cost savings or output?
• What if your employees complete their usual 40+ hours of work in 40-and experience higher job satisfaction, boosting retention and reducing hiring costs?

Make sure you clarify expectations upfront. A shiny tool without quantifiable impact may not be worth the investment.

2. Driving Predictive Results
The second, more ambitious path is using AI for predictive outcomes-reducing costs on inventory, routing, or other operational areas.
But before dreaming about what predictive AI can do, you must invest in a data foundation.

THE FOUNDATION MIX

Predictive AI starts with centralizing your data-bringing everything into one place. This means consolidating sources like your RAS, time clock, safety data, historical weather, and third-party vendor information into your own cloud environment. Options include:

• Snowflake
• Amazon Redshift
• Microsoft Fabric or Azure Synapse Analytics

Cloud providers are increasingly reporting-tool-agnostic, so if you're committed to Power BI, Tableau, or similar tools, you'll have that flexibility.

Microsoft is even streamlining the process. Their Fabric product combines the tools needed to handle data engineering, and they've partnered with Snowflake for near-real-time mirroring and enhanced integrations.
Building Quality Data
A foundation isn't just about where your data lives-it's also about the quality of your data. Reliable AI requires:
1. Consistency - Your data sources must deliver information in the same format, every time. This is table stakes for any vendor.
2. Monitoring - Dedicated resources must monitor data pipelines and flag any anomalies to avoid skewing results. Remember: AI is only as smart as the data it receives. Low-quality inputs mean low-quality outputs.
3. Alignment with Business Logic - Leadership needs to align on and clearly communicate out metric definitions and business rules. AI doesn't magically "know" your operations; it must be trained.

• What counts as slow-moving inventory?
• Do miscellaneous route costs factor into your models?
• Are there exceptions that skew normal trends?

These parameters need careful discussion and thorough testing before predictive AI models can deliver reliable results.

CONCLUSION

Building an AI foundation isn't an overnight task. It's a strategic, step-by-step process that requires intention and clarity.
With a well-defined goal and resources to maintain momentum, it's entirely possible to have your distributorship AI-ready by the end of 2025.
So the next question is, are you ready to own your data destiny?

About the author
Elevated BI is an independent consultancy founded by Austin Bristow. He spent the majority of his career at fairlife, LLC, where he played a pivotal role in designing and implementing their world-class business intelligence platform from the ground up. After successfully consulting for beer wholesalers on the side for a year and a half, he jumped full-time to Elevated BI in 2024 to focus exclusively on helping beer wholesalers harness the power of data. Austin specializes in building automated reporting solutions using Power BI, enabling distributors to streamline operations, optimize inventory, and uncover growth opportunities.

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