Field guide
AI in wholesale: Hype vs. reality
Get unique data-backed insights on what's real, what's next, and what to do right now with AI.
Where brands really stand today
The excitement is there. The execution isn't.
Only 20%
use AI for demand forecasting
Just 13%
are applying AI to pricing
1 in 3
haven't implemented AI at all
Only 20%
use AI for demand forecasting
Just 13%
are applying AI to pricing
1 in 3
haven't implemented AI at all
Data is fragmented
AI needs unified, consistent data to deliver insights. Siloed systems (ERP, CRM, B2B) prevent accurate predictions.
Solution
Integrate your data systems using real-time syncing and API connections to ensure clean, connected data.
Disconnected systems hinder insights
Disparate tools that don’t exchange data make it hard for AI to provide relevant recommendations.
Solution
Consolidate your tech stack to ensure seamless data flow and eliminate fragmented systems.
Disconnected systems hinder insights
Disparate tools that don’t exchange data make it hard for AI to provide relevant recommendations.
Solution
Consolidate your tech stack to ensure seamless data flow and eliminate fragmented systems.
Manual processes slow down AI
If processes like inventory updates or pricing are still done manually, AI can’t optimize them.
Solution
Automate routine tasks like forecasting and pricing before leveraging AI to optimize decision-making.
Vague strategies
AI without a clear, defined goal often leads to wasted efforts.
Solution
Align AI initiatives with specific business goals (e.g., improving margin, reducing stockouts) to ensure measurable outcomes.
Vague strategies
AI without a clear, defined goal often leads to wasted efforts.
Solution
Align AI initiatives with specific business goals (e.g., improving margin, reducing stockouts) to ensure measurable outcomes.
1. Get your data in shape
Product, pricing, and inventory data need to be accurate, organized, and accessible.
2. Automate the basics
Start with processes that are repetitive and time-consuming. AI won't work until these are running smoothly.
3. Start with high-impact use cases
Focus on areas like forecasting or pricing where small wins lead to big returns.
4. Align teams across departments
AI adoption isn’t just a tech issue. It’s an operational one. Make sure everyone’s moving toward the same goals.
Standardize product, pricing, and inventory
Ensure your data is consistent across regions and categories to create a single source of truth. No duplicates, outdated prices, or missing attributes.
Automate repetitive tasks
Focus on automating processes like digital linesheet creation, order processing, and reordering to save time and avoid manual errors.
Integrate core systems
Connect your ERP, CRM, and inventory management systems to ensure seamless data flow and eliminate silos.
Capture real-time buyer behavior
Collect data on buyer trends and order behavior to build historical data sets that can fuel AI models for forecasting and dynamic pricing.
Surface insights for predictive forecasting and pricing
Leverage your platform’s reporting tools to surface performance insights and trends that will guide AI-driven decisions in the future.
By setting up these AI-ready workflows now, you’re not adding complexity—you’re simplifying and future-proofing your operations so when you're ready to integrate AI, your foundation will support it.
But none of that happens without the right systems in place today.
Bottom line
AI is not a silver bullet. It's a multiplier. It helps good systems run better and strong teams move faster. If you want to see the real benefits of AI, focus first on the foundation. That's what sets the leaders apart.