There’s a reason why today AI is all you hear about. We’ve experienced more AI innovation in the last 18 months than ever before. AI has exited the lab overnight and turned into a viable business driver.
One industry that stands to win big is B2B eCommerce. In fact, B2B eCommerce could use the technological boost to take the industry to the next level. There are a few key reasons for this:
- B2B transactions have many moving parts. They often involve multiple stakeholders, complex product configurations, and customized pricing agreements. It can be downright confusing.
- There’s just way too much data. B2B eCommerce generates an insurmountable amount of data from various sources such as transaction history, customer interactions, and supply chain operations.
- Customers want what they want. B2B buyers increasingly expect personalized experiences similar to those in B2C. Not surprising, and they will only get more demanding.
- Competition gets fiercer by the day. The competitive landscape is becoming increasingly crowded, with companies vying for market share and differentiation. Yes, your customers are likely to be using AI to get ahead already.
- Supply headaches are real. Supply chains are complex, involving multiple suppliers, distributors, and logistics partners. There are so many elements that are outside of your control.
Neither of the above is surprising. But the fact of the matter is that AI is now at our fingertips. Any organization that fails to jump on the bandwagon is essentially leaving money on the table, and poised to eventually lose customers.
Let’s run through where AI could have the most impact on your organization.
Navigating the intricacies of transactions
As I previously mentioned, B2B eCommerce transactions can involve many parties and other elements. AI can tap into all of these signals to analyze data on stakeholders, product configurations, pricing agreements, and more.
This could help organizations gain a better understanding of each buyer’s and each supplier’s unique needs, which in turn facilitates smoother negotiations, optimized pricing terms, and expedited deal closures. The ultimate result? Cost savings, improved supplier relationships, and faster time-to-market for products and services.
Expense management is another area where AI can have an impact. By analyzing historical spending patterns and supplier performance data, AI agents help businesses make informed decisions, reduce procurement cycle times, and achieve greater transparency and compliance in their procurement processes.
Mo’ data, mo’ problems.
Every company wants more data but also complains about the inability to harness it at scale. AI excels at processing and analyzing large volumes of data, turning it into actionable insights. Large language models in particular are excellent at analyzing transaction history, customer interactions, and supply chain operations to identify patterns, trends, and correlations that may not be immediately apparent to human analysts. For instance, it can identify which product combinations are frequently purchased together, which customers are most likely to churn, or which suppliers have the highest on-time delivery rates.
AI can also serve as a ‘connector’, integrating data from multiple sources such as CRM systems, ERP systems, and external data sources, to provide comprehensive insights into customer behavior, market trends, and competitive dynamics. For example, it can analyze sales performance across different regions, identify emerging market trends, and predict future demand for products or services.
AI agents can make your customers happier.
One of the biggest goldmines for companies is customer conversations. Customer service agents interface with customers on all levels, as they field reviews, complaints, and issues. Customer conversations can even yield insights that could help with product development.
Yet, most companies barely scratch the surface.
The beauty of customer interactions is that they are based on language. AI agents are powered by large language models that not only have the ability to process information at great speeds and volume, but also to respond—i.e., handle orders, resolve queries, provide personalized recommendations, and more.
AI Agents are available around the clock, ensuring customer needs are met promptly and efficiently. This can boost customer satisfaction and free up human resources to focus on more complex, value-adding tasks.
The supply chain conundrum.
It’s no secret that supply chains are intricate (and delicate). AI-driven supply chain optimization tools can improve various aspects, such as inventory management, logistics, and procurement. For example, Oracle Supply Chain Management Cloud utilizes AI algorithms to optimize inventory levels and reduce stockouts while minimizing carrying costs and stockouts by analyzing historical sales data, demand forecasts, and market trends.
Additionally, UPS’s AI-powered logistics optimization platform, ORION (On-Road Integrated Optimization and Navigation), leverages AI algorithms to optimize delivery routes and schedules. By analyzing data on package volume, delivery locations, and traffic patterns, ORION calculates the most efficient routes for UPS drivers, reducing fuel consumption, vehicle wear and tear, and delivery times.
IBM’s Watson Supply Chain is another good example, which applies AI-driven analytics to streamline procurement processes and improve supplier performance. By analyzing data on supplier quality, lead times, and pricing trends, Watson Supply Chain identifies opportunities to consolidate suppliers, negotiate better pricing terms, and mitigate supply chain risks.
Robotic process automation has risen as one of the most interesting areas for companies, with 60% of manufacturing executives polled by Sikich LLC mentioning it as their main area of interest, with machine learning for demand forecasting and predictive analytics also getting some mentions.
This rise in interest is where commerce platforms are needed to act quickly, fulfill this need, and initiate beta testing. Our AI-integrated Data Pipeline saw that manufacturers and other B2B businesses required simplified data consolidation, cutting custom infrastructure costs, which can eat away at their bottom line. B2B businesses wanted an experience similar to a food delivery app where they can easily select relevant datasets, specify retrieval frequency, and destination. This helps them align commerce data with internal sales targets efficiently.
Don’t rest on your laurels.
I just went through some of the ways in which AI agents can improve efficiency, so I’ll spare you the repetition. What I will say is: act now. If you’re not already using AI in some way, be warned that your competitors are.
It’s never been easier and more accessible to tap into model APIs and build your own system. If you don’t want to build, you can buy and experiment, as long as you reap the benefits. Just don’t wait too long.
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