For many B2B enterprises, the idea of chasing full AI autonomy is exciting. The possibility of agentic commerce, in which AI handles autonomous purchases, payments and fulfillments could completely transform how individuals and organizations do business. But even as AI applications become more fully engrained throughout business operations, immediately chasing full autonomy isn’t necessarily the best option.

In fact, research from McKinsey found that while 88% of organizations use AI, only 6% of those organizations could be considered “high performers” that are unlocking meaningful innovation and transformation.

As Yoav Kutner, co-founder and CEO of AI-enabled B2B platform OroCommerce explains, this isn’t necessarily a fault of AI itself. Rather, it’s an indication that B2B eCommerce should focus on mastering practical AI before trying to implement agentic AI.

Understanding the Difference Between Automation and Agentic Systems

Kutner believes one of the problems many B2B eCommerce businesses encounter is a failure to fully understand the differences between the many levels of AI autonomy. “There is a big difference between reactive tools like chatbots that simply respond to prompts and agentic systems that can execute multistep plans without any human oversight,” he explains.

“Eventually, we may reach a point where multi-agent systems and autonomous AI take on larger parts of how organizations run, though likely still with human oversight in place. With automations, AI follows rules set by humans.But with autonomy, the AI is able to make its own decisions within preset parameters.”

These differences are more significant than many organizations realize. While automations always follow the same steps, an autonomous agent has more leeway in determining how it will resolve an issue using context-based probabilistic reasoning. 

Such possibilities are exciting, but in many circumstances, B2B eCommerce brands may not fully benefit from the extra capabilities. The result is overspending for features that aren’t needed or wanted, rather than investing in more practical and budget-friendly solutions. 

Why Practicality Needs to Come First

As Kutner explains, B2B eCommerce brands often don’t need to dive into full autonomy in the first place. “In many B2B use cases, simply using a better optimized automation will deliver most of the value they need,” he says.

“Simple automation is often enough, and cheaper. B2B still runs on human relationships and trust, which makes fully autonomous systems a poor fit for most use cases today. Still, many companies jump to agentic solutions without clear needs, turning straightforward tasks into expensive experiments.”

In a B2B environment driven by relationships rather than basic transactional data, full autonomy is unlikely to deliver immediate value. Despite this, Kutner still sees significant potential for targeted AI applications within the B2B eCommerce space, as long as leaders follow some basic guidelines with their implementations.

“There are specific criteria that B2B eCommerce brands should look for when they begin to implement AI,” Kutner advises. “First, it should have low operational risk for the business as a whole. At the same time, it should have high visibility and measurable KPIs, so all decision-makers can see and assess its impact. Ideally, this initial application should augment your team’s workflow, so the human touch is still present and people can get more comfortable with AI. Areas like purchase order processing are especially good for this, because they have existing data to match to and are a high-volume and repetitive task where AI can offer an immediate benefit.”

By focusing on practical needs rather than making outsized investments in autonomous systems, B2B eCommerce organizations can streamline operations without hindering human oversight. As critical AI-based infrastructure continues to be developed, a baseline of practical use cases can better position an organization for future tech adoptions.

Scaling the Right Systems

Finally, Kutner strongly encourages B2B eCommerce brands to ensure they are prepared to scale the right systems when they implement any level of AI: “AI is a scaling machine governed by the good old ‘Garbage In, Garbage Out’ rule. If you feed it messy data or broken processes, you aren’t just making a mistake, you’re automating, accelerating and scaling it. To get true efficiency out of AI, you have to stop the garbage at the source.”

Reports have estimated that as many as 95% of generative AI projects at companies have failed, with the vast majority of these failures being the result of bad data and systems. Without properly structured data, rules and business logic, even relatively simple AI applications will struggle to provide meaningful assistance that aids B2B operations.

Laying the Foundation for Future eCommerce Autonomy

Each B2B eCommerce organization is likely at different levels of expertise and implementation with AI. And as Kutner’s insights reveal, this means that it isn’t necessarily in every organization’s best interest to begin chasing full autonomy right away. 

Organizations must first establish efficient systems for practical AI applications that still have human oversight before transitioning to fully agentic AI. By optimizing existing systems and data, B2B eCommerce businesses can begin implementing AI in a way that helps them scale and solve problems. This ensures that organizations have the right foundation to successfully implement AI, both now and in the future.