Smaller Software Providers Hold Potential for Higher-Impact Agentic AI Use Cases in the Supply Chain, but Require Hyperscaler Support

Applications of Artificial Intelligence (AI) in supply chain management is largely being driven by the top software players in the market.  Blue Yonder, SAP, and Kinaxis have been making big strides in evolving their existing offerings through the use of AI agents and pushing closer to fully-fledged agentic systems.

Relatively smaller players, often those with a greater degree of specialism in certain areas, appear to be much further behind, and are running into a lot more challenges bringing solutions to market. Development resources are a big factor, but partnerships with major hyperscalers and data management providers is proving to be a differentiating factor in the race to delivering Agentic AI at scale.

The recent partnership between Kinaxis and Databricks represents another major AI-driving partnership in the supply chain market. Other key partnerships include Blue Yonder and O9 Solutions with Snowflake, SAP, and Databricks, and all working with one or multiple hyperscalers including Azure, Amazon Web Services (AWS), and Google Cloud.

A recent ABI Insight, “Agentic AI Heats up the Supply Chain Software Space—Concise Market Messaging Is Imperative to Gain Momentum,” discusses the growing number of agents hitting the market, with Blue Yonder and SAP launching agents that cover areas such as inventory, logistics, and network operations. Each has its specific coverage area, providing targeted assistance, analytics, risk monitoring, and asset coordination. As the number of agents grows, so does the complexity of managing them. Multi-agent interoperability is emerging as a key challenge and opportunity, requiring “agent command centers” to measure, optimize, and harmonize the deployed agents.

However, it’s important to remember that organizations don’t just use a single provider for all of their applications. And it’s often recommended that organizations work with more dedicated, best-in-breed software providers to ensure they’re getting the best out of their operations.

Smaller, more industry- or regional-specific providers are also much closer to industry pain points than many of the larger providers. If such providers can’t access the same level of partnership support from the hyperscalers, the most impactful applications of Agentic AI are likely to be missed. By providing greater accessibility to hyperscaler infrastructure and data management tools, smaller software providers with deeper domain expertise would help deliver greater competition to the market, reducing over-reliance on a few dominant platforms and help deliver specific use cases through more agile development cycles.

One of the biggest sticking points to applying any level of AI within operations is understanding where to start. This is especially pertinent in areas where companies are still in the process of digitalizing or still have manual processes. Partnering with Information Technology (IT) service providers and leveraging testing tools such as the AI Prototype offered by Hitachi can be a good starting point for end users, allowing them to have hands-on experience with Microsoft’s AI solutions, run simulations, and help quantify Return on Investment (ROI). Running simulations within the context of one’s own workflows can help identify where to invest both time and effort.

For end users, identifying the most impactful use cases for AI and not being swayed by what solutions are readily available to deploy is essential for maximum value. For example, agents provided by major software providers that allow you to run major optimization simulations or help analyze global risk are unlikely to add as much value as an AI tool that can retrieve data from customs or transport documents, like those from Cradl AI and Raft AI. Such solutions are a relatively quick win and can remove significant amounts of low-value manual checking processes.

As hyperscalers and data management providers increasingly tap into the middle market, it’s important to be aware of the distinctions between the types of companies that medium-sized companies are working with. Where a large solution provider will often be working with a multinational organization, smaller providers are operating more with Small and Medium Enterprises (SMEs), which are often focused on one particular vertical or section of the supply chain, making the solution requirements much more specific. SMEs also have much lower internal resources and are more price sensitive, which can be an issue for AI-powered solutions that require a lot of cloud computing.