Can autonomous AI agents be the mid-market's new competitive edge?

Manufacturing and distribution aka the "make and move" industries have been facing mounting competitive pressure for years. Large enterprises wielding substantial resources and nimble niche players using the latest technologies have been chipping away at market share for some time.

Traditional strengths like personal service, regional loyalty and a familiar market presence are no longer enough. Rising costs, shrinking margins and customer expectations around price, sustainability and instant delivery are eroding these advantages.

So, how can mid-sized businesses differentiate? Double down on brand and customer support? Maybe, but the real opportunity lies in harnessing a new generation of technology called autonomous AI agents so mid-market businesses can level the playing field across all facets of operational efficiency.

Mid-market businesses implemented ERP systems years ago to collect data and use analytics tools to find trends. Many run a CRM for customer interactions and rebate automation software to ensure customers are in the right range. These tools have significantly improved efficiency, reduced manual work and delivered better visibility of performance. But using software is not a competitive advantage, it is a basic need.

What’s changing now is how businesses interact with their software. And artificial intelligence (AI) technologies, particularly agentic AI will start to reshape this experience.

LLMs in your software are helpful but limited

Many businesses are experimenting with large-language learning models (LLMs) especially where it’s embedded in familiar software. According to Edge Delta AI adoption research, 80% of companies are using AI in some capacity, with 83% seeing AI use as central to future business strategies.

For example, Phocas Software uses natural language processing to allow users to ask plain-language questions about their data like “What were last month’s sales by region?” and get immediate insights. This helps new users quickly learn analytics by showing the steps to derive results. Other mainstream tools like Canva use LLMs to help create presentations and social posts from text prompts. HubSpot uses AI tools to draft emails and summarize CRM data. These are early examples of AI embedded in software, performing well-defined tasks based on user input. These algorithms rely on prompts, operate within fixed rules and don’t make decisions on their own. They enhance efficiency, but they don’t truly act on behalf of the business.

What an autonomous AI agent can do

As Phocas grows its AI capability, we see the rise of agentic AI giving mid-market companies a fresh competitive advantage. But not the standard AI tools people are experiencing today but rather autonomous AI agents.

Autonomous AI agents are sophisticated software systems that add a level of reasoning to powerful generative AI to independently pursue defined objectives and execute complex tasks without constant human prompting.

These AI agents will make decisions and act autonomously and operate beyond processing information. They will interact with your data environment and take steps to achieve specific goals. Agents can execute an action automatically and can get the job done without human resourcing.

AI agents will soon possess the ability for reasoning and planning, allowing them to analyze situations and devise a plan. Their actions are goal-oriented, focusing on tasks such as sending emails, booking appointments, and data entry until targets are achieved.

A defining characteristic of autonomous AI agents is their ability to utilize a diverse array of tools (the classic test of intelligence) like software applications and APIs to effectively interact with the real world and carry out their assigned tasks. This sophisticated functionality marks a significant departure from traditional automation, which typically adheres to a fixed set of rules and lacks the intelligence to adapt to changing circumstances. Autonomous AI agents represent a substantial leap forward, offering mid-market companies a pathway to handle the challenges inherent in their operations like managing inventory and pricing.

What could this look like in your business?

The integration of autonomous AI agents holds much promise. Software companies like Phocas could eventually add autonomous AI agents to all its BI and FP&A software. There will also be many use cases for mid-market companies to build their own agents within their own data warehouse.

In the realm of logistics that manufacturers and distributors operate in, autonomous AI agents can improve complex supply chain management. They’ll have the capability to optimize transportation routes by meticulously analyzing a multitude of factors including traffic patterns, weather conditions and delivery schedules. Route optimization agents, for example, can significantly minimize both travel time and fuel consumption, directly addressing the ongoing challenge of rising transportation costs. AI agents may also provide better real-time visibility throughout the entire supply chain by continuously monitoring data streams coming from various interconnected systems, effectively tackling the long-standing issue of limited supply chain transparency. Perhaps most crucially, these autonomous agents can proactively predict potential disruptions, such as delays in supplier deliveries or adverse weather events.

The intrinsic complexity of the global supply chain requires a level of dynamic adaptability beyond what is currently being used. Autonomous AI agents offer a new flexibility, and for all the talk and research papers around fixing, greening and changing supply chain– these autonomous AI agents might finally do it.

Autonomous AI for distributors is also well placed to transform inventory management. These agents will be able to forecast future demand with accuracy and then advise another autonomous inventory agent to leverage the predictions by automatically adjusting stock levels across multiple storage locations, to avoid stockouts and overstocking. Autonomous AI agents can also streamline procurement by automating the generation of purchase orders based on pre-defined reorder points and sophisticated demand analysis. Maintaining a delicate balance in inventory levels has long been a persistent challenge for distributors.

In manufacturing, AI agents bring expertise to the critical area of equipment maintenance. By continuously analyzing sensor data in manufacturing machinery, these agents can detect subtle anomalies and accurately predict potential failures before they occur. This proactive capability allows for the scheduling of maintenance interventions before breakdowns happen. AI agents can also optimize maintenance schedules based on actual equipment usage patterns and historical performance data, ensuring that maintenance resources are deployed efficiently and effectively. This predictive approach not only reduces the frequency and severity of disruptions but also contributes to extending the overall lifespan of valuable manufacturing equipment.

A new kind of workforce

The strategic benefits of agentic AI go well beyond simple cost reductions. Mid-market companies can be more responsive by adopting these AI agents that handle routine and repetitive tasks. These agents allow human employees to focus on activities such as innovation and strategic planning which will effectively help them differentiate and solve problems that have plagued them for years such as supply chain efficiency and machine downtime. Mid-market companies won’t just compete on price but on adaptability and creativity.

The dawn of the autonomous AI era for the mid-market is imminent. Organizations that strategically leverage the potential of autonomous AI agents in conjunction with their workforce are positioned to create unique business models. These AI models have the potential to disrupt both large corporations and niche players by striking the right balance of human intervention and tech, ensuring robust data supports the AI agents’ operations.

Read more about Phocas' approach to Agentic AI