Central to intelligence is decision-making – the simple but ineffable spark of autonomy that allows us to make choices.
Likewise, some of the most recent and important advancements in artificial intelligence have taken place in this realm with the growing influence of Agentic AI. Put simply, AI agents are able to consider options and elect one over the other. This new level of versatility leaves AI-governed tasks freed from the need for human intervention and oversight.
Graphics courtesy of Chetu.Moreover, companies are taking notice. Trade industry publication Supply Chain Brain estimates that, by 2028, a third of enterprise software will feature this type of AI, and it will represent 15 percent of daily workflow choices.
That optimism is also seen at Statista, which has predicted the Agentic AI market value will exceed $47 billion by 2030.
This is clearly a product that has value and the demand is high.
Agentic AI – from rules to revolution
The older style of AI was often rule-dependent. Programmers set up a paradigm, and AI followed the map. However, new AI systems are changing the game. Using its own environment to educate itself, Agentic AI can attain a degree of independence, which is further enhanced by the use of Machine Learning (ML), Natural Language Processing (NLP), Reinforcement Learning, and Large Language Models (LLM). These tools promote the ability to make choices as circumstances demand.
The opportunities
This isn’t just theoretical. It can have an effect in the real world. In the wake of the pandemic, the auto industry was stricken with a shortage of semiconductors. As Supply Chain Management Review noted, 8.2 million fewer vehicles were manufactured as a result. That led to more than $200 billion in lost revenue because of supply chain disruptions. Meanwhile, waves of cancellations unexpectedly morphed into massive demand for cars no one had built.
This is the sort of situation that Agentic AI might be called upon to address. By utilizing previous sales figures and merging them with new information about modern consumer buying trends, a well-informed AI system can synthesize a useful picture of what the automotive market might look like going forward. Automakers could use this analysis to react to a fast-changing situation that might otherwise lead to serious overstocking or disastrous shortages.
In this way, supply chain issues are addressed before they become a problem – and perhaps even before they begin at all because an organization’s AI has already foreseen the demand. The positive effect on productivity and profits is obvious. With the right data, an Agentic AI system is worth its weight in gold – or semiconductors.
AI chatter
Chatbots are another area where Agentic AI could make serious inroads. AI-driven products that can interact with customers are already in play, but NLP represents a substantial change with chatbots that can truly provide the kind of customer service experience that would normally require human intervention. Consumers are happier because they can get more than a one-size-fits-all reply to pressing issues. As chatbots increasingly learn from interactions, they are able to meet goals.
This is the basic idea behind Agentic AI. Whether it is an angry customer or an unexpected change in the market, this technology can learn and analyze data to make autonomous decisions. At the same time, humans work on other crucial tasks.
Graphics courtesy of ChetuSaaS and agentic AI
Humans still need some involvement, of course, and SaaS solutions are an increasing part of that equation with a menu of capabilities that developers can offer to customize Agentic AI solutions that fit the needs of a given company or space in the market.
The SaaS model in an AI environment can provide flexibility for organizations as they navigate which specific services are needed and recruit the proper professionals to make it happen. As well as monitor project performance. That’s a win-win for everyone since clients can integrate new AI tools without grinding everything to a shuddering halt while SaaS pros keep the system running and cut costs that might otherwise go to additional IT people needed for monitoring efforts.
More businesses than ever before are finding that Software as a Service is a practical and efficient option. According to Fortune Business Insights, the global $317 billion SaaS market size is predicted to break an eye-popping $1.2 trillion by 2032.
Implementation: Buy, build, or hybrid
When companies decide to implement an AI or agentic AI system, there are three major options: buy, build, or use a hybrid approach. Companies can buy third-party supply chain management systems to reduce some upfront costs and enable faster deployment, but they often come with generic features, incompatibility with existing programs, and ongoing subscriptions. Although custom-built software solutions may come with higher initial investments and longer development times, a vetted software solutions provider will build, for example, an AI chatbot to meet the specific operational needs of the business. With this approach, businesses will have an easily scalable platform, no recurring licensing fees, and full control because they own the intellectual property and code.
To receive the best advantages of building and buying, some companies buy a third-party product and have a trusted software developer customize it to meet their needs.
Businesses need to consider all technological innovations that can keep and make their companies more competitive. It is readily apparent that many organizations are early adopters of agentic AI because they see how it can give them an advantage. The question for others is whether they will grow with technology or fall behind.
