The use of artificial intelligence continues to evolve. In coming years the automation of business processes will rely more on the use of AI agents that are trained on datasets relevant to the process or a particular business campaign as opposed to larger, more generic datasets. This can only help to improve outcomes, as the AI involved becomes more tailored and specific.
Before we go any further, it’s important to note the difference between generative AI and agentic AI. While generative AI automates routine tasks — say querying data or retrieving and compiling information — agentic AI is more sophisticated. Agentic AI can make its own decisions and learn on the go. It’s like your own personal hive full of worker bees constantly improving honey output.
The best part is that it will start to use the most relevant data. But why is this happening now?
More Data, More Opportunities
In my opinion, there are a few factors that are contributing to this shift. The first and most obvious is that businesses are continuing to generate and bank huge amounts of data specific to certain departments. Consider a company embarking on an advertising campaign. That firm can use its existing AI to collect data related to customer behavior through website visits or social media interactions. The same firm can then use that data to tweak its marketing efforts to better reach target customers. And all of the data related to these processes can be used to inform future ones using agentic AI, as the AI can be trained to make adjustments in real-time.
In order to better automate business processes, AI agents will need the most accurate and up-to-date information though and firms will subsequently need to control data quality before handing it over to the AI agents. This will be one way of ensuring that AI adoption is a success.
Advancements in Computing
The next most obvious factor at play here is that AI tools are also developing. Think of how much AI has developed in just the past five years. Its use has also been spearheaded in other areas, such as healthcare, where AI systems like IBM Watson can analyze patient data and dmake personalized treatment recommendations. The private sector is improving its AI in a similar way, to enhance outcomes and to automate complex decision-making processes. In the case of businesses though, it’s not healthcare data that is being crunched, but data related to specific processes or campaigns.
There have also been big advancements in cloud computing. Think of how much cloud computing platforms like Microsoft Azure and Amazon Web Services have scaled since they launched. This expansion in computing power and storage capacity is providing the foundation for complex AI models. These complex AI models, in the form of agentic AI, are now ready to help businesses automate their processes using the best data that is captured in real time.
Multiple Benefits
Companies that make the decision to train AI agents to use more specific datasets related to their business will see that move pay off for several reasons. There will be multiple benefits.
The first most apparent benefit will be an improvement in cost efficiency. The use of better trained agentic AI will allow companies to trim the amount they spend on more routine tasks. They can then reallocate those financial resources and invest them toward more strategic goals. A reduction in operational costs is therefore forecast once agentic AI is adopted for these purposes. Think of the banking sector, where AI agents can automatically process transactions, or respond to client queries. As the monies once used to support those operational costs are reallocated, banks can invest in higher-value activities, such as improving customer service. Also, in making strategic decisions.
Businesses can also improve their services at scale with these AI-automated processes. Think of how AI-powered chatbots manage customer inquiries during Black Friday sales, for example. There is no limit to the amount of queries these chatbots can handle, and backed with data collected from the same processes they are undertaking, they can improve their performance. Service quality will therefore be improved, while the scope and scale of service will be as well. This is something that would be difficult if not impossible to pull off with only human agents.
Gaining an Edge
Of course, the elephant in the room here is that the shift to using more representative data will allow companies to perform better in the market. As these AI agents process these new datasets in real time, they will also produce better decisions for the companies that use them. Think of how Google Analytics uses AI to analyze customer behavior and campaign performance. Every company is capable of doing the same thing using the same kinds of tools. They can adjust their strategies accordingly, tweak content, fueling future revenue growth.
This will result in a major competitive advantage, and businesses who are quicker to adopt will be able to leapfrog the competition. Those who do not risk falling behind and seeing sales drop. Again Amazon is a great example of this. Amazon uses agentic AI to optimize management of inventory, to personalize its product recommendations and to streamline logistics. Competitors who have not embraced AI to the same effect stand little chance in competing against the firm.
Customers will also react positively, it’s worth noting, as they will be better served and therefore will be more loyal and consistent.
About the Author
Oleg Bondarchuk is a Microsoft Azure Certified Solution Architect Expert with over 18 years of IT experience, including more than nine years of specialized experience in designing and supporting Microsoft Azure environments, also he holds IEEE Senior Member status . He’s proficient in Azure Open AI tools, Azure Ai Studio,Azure DevOps, Bicep, ARM and infrastructure automation using PowerShell, Bash and Python, and has expertise in virtual networks, configuration management systems, and implementing high-availability, business continuity, and disaster recovery strategies.
