Agentic AI Is Transforming Enterprises—Here's Where To Start
Artificial Intelligence
Today, artificial intelligence (AI) is greatly affecting enterprises and how they do business. It has helped automate tasks, optimize processes, and increase employee productivity – covering everything from content creation to research and education to writing code itself.
However, as AI technology advances, and new generations emerge, it is up to enterprises to embrace these changes. Agentic AI is one subset that will undoubtedly have a significant impact. Organizations that do not embrace it run the risk of being left behind.
Agentic AI's Impact on Enterprise Organizations
Generative AI and Agentic AI each have roles within organizations but also work together to help businesses optimize and achieve their goals. While generative AI uses existing data to create new content such as creating product guides or code, Agentic AI builds on this by making choices and executing the tasks autonomously, such as deploying the code to the right parts of the software infrastructure.
According to a recent Prosper Insights & Analytics survey, over 20% of employees, over 30% of business owners, and over 40% of executives know about and are using generative AI. Additionally, by 2028, Gartner anticipates that one-third of enterprise software applications will have embedded Agentic AI. As these technologies develop, enterprises have many opportunities to achieve their business goals.
Prosper - Heard of Genetrative AI
Nitesh Bansal, CEO of product engineering and digital services company, R Systems, believes 'Agentic AI is going to be the next game-changer, and has the potential to transform industries with its increasing ability to enhance efficiency, reduce costs, and improve experiences.' He also notes that 'enterprises that can safely and effectively deploy Agentic AI, while also ensuring that its participation in business processes is robust and accurate, will be the ones to leapfrog competition.'
Adopting Agentic AI in the Enterprise
When starting on the Agentic AI adoption journey, organizations can take three crucial steps to ensure their proper and ethical deployment.
First and foremost, the enterprise should lay out the objectives for the Agentic AI implementation – there should be a clear view of the OKR it would drive.
Then, the company must evaluate the data readiness of the organization to sufficiently feed the AI model for training and ongoing execution. When looking at the data to be used, ensuring it is high-quality is essential. An important step to this is establishing data identification and curation principles that prevent any biases, as well as understanding the data lineage to know whether the data is generated within the organization or sourced from trusted sources.
According to a recent Prosper Insights & Analytics survey, 40% of executives believe AI needs human oversight, and 30% feel that it needs more disclosure/transparency on the data it uses. Building trust in the Agentic AI models requires organizations to implement more data governance, transparency, and accountability measures to enhance the accuracy, reliability, and organizational trust of insights generated by Agentic AI systems.
Prosper - Concern About Recent Developments in Artificial Intelligence
Bansal highlighted that AI can quickly become resource intensive, so planning for infrastructure requirements with careful projections of growth in consumption and costs can help prevent the business case from failing.
Once data and infrastructure capabilities have been evaluated and addressed, Bansal noted the importance of initiating small-scale pilot programs to test Agentic AI applications before full deployment. This allows organizations to identify issues and refine their strategies based on real-world feedback without a significant investment of resources.
Lastly, Bansal shared that continuous training and development are needed for successful adoption. 'There is currently a significant gap in the skills and knowledge needed for enterprises to successfully leverage Agentic AI,' said Bansal. 'Enterprises must invest in their workforces in order to bridge this gap. They can do so by offering employees tools and learning opportunities that not only enhance their AI skills but allow them to completely embrace innovation.'
R Systems has implemented comprehensive AI literacy programs across different organizational levels to equip employees with the knowledge and skills needed to thrive in an AI-driven landscape. The programs range from foundational AI awareness programs to role-specific AI advanced training programs. The company also offers upskilling or reskilling programs tailored to specific role requirements and business needs and actively sponsors industry-recognized certifications in AI, cloud, Microsoft, and others.
The Opportunities for Agentic AI In Enterprises
Organizations are growing curious to explore Agentic AI across all industries.
Customer service, in particular, is one area where Agentic AI can improve how organizations interact with their customers. Agentic AI is able to learn from different data sources and subsequently provide context-aware responses to the customer and can also act on behalf of the customer service agent, fulfilling tasks such as updating CRM or ERP systems. Real-life deployments have already shown great payoff with AI agents in contact centers cutting cost-per-call by 50% while improving customer satisfaction.
Using Agentic AI, R Systems has helped organizations achieve cost-saving measures, particularly for mundane, routine tasks where operations can be streamlined, and decisions can be made faster. For example, an EdTech company wanted to have an easier way to summarize lengthy videos to curate engaging content on topics being discussed in the video. With Agentic AI, users can directly browse through the topics of their interest over the length of the video, saving up to 70% of their time spent.
However, Bansal still cautions enterprises.
He believes that emphasizing data management and training more deeply, along with developing rules within which the agent gets access to data, will foster greater trust in Agentic AI and improve control over the information available to the agent.
Bansal also believes enterprises that rush ahead without a holistic plan may face 'Agentic AI debt' – poorly integrated, unmanaged AI agents that become a burden rather than a benefit. They should avoid the temptation to deploy autonomous agents without careful planning.
Balancing Trust Between Humans and Machines
According to a recent Prosper Insights & Analytics survey, over 85% of executives have expressed some level of concern about their privacy being violated by AI using their data. This also rings true for business owners and employees, and it is valid.
Prosper - Concern About Privacy From AI
While much work has been done, there is still more to do to ensure that all data used in Agentic AI is ethical and unbiased. As organizations build their technology infrastructures to incorporate ethical Agentic AI, due diligence on the data vendors and applications they are using must be top of mind, focusing on data management and maintenance, transparency, fairness, and accountability to mitigate any potential biases. Governance and ethical oversight must be built into agentic AI initiatives from the start.
'Organizations must encourage and prepare their teams to embrace the new workflows that will come with Agentic AI's adoption,' said Bansal. 'Getting the organization on board and promoting the shift into this mindset can be challenging, especially for those companies and teams that are more traditional in their way of thinking, but when approached the right way, the technology can be revolutionary.'
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