Agentic AI: Shaping Tomorrow’s Business Landscape

AI has become essential for running a successful business. Following the emergence of generative AI (genAI), which has become a cornerstone in modern business operations, about 74% of organisations already started using AI in 2024, with a quarter of their tasks being supported by AI.
Businesses have seen how AI significantly enhanced the efficiency of various AI-driven processes. Some real-world genAI use cases saw a rise in productivity in various industries. For example, Toyota Research Institute saw a reduction in the time-to-design phase and enabled faster creation of innovative vehicle designs. Healthcare institutions, like Mayo Clinic, also gained the ability to provide personalised care recommendations.
Along with the ongoing evolution of AI technologies, another form has surfaced: agentic AI. Similar to genAI, agentic AI employs large language models but operates with greater autonomy. Due to the anticipated efficiencies from this novel AI technology, Gartner predicts that by 2028, 33% of enterprise applications will have agentic AI, reflecting the growing need for an automated and reliable work tool.
Meanwhile, Deloitte expects that at least 25% of organisations will begin with proof of concept for agentic AI, which could grow to 50% in 2027.
What these forecasts tell us is that AI is here to stay and new technologies – such as agentic AI – could offer new opportunities to improve business operations. In this blog entry, let’s delve deeper into agentic AI: What is it and what does it mean for businesses?
From Gen AI to Agentic AI
Agentic AI’s biggest difference with genAI is its capability to perform tasks without human intervention like prompts that we have been familiar with since ChatGPT. Its autonomous capabilities are based not only on gathering databases, but also on understanding user behaviours and patterns. It operates by analysing instructions and planning subsequent steps without user prompts, allowing human workers to focus on more strategic tasks.
Agentic AI combines flexible and traditional programming. For example, in the finance sector, agentic AI can be used for fraud detection by integrating traditional programming rules for known fraud patterns with agentic AI models that learn from new transaction data. This hybrid approach allows the system to detect both established and emerging fraud tactics more effectively.
In customer-facing scenarios, such as through customer support chat, an AI agent can analyse a user’s information and concerns to offer personalised assistance, helping reduce wait times for a live human agent and resolve issues promptly.
Healthcare facilities can use agentic AI to automate essential operations such as updating patient records, optimising billing processes, and managing appointment scheduling. AI-powered chatbots further enhance efficiency by handling patient inquiries and assisting healthcare workers with routine administrative tasks, ensuring seamless operations and improved service delivery.
In the field of education, agentic AI is transforming the student journey by enhancing both administrative efficiency and personalised support. For prospective students, AI-powered recruiters provide round-the-clock, tailored guidance on admissions, financial aid, and academic programmes. Institutions leveraging these systems have reported impressive outcomes, including doubling student engagement and saving administrative teams up to 150 hours per month.
For current students, AI advisors act as continuous support systems, assisting with tasks such as course selection and financial aid renewal. These systems are especially beneficial for first-generation college students, offering proactive support to address challenges early and connecting them with resources to prevent small issues from escalating.
Even after graduation, AI agents can maintain meaningful connections with alumni, facilitating ongoing engagement with their alma mater and creating opportunities for mentorship, giving, and continued learning. This lifetime relationship management represents a fundamental shift from the traditional transactional approach to alumni relations.
Today, agentic AI is being incorporated into AI assistants, SaaS platforms, Internet-of-Things (IoT) devices, and robotics. A number of startups and major tech companies are now actively developing and promoting these systems for their ability to manage complex tasks, adapt to real-time changes, and enhance performance over time through methods like reinforcement learning.
Salesforce in late 2024 announced the implementation of an autonomous AI technology to help them resolve customer cases, qualify leads, and optimise campaigns; Microsoft utilises agentic AI technology to help with data retrieval and analysis; and Google offers AgentSpace to help organisations with information discovery and analysing customer behaviour.

How Businesses Can Prepare for Agentic AI
The precision of agentic AI’s predictive abilities is fundamentally dependent on the company’s data; it is essential to effectively consolidate data in the environment where AI will be implemented. This requires a solid governance framework that prioritises data quality, security, and accessibility.
For instance, a financial services organisation might consolidate data post-acquisition by migrating data from multiple systems into a unified data warehouse — whether it is a move from an on-premises infrastructure to cloud, or cloud to cloud. This approach ensures that all data is accessible, consistent, and of high quality, which is critical for accurate predictive analytics and operational insights.
On top of having a centralised data source, ensuring that this information is up-to-date and free of redundant, outdated, and trivial (ROT) data is equally important. Having outdated data in the digital environment not only compromises decision-making but can also negatively impact automation tasks, like data pulls and analyses, among others. Implementing reliable data management practices helps keep data clean, relevant, and accurate, enabling AI systems to function effectively.
Additionally, businesses must ensure data remains secure while reaping the benefits of automation and increased productivity from AI. Implementing comprehensive data governance solutions, such as by establishing clear data ownership roles and implementing secure access controls, is crucial in this regard. This ensures that only authorised personnel can access sensitive patient information, thereby maintaining data integrity and compliance with regulations.
To maintain the integrity and security of sensitive data, businesses must employ a trusted and reliable data management system. This system should safeguard data, provide robust access controls, and ensure comprehensive monitoring. Efficiency is crucial for productivity, and data owners must be able to configure flexible yet secure permissions.
Organisations need clear visibility into their data activities, including who has access and which applications are utilising the data. Proper monitoring is needed to mitigate risks and ensure data security.
In summary, a strong governance framework that ensures data quality, security, and compliance is crucial to maximising the potential of agentic AI, safeguarding sensitive information, and streamlining business operations.

The Road to Embracing Agentic AI
Any new technology brings both benefits and risks, the same goes for agentic AI. One significant advantage of agentic AI is its ability to automate tasks, whether small or large, with minimal oversight. This automation enhances employee efficiency and accelerates project completion.
However, the deployment of agentic AI is not without risks. Potential issues include biases in decision-making, errors due to lack of human oversight, and the ethical implications of AI autonomy. To harness the benefits while mitigating the risks, organisations must prioritise transparency, develop thoughtful strategies, and foster human-AI collaboration. By doing so, they can unlock the full potential of agentic AI in a responsible and effective manner.
By understanding AI and the evolving trends surrounding it, such as agentic AI, businesses can effectively leverage these technologies to cultivate their full potential to drive innovation, efficiency, and productivity. Embracing these technologies responsibly will ensure they enhance employee performance and contribute to a more dynamic and effective workplace.

Mayella is a Content Marketing Specialist at AvePoint, focusing on digital transformation, AI confidence, and education technology. With a background in content production and communications for B2B and e-commerce, she excels at helping businesses transform their digital workspace into a dynamic and efficient environment.