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Agentic AI refers to AI systems that can act independently, make decisions, and perform tasks without constant human control. Think of it like a digital assistant that doesn’t just follow instructions but also figures out the best way to solve a problem based on the goals it has been given.

Agentic AI is getting a lot of attention because of its promise and ability to deliver business outcomes, not just automate tasks. For example, if you are a marketer – in the past, you may use AI to help you draft an email or write a blog, but with agentic AI – you may simply be able to ask it to run an email campaign to generate leads. The task of understanding your products, identifying targets, researching targets, drafting personalized emails, sending a campaign, and analyzing results will all be done by a Marketing AI Agent (at some point, you may even decide to hand over the lead to your AI Sales Agent☺)

So, an Agentic AI is an AI system with the following four characteristics.

1. Autonomous – it can do a full range of work without needing step-by-step guidance (though it can still be supervised by humans as needed)
2. Outcome Oriented – it can take a goal, deconstruct it into specific sub-tasks and manage the work to deliver the result 
3. Continuously learning and adapting: It can analyze the outcomes of past activity and perceive context to improve itself 
4. Collaborating: It can partner with other AI systems or people to deliver the target outcome

How is it different from other common types of AI?

If you have been following the AI space, it is hard to imagine that ChatGPT burst on the scene only in December 2022. This short amount of time has seen an astonishing pace of AI development, which is important to compare for better understanding.

How can agentic AI transform the top 10 enterprise functions adopting Agentic AI?

In concept, Agentic AI can be applied to any enterprise function. We are already seeing many AI agents across numerous functions and industries. Generally speaking, areas that make good candidates for Agentic AI are ones that are relatively low risk and have mature commercial Agentic AI solutions. Here is a quick summary of the top 10 possible enterprise use cases that will likely adopt the technology in the coming years.

• Customer Support Automation: Intelligent AI agents in customer service can autonomously handle customer requests, troubleshoot problems, and offer personalized recommendations through chatbots or virtual assistants.
• IT Service Management (ITSM): Specialized AI Agents for ITSM handle most Level 1 support requests and incidents, including troubleshooting issues, user provisioning or software installs, problem management and intelligent human escalation and routing. 
• Marketing Automation: Intelligent AI agents can analyze customer data to create personalized marketing campaigns, recommend products, and optimize content delivery.
• Sales Automation: Intelligent agents can manage the sales pipeline by automating lead generation, qualification, and follow-up processes. 
• Automated IT Operations (AIOps): Agentic AI autonomously monitors IT systems, detects anomalies, and resolves incidents without human intervention.
• Human Resources (HR): Agentic AI automates recruitment processes, employee onboarding, and performance evaluations. It helps HR teams by screening resumes, scheduling interviews, and predicting employee retention risks.
• Business Process Automation (BPM): Agentic AI can handle complex workflows across finance, HR, and supply chains by automating decision-making tasks such as approvals, risk assessments, and resource optimization. 
• Procurement Automation: From generating purchase orders to invoice reconciliation, Agentic AI automates routine procurement tasks, reducing errors and speeding up transactions.
• Cybersecurity: Agentic AI can autonomously detect and respond to cybersecurity threats by monitoring networks, identifying vulnerabilities, and deploying countermeasures.
• Fraud Detection and Prevention: Agentic AI can autonomously analyze transaction patterns, detect anomalies, flag suspicious activities and take preventive action in real-time.

Harnessing swarm Intelligence across multiple enterprise AI agents

We as employees coordinate across multiple functions in a company. Whether it's marketing working with sales to grow revenue or HR working with IT to onboard a new employee – we cannot imagine work being done without such collaboration. With the rise of Agentic AI, it is only natural to assume that these autonomous AI agents will collaborate to handle complex tasks. This may be applicable even within a single function like Marketing with sub-specialized AI Agents. Here is an example -

What roles will humans play alongside Agentic AI?

Humans have an integral role to play alongside their new AI Teammates. This includes

• Supervision and Verification: Periodic review and audit of the work by AI agents to ensure it complies with the company’s standards and policies.
• Approvals: For high-risk or high-value transactions, such as healthcare-related or money–movement transactions, the final approvals should be human to eliminate any risk.
• Escalations: Humans may be better suited to handle complex one-off issues or difficult situations, such as an irate customer. 
• Performance Reviews: Humans should review the bot's KPIs to ensure they are meeting business needs and, when necessary, look for alternatives.
• Hiring: As Agentic Ai expands to new business functions, look for opportunities to expand and use the technology.

What are some best practices and potential challenges to be aware of?

Agentic AI is a relatively new technology, and building some muscles before expanding is important. This should include

• Carefully pick areas for the initial Agentic AI pilot – Areas like ITSM provide a low-risk, high-payoff environment
• Start with a possible value/ROI assessment – analyze historical data to develop possible ROI of Agentic AI. An example of this is Quick Value Assessment being done by rezolve.ai - [link to QVA]
• Select Agentic AI platform products: While it may be tempting to start with generic AI platforms, it is almost always easier to start with domain and function-specific platforms that can provide faster outcomes.
• Set goals and measure progress: Define critical KPI to measure Agentic AI – for example, % of L1 Tickets automated in ITSM, and monitor them ongoing to ensure success.

Closing Note

Agentic AI offers a significant shift in how work gets done in enterprises. It can potentially increase efficiency and productivity by significant orders of magnitude. Developing a systematic approach to harnessing this exciting capability should be on everyone’s roadmap.