Agentic AI: What It Is and How Organizations Are Utilizing It
blog/agentic-ai-what-it-is-and-how-organizations-are-utilizing-it
2024-04-09
In our April 18th, 2024 article about the future of Artificial Intelligence, we made three predictions about what AI would look like in 2025.
While all three have held up so far— we were slightly off on one.
Prediction #2 suggested we’d see a rise in autonomous AI systems that could make decisions and carry out tasks independently. That’s happened—but what we’re seeing now goes a step further than “Ambient AI”.
While Ambient AI is meant to seamlessly operate within the background, passively gather data, and perceive its environment to provide assistance, Agentic AI takes it a step further…
What is Agentic AI?
Agentic AI refers to AI systems that exhibit agency—the capacity to make decisions, initiate actions, and pursue goals independently based on their understanding of context and priorities.
In simpler terms: it’s AI that thinks like an agent, not just a tool.
These systems combine:
Goal-setting capabilities
Context awareness
Adaptive learning
Planning and decision-making
Unlike reactive models that wait for prompts, agentic systems anticipate needs, solve problems, and even collaborate with other agents (human or machine) to get things done.
These systems typically combine:
Machine learning (to improve over time)
Natural language processing (to understand human input)
Robotic process automation (RPA) (to execute tasks)
Decision-making algorithms (to evaluate options and outcomes)
5 Examples of How Organizations Are Utilizing Agentic AI
1. AI Agents in Knowledge Roles
Organizations are increasingly deploying agentic AI to manage complex, multi-step tasks that typically fall to analysts, strategists, or project leads.
These agents can draft reports, analyze market trends, summarize research, and even develop client-ready proposals—without being prompted at every step.
Tools like AutoGPT, Claude, and Microsoft Copilot are already pushing past simple chat interfaces toward agents that reason, plan, and execute.
According to McKinsey, generative AI has the potential to automate up to 70% of business activities across knowledge-intensive sectors—and agentic systems are at the center of that shift.
2. Workflow Automation Across Systems
Agentic AI doesn’t just automate tasks—it can orchestrate them by navigating through multiple platforms, cross-referencing documents, updating records, and adjusting workflows based on changing data.
These agents can help eliminate friction between siloed tools and departments.
Instead of hard-coded rules, agentic systems use reasoning and memory to adapt processes in real time.
Research from Accenture notes that companies using AI to integrate workflows across tools see up to 40% improvements in operational efficiency.
3. Product Development and R&D
In research and innovation roles, agentic AI is becoming a powerful accelerator.
These agents help teams generate ideas, simulate test conditions, recommend design improvements, and even flag cost or compliance risks early in the development cycle.
Rather than replacing domain experts, agentic AI amplifies their capacity to explore more possibilities in less time.
A 2024 Deloitte study found that R&D teams using generative and agentic AI models report faster prototyping cycles and a 30%–50% drop in time-to-market for select products.
4. Customer Support and Engagement
Agentic AI is reshaping customer support from reactive to proactive.
Unlike static chatbots, agentic agents can troubleshoot issues, update records, rebook appointments, or escalate tickets across systems—often within the same conversation.
They also adapt their responses based on user sentiment, tone, and urgency.
Gartner predicts that by 2026, 75% of customer service interactions will be handled by AI, with agentic capabilities driving the next wave of seamless, human-like assistance.
5. Executive Assistance and Operations
Emerging agentic tools are now capable of functioning as digital executive assistants.
They don’t just manage calendars—they prioritize communications, monitor deadlines, reschedule meetings with context, and preemptively identify blockers in operations.
This shift toward trusted delegation is still evolving, but early pilots are showing promise for what’s to come.
For example, startups like Adept and Lindy are building AI agents that can handle routine administrative tasks and decision-support functions, freeing leaders to focus on higher-level priorities.
Benefits and Risks of Agentic AI
What Are the Potential Benefits of Agentic AI?
Agentic AI marks a significant leap in how organizations interact with technology. Rather than acting as tools or templates, agentic systems become collaborators. They can:
Work 24/7 without fatigue
Make informed decisions under uncertainty
Learn from experience and adjust course
Proactively flag or address issues
For businesses, this means faster execution, fewer bottlenecks, and entirely new workflows that simply weren’t possible before.
What Are the Potential Risks of Agentic AI?
While powerful, agentic AI raises important questions:
Control & Monitoring: How do you monitor or override an AI that makes its own decisions?
Accountability: If something goes wrong, who’s responsible?
Alignment: How do you ensure the agent’s goals match your organization’s?
That’s why governance, ethical frameworks, and clear boundaries are essential.
Agentic AI should be designed with human-centered oversight—especially as it gains more autonomy.
Key Takeaways for Organizations
Here’s how to start thinking about implementing Agentic AI:
Start small. Use agents for well-defined, high-impact tasks.
Align with business strategy and avoid automating for the sake of automating. Augment your team’s capabilities by using agentic AI to extend what people can do, not just replace tasks.
Prioritize observability. Choose tools that let you see how and why decisions are made.
Invest in training: Not just for AI, but for your people—so they know how to collaborate with it.
