Understanding Agentic AI for Your Business

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• Agentic AI is an advanced AI that autonomously plans, decides, and executes complex tasks to achieve goals, learning and adapting over time, going beyond simple content generation.

• Unlike GenAI, which creates content from prompts, agentic AI automates workflows and solves problems proactively.

• Agentic AI boosts autonomy, proactive problem-solving, adaptability, personalization, and process optimization, but raises concerns about control, accountability, and agent sprawl.

Ever feel like there aren't enough hours in the day to chase down every lead or answer every customer question? Imagine having a digital teammate that not only understands your goals but actively works to achieve them, making decisions and adapting on the fly. That's the promise of agentic AI, and it's a game-changer for businesses like yours. You've likely heard a lot about AI lately, especially with tools like ChatGPT making waves, but agentic AI takes things a significant step further. It's not just about generating content; it's about intelligent action and autonomous problem-solving, designed to boost your productivity and free you up for what truly matters.

What Exactly is Agentic AI?

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At its heart, agentic AI is an advanced form of artificial intelligence engineered to plan and execute complex tasks across multiple systems to hit specific objectives. ‍

Think of it as an AI that doesn't just react to your input; it proactively works towards a goal. Unlike more traditional AI that sticks to predefined rules, agentic AI can make decisions, use various tools and APIs, and perform sequences of actions without constant human hand-holding. This makes it a natural fit for automating workflows, keeping an eye on things, and handling multi-step processes. It even gets smarter over time through a process called reinforcement learning, where it learns from trial and error, getting better at its tasks as it goes.

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How Does This Smart Tech Work?

Agentic AI typically follows a four-step problem-solving process. 

1. First, it perceives by gathering and processing data from various sources, such as sensors, databases, and digital interfaces, to understand its environment. 

2. Next, it reasons, using large language models (LLMs) as its orchestrator or reasoning engine, that understands tasks, generates solutions, and coordinates specialized models for specific functions like content creation, visual processing, or recommendation systems. This step often uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs. 

3. Then comes the action phase, where it quickly executes tasks by integrating with external tools and software through application programming interfaces (APIs). 

4. Finally, it learns and continuously improves through a "data flywheel," where the data generated from its interactions is fed back into the system to enhance models, making future performance even better. This continuous learning and adaptation are key to its power and sets it apart from other AI systems.

The Ups and Downs of Agentic AI

Now, you might be wondering about the perks and pitfalls of bringing this kind of AI into your business. 

On the upside: 

Agentic AI is all about autonomy and proactivity. It can operate independently toward a set objective, maintain long-term goals, and even anticipate needs, turning passive tools into proactive teammates. This means it can take on routine, data-heavy tasks, freeing your team to focus on higher-value work. 

It can also:

• Accelerate execution by eliminating delays and enabling parallel processing
• Bring adaptability by continuously ingesting data and adjusting process flows in real-time
• Enable personalization by tailoring interactions and decisions to individual customer profiles or behaviors
• Offer elasticity to expand or contract execution capacity based on workload
• Make operations more resilient by monitoring disruptions and rerouting operations.

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On the downside

However, with great power comes great responsibility, and agentic AI does have its challenges. The primary concern revolves around autonomy control and the potential for it to "go off the rails" if not properly managed. 

Imagine an AI optimizing for speed that accidentally damages products, or one designed to maximize social media engagement that starts spreading misinformation. There's also the risk of "agent sprawl" – the uncontrolled proliferation of redundant, fragmented, and ungoverned agents across teams and functions. Plus, while they aim to reduce human intervention, human oversight is still crucial, especially for high-stakes tasks where precision, judgment, and ethical responsibility are essential.

Did you know…

Agentic AI can potentially lead to a 20% to 60% increase in productivity, and in one case, a bank saw a 30% improvement in credit turnaround speed by reimagining their credit-risk memo process with AI agents.

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Agentic AI in Action

This isn't just theory; agentic AI is already making waves. For example, in the retail industry:

• Walmart uses agentic AI for real-time inventory tracking and automated customer support 
• Levi Strauss employs AI-based demand predictions to balance stock levels 
• Amazon is pioneering autonomous shopping agents that personalize product discovery and automate purchases 
• Ocado has revolutionized warehouse automation with AI-powered robots 
• Sainsbury's has implemented AI for demand forecasting and labor scheduling to maximize efficiency

In the legal field, agentic AI is proving valuable because of its ability to carry out complex tasks quickly and make sense of large datasets. While generative AI can create documents or answer questions, agentic AI plans how to get multi-step work done, including tasks like consuming information, applying logic, crafting arguments, and then completing them. This leaves legal teams more time for nuanced decision-making, creative strategy, and client relationship-building. Specific use cases include document drafting and review, where agentic systems can adapt language based on jurisdiction and review drafts against firm standards. They also support case preparation by identifying patterns in judicial reasoning and suggesting persuasive approaches.

Even within software development, AI agents are boosting developer productivity by automating repetitive coding tasks. 

Did you know…

It's projected that by 2030, AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

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Agentic AI vs. Generative AI

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So, how does agentic AI stack up against its more familiar cousin, generative AI? It's a common point of confusion, but the difference is pretty straightforward. 

Generative AI (GenAI) is a creative powerhouse; it excels at producing specific content like text, images, or code, based on direct, step-by-step prompts. Think of it as the imaginative artist that creates something new when given clear instructions. Tools like ChatGPT, DALL-E, and Midjourney are prime examples. GenAI typically functions in a request-response model, like a chatbot.

Agentic AI, on the other hand, is the doer. Its strength lies in autonomously managing multi-step processes to achieve a larger objective with less human intervention. While generative AI reacts to input and creates output, agentic AI makes decisions and takes action to keep a process moving. Simply put, generative AI is about content creation, while agentic AI specializes in workflow automation and independent problem-solving. They're not mutually exclusive, though; an agentic AI system could very well use generative AI to craft personalized responses or create custom content as part of its larger goal.

Is Agentic AI Within Reach for Your Business?

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You might be thinking, "This sounds like something only big tech companies can afford." But the exciting news is that agentic AI is becoming increasingly accessible for small to medium-sized businesses, especially for consumer-facing companies with inbound funnels. 

Solutions are emerging that mimic sales development staff, handling inbound leads, qualifying them, and answering basic questions. This means powerful compute capabilities that were once exclusive to larger enterprises are now available to all, helping to maximize conversion and save valuable time and resources. Early adopters among large law firms are already leveraging agentic AI to create internal efficiencies and even build client-facing products, positioning themselves ahead of the curve.

Did you know…

In a call center scenario, reimagining the process around agent autonomy could resolve up to 80% of common incidents automatically.

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The Future is Agentic

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Looking ahead, the lines between generative and agentic AI are expected to blur even further, leading to systems that seamlessly blend creation and action. Imagine an AI that not only generates innovative ideas for your business but also implements them autonomously – a true game-changer. This evolution will allow human professionals to save time and concentrate on what they do best – building real-world connections and adding value through in-person interactions, where human judgment and expertise truly shine. While the technology is continuously advancing, it is already mature enough to drive real, transformative change across various industries. For businesses ready to embrace it, agentic AI isn't just an incremental improvement; it's the foundation for a new, highly productive operating model. The time for exploration is ending. The time for transformation is now.

Want to know more about how you could be using agentic AI in your business? Get in touch today or catch up on our webinar <insert link and title when ready> 

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