Agentic AI: the real competitive advantage for companies and professionals

TL;DR

• Generative AI answers to your requests; agentic AI carries out entire processes autonomously, within the limits you set. It’s like having a collaborator, or a team of collaborators, whom you brief with clear context, rules, and goals.
• Thanks to recent advances and a dramatic drop in costs, today even SMBs and independent professionals can now use AI agents to automate repetitive, low-value tasks, freeing up time and boosting productivity.
• The real competitive advantage lies not in the tool itself, but in rethinking business processes to decide what to delegate to AI and what not to, while always keeping people at the center of decision-making.

For at least three years now, AI has become a big part of the daily lives of people, professionals, and businesses. We all use it for a wide variety of activities, both personal and professional, and we’ve all experienced firsthand the incredible potential of this new technology.

However, there’s something not everyone has grasped yet, and it represents the real turning point, and that is how we use it. In fact, there are two profoundly different ways to put it to work: the generative approach and the agentic approach, and being aware of this distinction is what will make the enormous difference between being at the mercy of a new technology and, instead, harnessing it to enhance human work (never replace it).

A little bit of context first. When we talk about ChatGPT, Claude, Perplexity, and the others, we’re referring to so-called LLMs (Large Language Models): systems trained on enormous amounts of text, capable of understanding language and generating responses, content, analyses, and much more. That’s the part almost all of us are familiar with. But over the past two years, these models have taken a leap forward: they’re no longer limited to responding, now they can also take action. This is where agentic AI comes in as the most advanced frontier and, for businesses, the most interesting one. Let’s see why.

Generative vs. Agentic AI: Differences and Potential

When we use ChatGPT (or other models) by opening the chat, typing a request (the prompt), and conversing with it, we’re using AI in a generative way. What exactly does that mean? We’re using it to generate a specific, predetermined result: a text, an image, a summary, a translation, or an idea. In response to our input, we receive an output; if we want to continue, we must enter another input, and so on.

It’s extremely useful: it saves us time, supports us in our creative work, sparks ideas, and helps us search for and synthesize information. But at its core, it’s a highly advanced chatbot with an inherent limitation: it responds to one request at a time and always waits for our next move. And the “real work” - deciding, piecing together the steps, and repeating the process for various clients - still falls on us. Which is also a good thing, but the risk is that we end up doing even more work than before as AI has added infinite possibilities.

When we talk about agentic AI (or “AI agents”), however, the way things work changes radically. The main differences and characteristics, to the essentials, are: they have access to local files on the computer, they can use external tools and programs, they can write and execute code, and most importantly they chain together multiple actions autonomously to achieve a result.

So, given a context and a goal that we provide, agents carry it out by performing actions, reasoning, and learning autonomously. Do you see the paradigm shift?

It’s not simply “I ask a question, I get an answer, end of story.” It’s this: I have a goal or a task, no matter how complex it may be, that I want to accomplish (an example? I want to automatically fill out the onboarding information for a new client), I teach the AI the context it’s operating in, what the limitations are, and what processes and rules it must follow, and the agent will do the work for me.

In fact, we’re shifting from a reactive approach - where the machine responds to a specific request from us - to a proactive approach - where we teach the machine to perform tasks of varying complexity to achieve our goals.

We’re also talking about automation, but in this case, the difference from the traditional automation we’ve known for years is that while traditional automation is a closed system (it follows rigid rules, always performs the exact same step, and makes no decisions) If something deviates from the script, it freezes. An agent-based system, on the other hand, can orchestrate complex operations, coordinate multiple agents with one another, make decisions within the limits we set, and adapt to unforeseen situations. In a word: it handles the unexpected, something traditional automation cannot do.

The easiest way to understand this? Using agent-based AI is like having a colleague - or an entire team of colleagues - to whom you can delegate tasks, rather than a tool that waits for every instruction you give it.

What are the applications of AI agents for SMEs and professionals?

Once you understand the potential of AI agents, the practical questions are: How do I implement them in my business? For which tasks? Is it worth it? How much does it cost?

Let’s start with some context, because the landscape has shifted this year. Until recently, building automation systems and bringing AI into a business required an investment in both money and expertise that only large companies could afford. Now the situation is completely different: the cost of models has plummeted, and for an investment a small business can afford, you can build what is essentially custom software, with a direct impact on productivity, the quality of the work, and, consequently, revenue.

The examples of what an AI agent can handle are truly endless. Below, we’ll look at just a few examples to give you an idea, but to truly understand what’s best for a business, you need to identify and map out the business processes that are most repetitive, with low added value, and currently take up a lot of precious time that could be better invested. A good starting point is to identify which tasks take up the most time each week for you and your team; from there, you can begin by automating just one process.

Here are some concrete examples of processes that can be automated, drawn directly from our projects and feedback from SMEs professionals:

Lead Qualification: Collecting information on incoming leads, enriching it, and prioritizing them.

• Il senso è chiaro ovunque, ma la lista ha un problema di uniformità: alcuni item sono in Title Case, altri in minuscolo; alcuni finiscono col punto, altri no; e lo stile grammaticale oscilla tra frasi nominali ("Data collection...") e verbi in -ing ("extracting data..."). In una bulleted list conviene che tutti gli item seguano lo stesso schema. Ti do la versione uniformata, poi le note.

Uniformando su label in bold + verbi -ing, niente punto finale (convenzione più comune per le liste brevi):

• Customer onboarding: collecting data, preparing documents, creating folders, and sending welcome emails
• Quotes and admin: reviewing incoming requests, drafting quotes, and reconciling invoices and payments
• Document handling: extracting data from documents and automatically filling in recurring forms and paperwork
• Customer support: answering frequently asked questions from the company's own documentation, and passing only the cases that need judgment to the right person
• Marketing: tracking and reporting on campaigns, adapting content for different channels, and managing campaigns
• Research: gathering information on markets, competitors, and suppliers, and delivering it already summarized

To understand the difference compared to a chatbot, here’s an example. You ask a chatbot about the status of an order, and it tells you. An agent checks the order, notices that the supplier is running late, notifies the customer, and reschedules the delivery, without you having to tell them step by step how to do it.

What It Takes to Make It Work and the Role of Humans

It may seem a bit counterintuitive, but the critical factor for an agentic system to work well, even more than the technology itself, is the clarity of the context and information provided to it (the instructions). You need to be able to map out your company’s processes very clearly. It’s a bit like when you hire a new employee and onboard them: the better you explain things and the more context you provide, the better and faster they’ll learn.

One last, very important disclaimer: the role of humans must remain crucial. Agents don’t replace people; they take over the most repetitive, low-value-added tasks. Control over goals, limits, results, and outputs remains with the person. In fact, for sensitive steps, an agent makes a proposal, and the person approves it. You always decide how much autonomy to give it.

Conclusions

As with any new technology, it’s normal that not everything is clear yet: it all happened so quickly, and it takes a little time to adjust the way we think about work.

But that’s precisely where the opportunity lies. Understanding now the difference between using AI to write a couple more texts and using it to entrust entire processes to it is the real competitive advantage. To get started: identify an initial process - one that’s repetitive, time-consuming, and has clear rules - and start with that.

The cost of running these models has dropped dramatically this year, so at the moment, the cost-effectiveness of implementing AI-powered applications in your company is not in question. All you need to do is figure out how to use it to maximize the benefits and enhance human work, never replace it

⚡ At Akme, what we do is bring innovation to businesses and independent professionals, guiding them through the adoption of this technology with tailor-made solutions. We figure out together what you need, and we implement it turnkey, without endless consulting or big upfront investment.

👉 Want to explore how agentic AI could work for your business, at what cost, and with what concrete benefits? Get in touch: we're always happy to have a no-strings chat.