Most AI still waits to be told what to do. It generates content, answers questions and follows instructions—but it doesn’t act on its own. That’s starting to change.
Agentic AI is emerging as the next major shift in business automation. Unlike traditional systems that react to human input, agentic AI systems can take initiative. They plan, decide and act independently to achieve goals. These are not just tools—they’re virtual colleagues capable of executing multi-step workflows without needing constant direction.
The change is already underway. Microsoft, Salesforce and OpenAI are embedding agentic AI into enterprise platforms, and Gartner predicts that “by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI.” (BankInfoSecurity)
The business implications are profound. From faster onboarding and decision cycles to cost savings in the millions, agentic AI is giving mid-sized companies new ways to scale, streamline operations and adapt in real time.
In this article, we’ll break down what agentic AI actually is, how it differs from generative AI, where it’s already being used and what your business can do to prepare.
Most AI systems today are reactive. They follow prompts, execute predefined rules or respond when asked. Agentic AI changes that model. These systems are designed to operate with autonomy—identifying what needs to be done, deciding how to do it and taking action without being told at every step.
At its core, agentic AI refers to software agents with built-in decision-making capabilities. They’re not waiting for instructions. They pursue goals. They can assess a situation, plan next steps and take initiative to deliver an outcome—whether that’s completing a process, solving a problem or optimising a workflow.
This is what separates agentic AI from traditional automation or even advanced generative AI. Rule-based automation follows a set path. Generative AI creates content when prompted. Agentic AI combines input processing, reasoning and action. It can handle open-ended tasks, adjust to changes in real time and execute across multiple systems—without pausing for human input.
To illustrate the difference, imagine a typical customer data issue. In most businesses, spotting duplicate records might require someone to log a support ticket, wait for review, manually compare entries and then merge them. With agentic AI, that process is compressed. The AI identifies the duplicates, evaluates the accuracy of the entries, performs the merge and logs the result—all on its own.
As Arun Parameswaran, Managing Director at Salesforce India, notes: “A task like merging customer accounts, which traditionally required ticket creation and days of manual effort, can now be completed in seconds with agentic AI.” (BankInfoSecurity)
What makes this possible is the agent’s ability to reason through steps rather than just execute a static script. It decides how to reach the end goal based on the data, context and tools available—then acts. For mid-market businesses, this means AI can take on more complex, cross-functional tasks that were previously out of reach for automation.
It’s important to be clear: agentic AI isn’t a magic button. It needs clean data, clearly defined goals and careful oversight. But with the right conditions, these systems offer something new—AI that doesn’t just support the work, but actively moves it forward.
The terms "agentic AI" and "generative AI" are often used together, but they serve very different functions—and understanding the difference matters when you're evaluating what these tools can do for your business.
Generative AI, like GPT-4 or image-generation tools, is built to produce content in response to prompts. It’s reactive. It generates an answer, a design or a summary—but only after being asked. It doesn't act on its own, and it doesn't carry out tasks beyond that output. These systems have become widely used in business for drafting emails, creating reports and automating customer responses. But their role ends at creation—they don’t follow through.
Agentic AI, by contrast, is built for execution. It still uses generative capabilities in some cases—for example, to write an email or interpret an instruction—but it goes further by deciding when to act, how to act and what action to take next. It plans, adapts and delivers outcomes without needing a human to prompt every step.
This distinction isn’t just academic. It shapes what these systems can do inside an organisation.
Consider customer service. A generative AI might draft a response to a complaint once it’s been escalated. An agentic AI would monitor inbound messages, triage the urgency, prioritise the case, draft a resolution, initiate a refund if needed and log the activity in your CRM—automatically.
A TELUS Digital report puts it clearly: “Generative AI mimics patterns learned from its training data. While powerful, it can’t autonomously complete complex tasks, which is exactly what agentic AI can do.” (TELUS Digital)
In practical terms:
The two aren’t mutually exclusive. In fact, many agentic systems embed generative AI as a component. But the leap in capability comes from autonomy—not just intelligence. For businesses, that autonomy opens the door to deeper automation, smarter decision-making and entirely new workflows that go well beyond content creation.
Agentic AI isn’t just theoretical—it’s already being used to solve real business problems. Major platforms like Microsoft, Salesforce and OpenAI are leading the way, embedding AI agents into everyday tools and enterprise systems.
One of the clearest examples comes from Microsoft. In 2024, it partnered with Pets at Home to build an agentic AI for its profit protection team. The agent compiles case data for review—a task that once consumed hours of manual admin. Early testing suggested the tool could deliver seven-figure annual savings by freeing up skilled staff for higher-value work. (Microsoft)
Salesforce is seeing similar gains. At Wiley, a global research and learning firm, agentic AI has reduced onboarding time for seasonal staff by 50%, while boosting customer service resolution rates by over 40%. These agents don’t just assist—they complete tasks across systems, helping businesses scale without expanding headcount. (BankInfoSecurity)
OpenAI is enabling startups to build agents that use web interfaces, fill out forms, update records and complete onboarding journeys that legacy automation struggled to handle. In one case, a nonprofit automated its user enrollment process using agentic AI—cutting the rollout from months to days. (OpenAI)
These use cases span industries—retail, legal, finance, healthcare—and all point to the same trend: agentic AI is being used not to replace people, but to remove the friction that slows them down. It handles time-consuming processes, connects disconnected systems and acts with speed and consistency.
For mid-market firms, that’s the real opportunity. These tools can be applied to workflows like invoice processing, client onboarding, ticket triage and policy enforcement—areas that drain time and rarely add strategic value. With agentic AI, they can be automated safely, and at scale.
Agentic AI isn’t just another tech upgrade. For many businesses, it represents a way to do more with less—cutting manual work, speeding up decisions and reducing operational drag.
Unlike traditional automation, which handles repetitive tasks within fixed systems, agentic AI works across departments, tools and workflows. It removes friction by making decisions in real time, based on live inputs. That translates directly into reduced delays, faster turnaround and fewer handoffs.
In practical terms, this can mean:
This kind of autonomy is especially valuable for SMEs and mid-sized firms that don’t have large teams to manage every moving part. With agentic AI, they can extend their capacity without adding overhead.
The gains aren’t just theoretical. A recent Salesforce survey found that 91% of small and medium businesses using AI reported increased revenue—largely by improving speed and freeing up staff to focus on more valuable work. (Salesforce)
As Microsoft notes, “Autonomous agents allow businesses to scale operations without increasing staff. Built-in AI capabilities enable fast decision-making and process automation.” (365mechanix)
In a time where efficiency and agility are more important than ever, agentic AI offers something most systems don’t: the ability to move fast and act independently.
As powerful as agentic AI is, handing over decision-making to autonomous systems introduces new challenges. These aren’t reasons to avoid adoption—but they are reasons to proceed with care.
The first concern is control. Agentic systems make decisions without waiting for approval. That can introduce unpredictability, especially if goals are unclear or data is incomplete. If an AI agent misunderstands its task or acts outside its intended scope, the consequences could be significant. As IBM Fellow Kush Varshney puts it, “Because AI agents can act without your supervision, there are a lot of additional trust issues.” (IBM)
Security is another issue. Agentic AI increases the attack surface. If an agent has permission to access multiple systems and make changes, a breach or misconfiguration could have broader impact. Gartner has warned that “by 2028, 25% of enterprise breaches will be linked to AI agent abuse.” (The Economic Times)
Transparency is also a challenge. In regulated sectors, businesses need to explain how decisions are made—especially when those decisions affect customers. Many AI agents today still struggle to explain their reasoning in human terms, which complicates compliance and erodes trust.
Alignment matters. Agentic systems will optimise for whatever goals they’re given—but those goals need to be carefully defined. An agent told to reduce costs, for example, might cut corners unless clear boundaries are in place. That’s why governance is critical. As Salesforce’s Deepak Pargaonkar notes, “These systems must be implemented securely, with robust governance frameworks to mitigate potential risks.” (BankInfoSecurity)
For businesses exploring agentic AI, the key is to balance autonomy with oversight. Start with lower-risk use cases, put clear rules in place, and monitor performance closely. With the right controls, agentic AI can be both safe and effective.
Agentic AI is already being piloted across industries—but for many mid-market businesses, the question is where to begin.
The good news is you don’t need to overhaul your entire operation to see value. Most organisations can start with contained, low-risk use cases that free up time and reduce manual work.
Step one is preparation. These systems rely on clean data and clear goals. If your internal processes are fragmented or poorly documented, an AI agent won’t know what success looks like. Take time to map your workflows, standardise inputs and define the outcomes you want to achieve. As TELUS Digital advises, “Focus on putting strong data governance and cybersecurity policies in place.” (TELUS Digital)
Step two is selecting the right task. Start small. Don’t hand over strategic decisions on day one. Instead, look at processes that are repeatable, rules-based and operational—things like ticket triage, document routing or system monitoring. These are ideal for early experimentation.
Step three is control. Autonomy doesn’t mean letting go of oversight. Build in limits, approvals and human checkpoints. For example, allow an agent to process refunds up to a set value, but flag anything above that for review. Always monitor actions and keep an audit trail.
Platforms like Microsoft Copilot, Salesforce Agentforce and OpenAI’s agent tools now offer no-code interfaces that make it easier to build and test these capabilities without deep technical expertise. That makes agentic AI accessible to smaller teams—provided they start with a clear plan.
For businesses that want to stay competitive, the priority isn’t to automate everything. It’s to start learning what agentic AI can do—and what it needs in place to succeed.
Agentic AI isn’t coming—it’s already here. The shift from reactive tools to autonomous systems is happening faster than many businesses realise, and those who delay risk being left behind.
Early adopters are already seeing results: faster workflows, lower operational costs and better use of limited resources. They’re using AI not just to support their teams, but to extend their capabilities in ways that weren’t possible before.
This isn’t about replacing people. It’s about removing friction. Giving skilled employees more time to focus on what matters. Building systems that respond in real time, instead of waiting for approval or instruction. For mid-sized firms, that agility could be the difference between scaling effectively or getting stuck in manual processes that no longer keep pace.
As Salesforce EVP Kris Billmaier puts it, “Small and medium-sized businesses using AI see real returns across their operations... Those who wait too long to invest risk falling behind.” (Salesforce)
Now is the time to take a closer look at where agentic AI fits into your business. Not everywhere. Not all at once. But in the places where speed, consistency and autonomy matter most. Because this isn’t just a technology shift—it’s a decision-making shift. And it’s already changing how work gets done.