Agentic AI is quickly becoming a major focus for organisations. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI,
But what is Agentic AI, exactly? Agentic AI refers to artificial intelligence systems designed to act as autonomous agents. These systems proactively plan, make decisions, and take actions based on high-level goals. They operate in dynamic and often complex environments without needing constant instruction.
This kind of AI is starting to take over those routine, often tricky decisions that bog down teams. If you’re curious about what Agentic AI means for your business and how to get ahead of the curve keep reading.
According to Gartner, by 2028, “at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from zero percent in 2024”. But the real driver isn't the technology itself - it's the mounting pressure on businesses to do more with the same resources. Deloitte highlights that skilled technology talent is becoming increasingly difficult to attract and retain, especially as demand outpaces supply across nearly every industry.
Customer expectations for speed and responsiveness continue to rise. Operational complexity keeps growing as businesses expand across channels, markets and service offerings.
Traditional automation helped with some of this, but it hit a wall. Rule-based systems works well for predictable processes, but breaks down when faced with exceptions, changing conditions, or decisions that require judgement calls. That's where most of the remaining operational bottlenecks live - in the grey areas that need human thinking but don't necessarily need human creativity.
Agentic AI promises to fill that gap. It scales where humans cannot, adapts to business-specific needs and improves over time to make better decisions. Early adopters will build maturity while others are still figuring out where to start. Those who wait will find themselves playing catch-up in markets where autonomous systems have become standard practice.
If you're a CTO, IT Director, or Chief Data Officer, the key isn't to solve everything at once - start with a single use case where you can control the variables and understand how these systems behave in your environment. The main question becomes: where do you have clean data, clear success metrics and tolerance for some early experimentation?
For COOs and operational leaders, Agentic AI promises to addresses a familiar pain point. You're dealing with the daily reality of exceptions, escalations and situations that require constant human attention. Teams often struggle to stay ahead of operational demands that grow faster than headcount. Agentic AI won't solve every problem, but it can handle the monitoring and routine decision-making that currently eats up your team's time. Consider this: what decisions do your people make that follow recognisable patterns, even if those patterns aren't written down anywhere?
Senior executives and board members need to view this strategically. Organisations that successfully adopt new technologies often gain significant competitive advantages. However, rushing into AI initiatives without proper consideration of risks and resource requirements can be costly. The question isn't whether to explore Agentic AI, but how to approach it in a way that builds organisational capability whilst managing potential downsides.
For finance and procurement teams, the business case matters more than the underlying technology. You'll want clear ROI projections, but be cautious of promises around immediate payback. Often, the real value comes from secondary effects - improved customer satisfaction, faster response times, reduced errors - that are harder to measure upfront but ultimately more valuable than obvious cost savings.
The common thread across all these roles? The organisations that will succeed with Agentic AI start with a clear understanding of the problem they're solving, not the technology they want to implement.
One of many possible applications of agentic AI, is the self-managing merchandising agent supporting the Retail sector. It acts like a real-time merchandising strategist, constantly monitoring live sales data, inventory levels and customer demand across multiple channels. It connects to e-commerce platforms, inventory management systems and marketing tools to understand what’s selling, what’s stagnating, and what needs attention.
Based on that context, it makes and executes decisions: adjusting product listings, changing prices, launching timely promotions, or triggering restock orders - all without manual intervention. When a product begins to underperform, it might discount it. If inventory drops too fast, it adjusts supply chain notifications. If a competitor launches a new campaign, it recalibrates to stay competitive. It doesn’t just follow a script, it acts in the moment, driven by goals like increasing sell-through or clearing stock before a seasonal shift.
Like all agentic AI systems, this use case exhibits a set of core attributes that define its intelligence and autonomy in action.
By operating continuously and adaptively, this type of AI agent removes the need for constant human monitoring. It reduces missed sales opportunities, shortens response times and allows retail teams to focus on creative and strategic planning rather than daily tactical decisions.
While agentic AI offers many opportunities, it also introduces some challenges.
These systems don’t just follow instructions — they analyse your organisational data (if appropriate), form plans and act on them autonomously. That's why the quality of your data, the robustness of decision logic and the clarity of expected outcomes determine whether these agents perform well or fail. Poor data or undefined objectives can lead to misguided actions at scale.
Moreover, building agentic AI isn’t plug-and-play. These solutions require orchestration of multiple technologies, including LLMs, APIs, data pipelines, planning engines, and feedback loops. This demands skills that many organisations don’t yet have in-house.
Security and control are also growing concerns. Agentic AI systems will require appropriate cyber-defence strategies, including protection against adversarial inputs, data poisoning and autonomous AI-driven attacks. Prompt injection, jailbreaks and AI-led exploits are already being seen in the wild.
And as these systems grow in complexity and independence, so too do the governance and oversight requirements. Organisations will need to rethink their AI risk frameworks, with stronger orchestration tools and enforceable policy layers to ensure safety, compliance and explainability.
Finally, there's the human element: employees may feel threatened by the idea of AI making decisions or taking control. Change management, transparency and clear communication will be vital to building trust and driving adoption.
The shift to agentic AI isn't just about getting more work done faster, even if that seems like the headline. It’s more about changing and evolving how you think about operational design and human-AI collaboration.
Agentic AI isn't just another tool - it's a new way of thinking about how work gets done. The organisations that succeed will be those that start experimenting now, whilst the technology is still emerging and competitive pressures are manageable.
Ready to explore where agentic AI could work in your organisation?
Start by mapping three operational decisions your teams make repeatedly. Ask yourself: could an intelligent system handle these with 80% accuracy? If the answer is yes, you've found your first pilot opportunity.
At NashTech, we've been helping organisations navigate complex technology transformations for over 25 years. Our approach combines deep technical expertise with business analysis and human-centred design - because successful AI implementations aren't just about the technology, they're about how people and systems work together.
Want to discuss what agentic AI could mean for your specific operational challenges?
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