The hype around artificial intelligence has only become louder over the last few years, as the technology begins to change how organisations are run, who holds the reins and what strategy looks like.
The emergence of ‘Agentic AI’ has only amplified this noise. Unlike AI, which checks boxes, an agentic AI system connects the dots. It’s capable of understanding broad goals, deciding which tasks to take on, and adjusting the approach as things change.
Imagine a logistics team dealing with unexpected weather disruption. A traditional AI agent could be used to flag delays and notify the dispatcher. But an agentic AI system goes further. It can reroute shipments automatically, inform stakeholders, and simulate alternative delivery plans based on updated forecasts. It thinks through the goal, makes decisions, and adapts along the way.
And with 48% of enterprises adopting agentic capabilities, and another 33% exploring them, agentic AI is beginning to rise to the top of the C-suite agenda. While it is still in the early stages, getting ready is paramount: Gartner estimates that 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024.
But, getting AI agentic ready is about more than just tools. It’s about mindset, systems, and responsibility. Here’s the steps that businesses need to take to be prepared including the right approach to ethics, security, and compliance – and how to build in accountability.
Audit your current AI landscape
Businesses should start by understanding their current tech-stack. It’s best to go back to basics and ensure that key stakeholders understand the difference between AI agents and agentic AI.
AI agents add a layer of autonomy, but it is limited. This technology is capable of assessing a goal, breaking it down into smaller steps, choosing the right tools, and carrying out those steps without constant supervision. By comparison, an agentic AI system does more than just perform tasks. It can treat requests as open-ended challenges, generate ideas based on data, make suggestions. And as it learns more and receives feedback, it can refine its thinking.
Once this difference is understood, businesses should ensure that models and workflows are evolving toward agent-like autonomy, as well as being looking beyond static workflows. This means building systems where AI can make decisions and adapt with minimal human input.
Also think about modernising infrastructure. Businesses can use this moment to review their tech stack and ensure that it can support both traditional AI tools and agentic AI models in a hybrid environment.
Governed data is king
Agentic AI needs clean, complete, and representative data to avoid bias or distortion.
Take, for instance, a retailer who uses AI agents to explore areas where customer service can improve. The AI agent can use customer data to detect patterns of dissatisfaction and analyse sentiment. It could use this data to propose retention strategies like offering personalised discounts.
But that’s only if the data the AI agent is using is clean, complete, and representative. Businesses should ready their data, including data archives, by investing in data collection and governance that reduces biases and improves decision-making. This means reviewing the business’s approach to ethics, security, and compliance, and ensuring that standards like the GDPR and CCPA are met.
Get buy in from the ground
The adoption of agentic AI will spark organisational change. Roles will shift, as teams move from managing outputs to managing outcomes. Employees will need to be prepared to supervise decision-making, audit AI-driven initiatives, and collaborate with systems that offer suggestions rather than commands.
This makes getting buy-in from employees crucial to prepare the organisation to shift and encourage upskilling. Teams will need training to work alongside AI, managing models, and interpreting outputs to navigate ethical, legal, and security concerns.
Finally, work to build in accountability. Implementing systems to trace decisions back to their source and ensure alignment with business values will help keep adoption of the technology on track and encourage buy-in from employees.
Remember to stay agile
When it comes to AI, staying agile and being able to pivot fast is the name of the game. Businesses should track emerging AI capabilities and have contingency plans to pivot quickly as the landscape evolves.
The evolution to agentic AI won’t happen successfully overnight. But it’s already underway, and being able to scale intelligent decision-making and stay agile in an increasingly unpredictable business environment will support faster adoption of the technology.
The bigger picture
As this article has detailed, the move from AI agents to agentic AI is more than just a tech upgrade. It’s a mindset shift.
It changes how decisions are made, how systems adapt, and how humans and machines collaborate. Bringing initiative-driven AI to life inside the enterprise will require businesses to be – and stay ready – and understand their data and technology stacks, as well as adopting the right mindset.
