How businesses can harness the power of predictive and agentic AI to improve decision-making, streamline processes, and gain a competitive edge.
As predicted by tech leaders, 2025 has already seen the arrival of AI agents. Open AI’s Operator agent, for example, can not only search the web but perform actions on behalf of users such as ordering groceries or booking a restaurant reservation. And in a similar manner to DeepSeek, Manus, a new AI agent developed in China, has just been launched to disrupt the AI scene.
Alongside these developments, Amazon Web Services (AWS) has recently formed a new group focused on agentic AI “to help users and customers automate more of their lives”. The timing of this announcement is strategic and forward-thinking – AWS wants to position itself at the forefront of the transition to AI agents for businesses.
We’re at an inflection point where enterprises are moving beyond experimentation with AI to seeking tangible business value. But much like sifting through the hype that came with generative AI models like Open AI’s GPTs and Anthropic’s Claude, how can businesses actually achieve true impact from agentic AI to steal ahead of competitors?
AI agents only as good their tools and processes
AI agents are only as good as the tools they leverage; the most effective agents will integrate with superior tools and data sources. They can maximise their impact by accessing advanced analytics platforms, real-time data feeds and domain-specific software, for example. But this has to take place in a controlled and managed way – and that’s where effective processes are needed.
When evaluating agentic capabilities, organisations should prioritise three factors:
- Controllability: how effectively can they build, direct and constrain agent behaviour?
- Interoperability: how well can agents work with existing systems, data repositories and tools?
- Auditability: how transparently can they track agent actions and their decisions?
There are also considerations around potential platform lock-in. Cloud providers like AWS, for instance, will be hoping their agentic AI value proposition will be compelling enough that customers won’t want to leave.
But many technology leaders will want to ensure their agents can operate fluidly and securely between all clouds, systems and processes across their business. So, they are likely to be after agents that provide a balance between portability and access to a cloud provider’s native services.
Proprietary data will keep businesses one step ahead
Any competitive advantage from agentic AI will come from proprietary data ecosystems – i.e. high-quality data competitors can’t access – and predictive capabilities: these enable agents to anticipate the future, as opposed to agents simply looking backwards historically. But there is a spectrum across industries in how set up organisations are to create this advantage.
The industries, like financial services and manufacturing, that have done the hard yards to map out processes, business logic, workflows and key decision-making criteria are the ones currently one step ahead. These components can then be used to map out the optimal use of AI agents and give them the critical context, business rules, and guidelines they require to operate autonomously and effectively.
In particular, organisations that have invested in infrastructure like cloud data lakes and function-specific data warehouses are moving faster to give AI agents access to the right structured and unstructured data. They are generally in a good place with their enterprise data strategy and that means they are well-positioned to capitalise on agentic automation immediately.
But this is a select group. Many organisations still lack the data infrastructure, governance frameworks and AI literacy across the business required for its successful adoption and implementation. As a result, this is leading to many experimenting with co-pilots that augment processes, rather than autonomous agents that are taking actions on their behalf within key business processes.
The most forward-thinking enterprises are looking to address these gaps now, recognising that agentic AI isn’t just another tech trend but a significant business opportunity.
Transitioning to proactive autonomy
The convergence of predictive machine learning models with autonomous agency will create powerful agentic systems that can both forecast outcomes and take independent action. This evolution marks AI’s transition from passive analysis and rule-based automation to proactive decision-making and autonomous action.
Predictive ML models excel at forecasting future outcomes based on patterns in data, for example, while agentic AI systems possess the capability to take independent action based on set tasks or objectives. Therefore, when these two capabilities converge, AI agents can not only predict what is likely to happen but also autonomously respond to and even preemptively act on those predictions.
In the retail world, for example, AI agents could be used to independently order stock items in line with predicted consumer demand for those products. So, rather than execute these actions, supply chain managers can instead oversee factors like costs and monitor if agent decisions are happening in the right way.
This automated inventory optimisation would not only help to drive both margins and revenue, but free up even more time for managers to strategise on consumer predictions and how to maximise agent behaviour.
Harnessing a new power
It was only this time last year the tech industry was discussing how best to integrate models like GPT into business workflows. The speed of innovation is frenetic, and agentic AI capabilities represent a fundamental shift in how business processes can be executed.
But AI agents are only as good as the tools and processes they are given. There is a readiness gap amongst industries for their use, and the companies that can integrate it into their data systems and workflows most effectively will gain a competitive edge.
Yet one of the most powerful ways to bridge this gap is through combining predictive AI capabilities with agentic AI – these companies will be able to anticipate consumer market trends ahead of the game, drive efficiency and ultimately bolster their bottom line.
With the right tools and processes set up, companies can harness the very best of both predictive and agentic AI.
Author Quote or advice: AI Agents are only as good as the tools they use and the context they are grounded in. Go deep on mapping your critical business processes, the underlying systems, and the decision logic and data flows throughout. The enterprises who do this are already ahead in their ability to leverage Agentic AI. If you don’t understand your own processes intimately, your AI Agents have no chance