Transform or Be Left Behind: The Agentic AI revolution explained
AI agents are fast becoming embedded into business operations. From virtual customer service bots to marketing automation tools, agents promise faster service, smarter decisions and greater adaptability. Previously limited to their own data, agentic AI models now incorporate additional information and capabilities through special APIs and developments like Model Context Protocol (MCP), creating reliable connections to external sources.
For IT and data leaders, these tools represent a new generation of intelligent systems-ones that can boost productivity across the organisation. For consumers, AI agents open up an exciting portal of new personalised customer experiences, alongside AI assistants and new modes of gen engine optimised ‘conversational commerce.’
“AI agents are poised to become part of everyday life. Google’s Gemini helps plan your week, while OpenAI’s voice assistants manage tasks through natural conversation,” says Jonathan Reeve, Vice President, APAC at Eagle Eye. “A wave of startups and innovators are already building AI agent solutions for specific business needs using foundation models from leading providers.”
Defining agentic AI and rising above the hype
But experts are calling on the industry to step up on defining agentic AI clearly, choosing the right solutions amongst the ‘AI washing hype.’
“I keep hearing the term agentic AI thrown around, and frankly, I’m confused,” says Marty Hungerford, Chief Innovation Officer at BRX. “I’ve been searching for this mythical agentic AI, and all I seem to find are automated processes that leverage AI to complete tasks.”
“Let’s define it clearly: an agentic AI is one that acts like a true digital agent, setting goals, making decisions, taking action, and learning independently.”
So, what should a true agentic AI be able to do? According to Hungerford, they need to:
- Understand and interpret high-level goals
- Plan and decompose tasks independently
- Make decisions under uncertainty
- Take action across systems without supervision
- Self-correct and learn from feedback
“Recently, we built a zero-touch solution that scours the web for the latest AI news, writes a two-person script, generates two AI avatars to perform it, edits the video, and emails it for human review,” he explains.
“All of this happens without intervention. Impressive, yes. But agentic? No. It’s a finely orchestrated automated process linking multiple AI tools, not a self-driven agent. In fact, I’ve yet to encounter any AI that ticks all the boxes above.”
Agentic AI and the future of loyalty and customer experience
Experts agree the marriage of AI, retail and marketing makes a lot of sense. Eagle Eye, for example, already has a powerful AI-driven personalisation engine and other predictive systems, which thrive on ingesting and processing data intelligently.
In addition to being able to ask questions, AI agent helpers can make decisions, compare prices and steer people to where to shop. This stands to change how retailers reach customers.
“Consider this scenario: a customer asks their AI assistant, “Where can I unlock behind-the-scenes content as a member?” If your program’s benefits can’t be found and understood by that assistant, you’ll be excluded from consideration,” Reeve explains.
“AI agents, personal shoppers and deal-hunting assistants will change how brands promote their products and offers. The way large language models and agents process information will likely lead to a reorganisation of marketing strategies and loyalty structures.”
According to The Australian Loyalty Association (ALA) Founder and Director, Sarah Richardson, AI innovation is now giving brands the ability to deliver personalisation at scale, tailoring offers and experiences to each individual in real time across channels.
“This level of engagement also helps brands to analyse behavior patterns and anticipate what customers might need or want before they even know themselves,” she adds.
“Agentic AI will be most transformative to the loyalty landscape. Having an agent that can answer all your queries with relation to your membership as well as past purchase information helps brands to get on the front foot with customer expectations. Emerging technologies like voice assistants and visual search are also creating new pathways into loyalty ecosystems, so there’s plenty of innovation that AI will bring!”
Billy Loizou, APAC Area Vice President at Amperity agrees agentic AI is poised to reshape how brands compete for consumer attention globally.
“Imagine a world where your next purchase isn’t selected solely by you, but by an AI agent acting as your personal shopper. Need an autumn outfit? Your AI agent instantly scours online stores, considering your size, style preferences, budget, event theme, and even the weather forecast to deliver perfectly tailored recommendations,” he says.
Agentic AI requires solid data foundations
Loizou notes success in the era of AI agents will hinge on a brand’s ability to deeply understand customer preferences and anticipate future needs.
“Brands that excel will consistently surface the most relevant recommendations, predicting and meeting their customers’ evolving desires and behaviours,” he explains. “To succeed in this future, brands must fundamentally transform how they collect, unify, and leverage customer data.”
To prepare for a future where AI agents traverse the world wide web, Loizou recommends brands invest in their data infrastructure now.
“Companies that excel at managing customer information will create a positive data cycle: the more effectively they use data to personalise interactions, the more engagement they’ll generate, leading to richer datasets and increasingly tailored experiences. Such precision will also help brands craft offers capable of navigating past AI gatekeepers,” he adds.
Derek Slager, Co-Founder and CTO, Amperity, agrees. Slager stresses even the most advanced AI agent is only as good as the data it’s built on.
“At their core, AI agents use data to make decisions across systems, based on constantly changing variables and conditions. However, if the underlying customer data is spread across disconnected tools, fraught with duplication or siloed in different formats, the agent is doomed to be ineffective,” he explains.
“Fragmented, outdated or inconsistent information can make the best tech unreliable. To work effectively, AI agents need data foundations that are accurate, connected and governed. Without them, outputs become unreliable and trust breaks down. Meanwhile, expectations keep rising.”
Agentic AI designed for real business
Looking to harness the benefits of AI to provide its customers with more features, one company has taken steps to build AI features conveniently into its product, providing users with hassle-free access to frontier agentic technology.
Leading enterprise resource planning and analytics software provider, Pronto Software, recently signed a strategic agreement with IBM Australia, enabling the integration of powerful agentic AI capabilities into its Pronto Xi ERP platform via IBM Watsonx.
Agentic AI enables systems to autonomously interpret data, initiate actions, and optimise workflows, all with the goal of enhancing productivity and decision-making. By embedding this capability into the core ERP platform, Pronto Software ensures these tools are accessible where they are needed most in real operational environments.
Pronto Software Managing Director Chad Gates says the initiative is designed to democratise access to intelligence, helping businesses develop the capabilities of their teams.
“We’re using AI to elevate workers, not replace them,” says Gates. “Our customers, many of them family-run, mid-sized businesses, can enable staff to act strategically. Pronto Software can work with customers to build and deploy agentic AI that not only informs, but acts on the information, unlocking real business value without compromising security.
Another example of ‘no hype’ agentic AI is Red Owl, a fresh innovation that is transforming business transactional workflows with the power of AI and automation.
“AI agents are revolutionising how the modern enterprise operates,” says Jitto Arulampalam, Chief Executive Officer at RedOwl. “As an example, the advent of agentic AI is about to breathe new life into the age-old profession of Accounting and the necessary governance protocols that go with it. At RedOwl, we have seen AI’s ability to operationalise board mandated governance, compliance and control across the organisation. We also see a future where AI agents are delivering board managed governance and control in real time.”
Maturity lags and mindset shifts in the age of agentic AI
Despite the buzz around agentic AI, research reveals significant maturity gaps when it comes to adoption and transformation. A recent Digital, Marketing & eComm in Focus 2025 report, produced by digital, data and eCommerce advisory and consultancy Arktic Fox, in collaboration with recruitment firm Six Degrees Executive, revealed a whopping 75% of all surveyed brands felt their eCommerce maturity lags behind global leaders and they have work to do. In fact, only 2.5% of retailers believe their maturity is very high and on par with global leaders.
Teresa Sperti, Founder and Director at Arktic Fox says with their houses out of order and lacking maturity, particularly in the current core pillars of eCommerce, local brands stand to be challenged even further with the growth of AI, which is going to completely reshape the shopping experience.
“Agentic AI will see machines talking directly to machines to undertake shopping on behalf of consumers and B2B buyers and that will completely up-end the shopper journey as we know it – as it means we need to market as much to the machines as we do shoppers,” she adds.
“I believe retailers who don’t understand where the industry is headed are at risk of extinction within five to ten years, given that the vast majority of product discovery for most categories now starts online.”
Anthony Cipolla, AI Lead with data-led asset management solutions firm COSOL, agrees. He sees organisations across another sector, the asset-centric industry landscape also exhibit mixed maturity when it comes to their AI journeys, and are looking for guidance on getting AI integrations right.
“Verticals that rely on Enterprise Asset Management (EAM) are undergoing a revolution whereby traditional high manual effort required by humans to establish and maintain quality digital twins and master data will be rapidly replaced with semi-to-fully-autonomous agents which are capable of speeding up and improving workflows and processes,” he explains.
“AI is both a disruptor and an enabler, and no doubt there will be tensions and hurdles along the way as businesses reconcile this. Cultural change, mindset and trust will be key factors that organisations either have faced, are facing or will face along their efforts to modernise with data, AI and automation.”