Author

Santo Orlando

Published

8 December 2025

Estimated Read time

5Mins

Santo Orlando, Practice Director – App, Data and AI Services at Insight, on how your organisation can level up with Agentic AI

By now, most of us have heard of Generative AI. Many businesses have already adopted the technology for tasks like customer service, code generation and content creation. Generative AI, however, is only the start; we’re only scratching the surface of the potential that AI has to offer

Enter Agentic AI

Unlike Generative AI, which relies on human input and prompts, Agentic AI can act autonomously to fulfil complex tasks without human intervention. As a result, nearly 45% of business leaders think Agentic AI will outpace Generative AI in terms of impact, and more than 90% expect to adopt it even faster than they did with generative AI. However, despite its promise, our joint understanding of Agentic AI – and how to implement it – is still very much in its infancy.

So, where do you start? To kickstart your Agentic AI journey here are five fundamental steps to consider. 

Generative AI vs Agentic AI

If Generative AI is like having a personal assistant, supporting you one-on-one to speed up your tasks, then Agentic AI is more like having a dedicated team of smart, individual coworkers who can take initiative and get things done across your business – without needing constant oversight. 

One powerful example of this in action is in sales. With Agentic AI, organisations are able to receive real-time insights during discovery calls. The AI ‘agents’ allow sales reps to respond with timely, relevant information, helping them build trust, operate faster and close deals more effectively. 

By collecting and analysing data from across teams, agents can uncover patterns, translate complex metrics into actionable strategies and even highlight opportunities that might otherwise be unintentionally overlooked. In some early implementations, sales teams have reported saving five to ten hours per rep each month – adding up to thousands of hours redirected toward deeper customer engagement.

The one-to-one relationship we’ve grown accustomed to with Generative AI has evolved into the one-to-many dynamic of Agentic AI, which is capable of handling tasks for multiple users and automating entire business processes. Even more impressively, agents can make decisions, control data and take actions on their own. A capability that can seem daunting without a clear understanding of how it works.

That’s why businesses need to start small, and here are a few practical steps to get going quicklyand wisely with agentic AI. 

Step 1: Getting your data ready

Agentic AI is the logical progression for organisations already exploring generative tools. However, the data needs to be in an optimal condition – clean, organised and secure – before autonomous agents can be deployed effectively.

As such, eliminating redundant, outdated and trivial (ROT) data is vital. Without removing ROT, agents may rely on obsolete information, leading to inaccurate or misleading outputs. For example, this could happen if a company deploys an HR chatbot that’s connected to outdated data sources. If an employee were to ask about their 2025 benefits, the chatbot might pull information from as far back as 2017, resulting in confusion and misinformation.

Proper file labelling, standardised document practices and use of version histories in place of multiple saved versions helps to ensure agents access only the most relevant and accurate information.

Step 2: Start with low-risk cases 

Agents work on a transactional basis, charging for each operation, which can quickly add up. As such, it’s wise to experiment with simple, low-stakes applications first. This approach allows for quicker deployment and demonstrates immediate value to the business without significant costs or risks.

One example could be using an agent to assess sentiment in social media responses following a product launch. This can offer real-time feedback on public perception and inform messaging strategies. Other low-risk use cases include generating reactive press releases and monitoring competitor websites. Additionally, prioritising automation of routine tasks, especially those involving platforms like Salesforce, SharePoint, or Microsoft 365, allows teams to maximise impact without costly system overhauls. 

Overall, organisations need to be willing to fail fast and expect failure. It won’t be perfect from the start. However, an experimental pilot approach helps to efficiently refine AI agents, reducing the risk of costly mistakes and making sure that only effective solutions are scaled up.

Step 3: Create a single source of truth

Establishing a dedicated, cross-functional team to explore agentic AI use cases helps prevent siloed adoption and supports enterprise-wide visibility. This team should span as much of the organisation as possible and include representatives from departments such as marketing, finance and technical solutions.

Collaborative workshops can then act as a forum to identify key processes that would benefit from autonomous capabilities and help businesses align potential applications with specific departmental objectives and broader business goals.

Step 4: Learn, learn and learn

Many companies underestimated the importance of training and governance with Generative AI – and Agentic AI is no different. Organisations need to establish clear governance to define how AI agents should and shouldn’t be used, covering not just technical implications, but HR, compliance and risk concerns as well.

Equally, businesses and those employed must understand Agentic AI’s full functionality to get the most out of it. Like with almost all technical training, AI education cannot be viewed as a one-time ‘tick-box’ exercise. Ongoing learning is necessary to keep pace with new capabilities and best practices.

For example, consider what’s already emerging, like security agents that automate high-volume threat protection and identity management tasks; sales agents that find leads, reach out to customers and set up meetings; and reasoning agents that transform vast amounts of data into strategic business insights.   

Step 5: Reviewing ROI

Enthusiasm around Agentic AI is high. But before organisations dive in headfirst, it’s important they first define success. Technology can’t be the solution if there is uncertainty surrounding the goal. Successful deployment requires a clear definition of the problem organisations are looking to solve and knowledge of how to align the solution with measurable business value. Without this, initiatives risk stalling at the experimental stage.

Key performance indicators should also be identified early. These may include increased productivity, time savings, cost reduction or improved decision-making. Establishing these benchmarks and taking a data-driven approach ensures that AI initiatives align with business goals and demonstrate tangible benefits to stakeholders.

Moving forward

The process of switching to Agentic AI is about changing how businesses handle everyday problems with wide ranging effects, not just about using cutting edge technology. Iteration and learning along the way, as well as deliberate, measured adoption are the keys to increasing value. It’s simple. Success with AI starts with small, straightforward actions and use cases.

Learn more at insight.com

  • Data & AI
  • Digital Strategy

Author

Alessandro De Leonardis

Published

5 December 2025

Estimated Read time

6Mins

From banking to alternative funds, modular architecture is the missing link for effective adoption of artificial intelligence, writes Alessandro De Leonardis, CIO of Armundia Group

The global banking industry is approaching a strategic crossroads – one that will prove expensive for those who choose the wrong direction. Financial institutions stand to lose USD 170 billion in profits over the next decade if they do not adapt rapidly to the evolution of artificial intelligence, according to the McKinsey Global Banking Annual Review 2025. Yet the report’s most provocative insight isn’t about AI itself, but the infrastructure required to leverage it effectively.

Agentic AI has the potential to reshape banking at its foundations. Early adopters will strengthen long-term advantages, potentially boosting returns on tangible equity by up to four percentage points. On the other hand, laggards face structural declines in profitability. The difference between these outcomes won’t be determined by who adopts AI first, but who has the architectural foundations to implement it effectively. Increasingly, those foundations are modular.

From Generative to Agentic AI: Revolution not Evolution

To understand why architecture matters so deeply, we must distinguish between the two paradigms reshaping financial services.

Generative AI, the star of 2023-24, excels at creating content: automated reports, document summaries, customer-service response, and so on. It is powerful, but fundamentally reactive. GenAI requires human prompts and produces outputs that must still be reviewed and acted upon by humans.

Agentic AI represents a step-change. These systems combine autonomous reasoning, planning, and execution. They don’t only generate recommendations, they act on them. An Agentic AI system can autonomously manage an entire loan-approval workflow: collecting documents, verifying information, assessing creditworthiness, checking regulatory compliance, and making approval decisions, all without human involvement at each step.

The impact is already measurable. MIT Technology Review Insights found that 70% of banking leaders are implementing agentic AI through production deployments (16%) or pilot projects (52%). Deloitte reports early adopters achieving 30–50% cost reductions in specific workflows. McKinsey anticipates the emergence of a “disruptive agentic business model” within three to five years, with potential cost reductions of up to 70% in some categories. But the benefits are far from evenly accessible.

Why Monolithic Architecture are Incompatible with AI

The uncomfortable truth is that most banks are attempting to deploy twenty-first-century AI on twentieth-century infrastructure. And it doesn’t work.

Legacy systems still absorb around 60% of banks’ technology budgets, according to a 2024 Bloomberg Intelligence survey. These monolithic architectures were never designed for the rapid iteration, continuous integration, and granular governance demanded by AI deployment.

Monolithic systems require release cycles lasting months; AI models require continuous retraining and fine-tuning based on real-world performance. The mismatch is structural. Modern Agentic AI relies on orchestrating multiple specialised agents… One for data collection, another for risk evaluation, a third for decision execution. Monolithic architectures struggle to support this level of inter-system communication.

Governance is another barrier. AI systems require differentiated risk controls depending on the level of autonomy. A fully autonomous fraud-detection agent needs different guardrails than a customer-service chatbot. Monolithic systems offer all-or-nothing governance, not graduated controls.

Financial institutions cannot transform everything at once; they need incremental adoption. Starting with high-impact use cases, learning, then expanding. Monolithic architectures force “big-bang” transformations that almost never succeed.

This architectural misalignment explains why so many AI initiatives stall in pilot purgatory, never reaching production scale.

Modular Architecture as an Enabler of AI

Modular, service-based FinTech architecture solves these problems by design. Instead of monolithic platforms, modular systems are composed of independent, interoperable functional blocks connected via APIs. Each module can be developed, updated, or replaced without affecting the whole.

The key is the concept of the service: a module that does not expose standardised technical interfaces simply does not function. Services are the technical objects enabling interoperability:

  • A compliance module exposes services for regulatory checks,
  • A data-ingestion module exposes services for data collection and structuring,
  • An Agentic AI module exposes services for executing autonomous workflows.

This architecture creates an ecosystem where each component has clear responsibilities and well-defined interfaces.

For AI deployment, this translates into concrete advantages. Banks are implementing Agentic AI systems into specific processes – KYC/AML screening, credit-memo generation, collections monitoring, intelligent communication routing – without rebuilding their entire stack. Service-based modularity allows AI agents to be activated on circumscribed workflows, with impact measured before expansion.

Because agents operate within discrete modules, failures remain contained. A malfunctioning fraud-detection agent does not propagate into customer-facing systems. This isolation allows institutions to experiment more boldly.

Service-based architectures also enable integration of best-of-breed AI solutions. One module may use Anthropic’s Claude for document analysis, another Google’s Gemini for customer interaction, a third proprietary models for highly specialised credit scoring. Monolithic systems lock institutions into single-vendor dependencies.

Different modules can carry different levels of AI autonomy, aligned with risk profiles and regulatory requirements: high autonomy for customer-service bots, human-in-the-loop supervision for lending decisions.

As McKinsey notes, the winners of this transformation will practise “precision over heft”- implementing AI surgically where it generates measurable bottom-line impact. Service-based modular architecture is the technical manifestation of such precision.

Techfin vs FinTech: When Architecture Comes First

There is a fundamental difference between starting from finance and adding technology, and starting from technology and specialising in finance.

In the first case, solutions are built top-down – gather functional requirements, then find the technology to satisfy them.

In the second, solutions are built bottom-up – design the architecture before the functional requirements, optimising for flexibility rather than feature completeness.

When designing wealth- and asset-management platforms – such as FundWatch or 360 FUNDS – this distinction becomes tangible. Being AI-ready does not mean adding an ‘AI layer’ on top of an existing platform. It means the modular architecture allows AI capabilities to be integrated precisely where needed.

Modularity operates along two dimensions:

  • Process modules (compliance, analytics, reporting, client engagement) that can be activated independently;
  • Target modules tailored for different market participants: custodians, asset servicers, alternative-fund managers, wealth advisers—each activating different module combinations.

AI governance is embedded in the architecture, not layered on top. A fully autonomous reconciliation agent operates under different guardrails than a semi-autonomous investment-recommendation agent—different approval workflows, audit trails, and supervision requirements.

This approach does not remove the need for transformation, but it changes its rhythm. Instead of three-year platform-replacement projects, institutions can transform progressively: start with a high-impact module, prove value, learn from deployment, scale outward.

The key managerial shift is conceptual: the question is no longer “When will our digital transformation be finished?” but “Which module do we activate this quarter, and what do we learn?”

The $170bn Question

McKinsey’s warning – USD 170 billion of potential profit erosion – is not inevitable. Avoiding it requires strategic decisions today about the technology architecture of tomorrow.

The institutions that will thrive are not necessarily the largest or the earliest adopters of AI. They will be those building modular infrastructures engineered for precision, capable of integrating AI surgically, experimenting rapidly, scaling intelligently, and governing rigorously.

They will recognise that AI is not merely a technological deployment, it is an architectural imperative. And they will understand the deeper truth: in the Agentic AI era, precision beats scale.

The question faced by every financial institution is not whether to adopt AI, but whether its architecture can support it. For most legacy systems built on monolithic foundations, the honest answer is no.

The modular imperative is clear. The question remains: are you building for yesterday’s challenges or tomorrow’s opportunities?

Find out more at armundia.com

  • Artificial Intelligence in FinTech

Author

Dan Brightmore

Published

13 March 2025

Estimated Read time

5Mins

Fouzi Husaini, Chief Technology & AI Officer at Marqeta, answers our questions about Agentic AI and its applications for businesses

Agentic AI is emerging as the leading AI trend of 2025. Industry figures are hailing Agentic AI as the broadly transformative next step in GenAI development. The year so far has seen multiple businesses release new tools for a wide array of applications. 

The technology combines the next generation of AI tech like large language models (LLMs) with more traditional capabilities like machine learning, automation, and enterprise orchestration. The end result could lead to a more autonomous version of AI: Agents. These agents can set their own goals, analyse data sets, and act with less human oversight than previous tools. 

We spoke to Fouzi Husaini, Chief Technology & AI Officer at Marqeta about what sets Agentic AI apart whether the technology really is a leap forward in terms of solving AI’s shortcomings, and how Agentic AI could solve business problems.

1. What makes AI “agentic”? How is the technology different from something like Chat-GPT? 

“Agentic refers to the type of Artificial Intelligence that can act as agents and on its own. Agentic AI leverages enhanced reasoning capabilities to solve problems without prompts or constant human supervision. It can carry out complex, multi-step tasks autonomously.

“GenAI and by extension Large Language Models, the most famous example being ChatGPT, require human input to solve tasks. For instance, ChatGPT needs user prompts before it can generate content. Then, sers need to input subsequent commands to edit and refine this. Agentic AI has the capability to react and learn without human intervention as it processes data and solves problems. This enables it to adapt and learn much faster than GenAI.”

2. Chat-GPT and other LLMs frequently produce results filled with factual errors, misrepresentations, and “hallucinations”, making them pretty unsuited to working without human supervision – let alone orchestrating important financial deals. What makes Agentic AI any better or more trustworthy? 

“All types of AI have the possibility to ‘hallucinate’ and produce factually incorrect information. That being said, Agentic AI is usually less likely to suffer from significant hallucinations in comparison to GenAI. 

“Agentic AI’s focus is specifically engineered to operate within clearly defined parameters and follow explicit workflows, making it particularly well-suited for having guardrails in place to keep it on task and from making errors. Its learning capabilities also allow it to recognise and adapt to its mistakes, ensuring it is unlikely to hallucinate multiple times.”

“On the other hand, GenAI occasionally generates factually incorrect content due to the quality of data provided, and sometimes because of mistakes in pattern recognition.”

“In fintech, Agentic AI technology can make it possible to analyse consumer spending data and learn from it, allowing for highly tailored financial offers and services that are more accurate and help to create a personalised finance experience for consumers.” 

3. How could agentic AI deployments affect the relationship between financial services companies and their customers? What about their employees? 

“The integration of Agentic AI into financial services benefits multiple parties. First, 

integrating Agentic AI into their offerings allows financial service companies to provide their customers with bespoke tools and features. For instance, AI can be used to develop ‘predictive cards’. These cards can anticipate a consumer’s spending requirements based on their past behaviour. This means AI can adjust credit limits and offer tailored rewards automatically, creating a personalised experience for each individual.

“The status quo’s days are numbered as consumers crave tailor-made financial experiences. Agentic AI can allow fintechs to provide personalised financial services that help consumers and businesses make their money work better for them. With Agentic AI technology, fintechs can analyse consumer spending data and learn from it. This allows for more tailored financial offers and services.   

“As for employees, Agentic AI gives them the ability to focus on more creative and interesting tasks. Agentic AI can handle more routine roles such as data entry and monitoring for fraud, automating repetitive tasks and autonomous decision making based on data. This helps to reduce human error and enables employees to focus more time and energy on the creative and strategic aspects of their roles while allowing AI to focus on more administrative tasks.”

4. How would agentic AI make financial services safer? 

“Agentic AI has the capability to make financial services more secure for financial institutions and consumers alike, by bringing consistency and tireless vigilance to critical financial processes. With its ability to analyse vast strings of information, it can rapidly identify anomalies in spending data that indicate potential instances of fraud and can use its enhanced reasoning and ability to act without human prompts to quickly react to suspicious activity. 

“While a human operator will be susceptible to decision fatigue, an AI agent could always be vigilant and maintain the same high level of precision and alertness 24/7. This is vital for fields like fraud detection, where a single missed signal could lead to significant consequences.

“Furthermore, its capability to learn without human interaction means that it can improve its ability to detect fraud over time. This gives it the ability to learn how to identify new types of fraud, helping it to adapt as schemes become more sophisticated over time.” 

5. What kind of trajectory do you see the technology having over the next year to eighteen months?

“In fintech, Agentic AI integration will likely begin in the operations space. These areas manage complex, but well-defined, processes and are perfect for intelligent automation. For instance, customer call centres where human agents usually follow set standard operating procedures (SOPs) that can be fed into an AI system, which makes automation easier and faster than before.

“In the more distant future, I believe we will see Agentic AI integrated into automated workflows that span entire value chains, including tasks such as risk assessment, customer onboarding and account management.” 

  • Artificial Intelligence in FinTech