AI Use Cases: Just the Tip of the Iceberg, Use IDEA
AI, this is likely the most frequently and widely used acronym of the past years since the public launch of ChatGPT in 2022. Across industries, companies are trying to capitalise on AI opportunities.
Artificial Intelligence, different from conventional algorithms (built on rule-based programming) is the replication or augmentation of human reasoning and decision making in machines.
This includes machine learning (ML) algorithms for pattern recognition, large language models (LLM) for natural language processing and generation, speech-to-text systems, computer vision for visual data interpretation, and decision-support mechanisms based on predictive modelling. Whereas some forms of AI such as Machine Learning already find plenty of real-life application in the financial services industry, for example in areas like fraud detection, risk modelling, data analysis etc., financial institutions are looking for proper use cases to implement Gen AI to maximise their potential. The key functionalities of Gen AI providing added value to companies are:
• Summarisation: Gen AI can simplify complex texts, reorganise content and extract core messages from long documents
• Semantics search: It can do quick searches and extract information, detect anomalies in documents and have conversational interactions about the content of documents with the user
• Code automation: Gen AI is able to generate code automatically based on natural language requirements
• Content generation: It can write texts, create videos, audios and more, which can be used for the creation of policies, internal training material, production of reports, marketing etc.
These functionalities can be used individually or combined for building solutions that increase efficiency and reduce the operational risk for many otherwise manual tasks. In the following paragraphs, some use cases highlighting the impact that Gen AI can have on the financial services industry are presented.
Use Cases
For most people the application of AI has been limited to the use of chatbots, but new Gen AI tools are being developed every day exploiting the four key functionalities that Gen AI offers. We, at Reply, have been working with various clients across industries to integrate AI in different processes and solutions:
• The Compliance Monitoring Plan tool (CMP tool), developed by Reply and already deployed to several clients. It improves the conventional CMPs by centralising all CMP content, such as obligations, requirements, risks and monitoring data, into a unified platform and providing continuous suggestions to update the content based on regulatory changes. The tool scans the regulators’ websites daily to identify any new publications. The AI then identifies any changes resulting from the regulation and assesses whether they are applicable to the specific institution. If the new regulation applies, the tool suggests changes to the CMP to incorporate it. Crucially, all AI-suggested updates are subject to mandatory review and approval by compliance officers, ensuring human oversight and regulatory accuracy. This solution boosts efficiency, reduces manual workload and strengthens compliance governance.
• Machine Learning models often behave as black boxes: they take structured input data and generate predictions, but without exposing the underlying rationale or decision-making process behind the result. This lack of transparency can pose significant risks when basing important decisions on analyses made by ML. The xplAInability tool developed by Reply uses Gen AI to illustrate with natural language decisions made by ML models. It enables to retrace and comprehend the steps taken by the ML tool while processing the data. It transforms the black box into comprehensible decision-making tool. xplAInability enables a new form of understanding and credibility of ML models.
• Another use case proposed by Reply focuses on AI solution capable of automatically generating code, solely on the basis of business requirements expressed in natural language. This is dedicated to supporting IT project management by helping with the preparation of business requirements and their correct upload on Business Requirement monitoring applications (e.g. Jira), coding production and test design (that can be fed in the test execution tool of the client). Furthermore, it can automate testing based on a given test plan. It goes as far as doing delta-testing of different code versions in order to determine what test cases should be used for non-regression testing. With all these features, the tool can boost productivity through the automation of repetitive tasks, improves quality by minimising errors and misinterpretations - thus reducing operational risk, and it decreases costs by involving human resources only as controllers of the process.
• The fourth use case is the Pathfinder, a collaborative multi-agent framework designed to perform complex, non-linear financial analyses by orchestrating specialised AI agents that aggregate and interpret heterogeneous data sources; such as market trends, news, and financial statements, through emergent reasoning to deliver coherent, context-aware insights. Pathfinder offers significant advantages for organisations conducting market analyses: it processes large volumes of data to deliver a more comprehensive view of financial scenarios, enhances analytical accuracy by minimising human error, and drastically reduces time-to-insight through automation, cutting analysis time to just minutes.
These four use cases only offer a small glimpse into the possibilities that are opened up through the implementation of AI solutions in daily operations. Reply has built deep expertise in developing AI solutions through a combination of continuous internal R&D and close collaboration with top-tier clients across Europe and globally.
Challenges
With such wide-ranging opportunities, it is easy to get swept away and have the impression that AI is the solution to all our problems. But identifying the Use Cases that could better fit your organisation is only the proverbial tip of the iceberg. Under the surface, hidden away from the spotlight that shines on AI from all directions are many challenges looming that await any potential adopter of the technology. After a company decides to integrate an AI solution into their operations, the implementation process is tricky: political, technical and regulatory factors must be considered to guarantee a successful AI adoption.
The first and foremost hurdle when implementing Gen AI solutions is the design of the underlying infrastructure. The starting point is always a careful assessment of the sensitivity of the treated data. This determines whether it is acceptable to rely on public Large-Language-Models (LLMs), such as ChatGPT, whether the solution should use foundation models offered by major cloud providers (Microsoft, Amazon, Google), or whether extremely sensitive data require an on-premise setup, hosting open-source LLMs on privately owned GPUs. Alternatively, European cloud providers can also be considered for hosting non-proprietary models while maintaining data sovereignty.
Cost is another important driver. While GPU ownership comes with high upfront investment, cloud-based LLMs typically follow token-based pricing models. In some cases, for instance, high token volume but low model complexity, purchasing and hosting a mid-sized model may turn out to be more cost-effective. Model selection should also be supported by external advisors and available market benchmarks. In short: choosing the right infrastructure is already a significant effort.
Decisions about the underlying infrastructure also have a significant impact on the overall data-security of the desired AI solution. Data security needs to be actively considered before, during and after the integration of any solution. Because of the transfer of data between the company’s systems and the LLM, it needs to be ensured that communication flows are highly secured with no chance of any data leakage.
Additionally, if the AI solution includes a public-facing interface, AI Red Teaming is essential. It combines traditional IT security testing with AI-specific checks to ensure protection against model poisoning, unauthorised content manipulation and excessive usage leading to cost or performance issues.
Another major consideration is data readiness. Although Gen AI can process large amounts of both structured and unstructured data, the results are only as good as the quality of the inputs. “Garbage in, garbage out” still applies. Furthermore, Gen AI is best suited to support repetitive and time-consuming processes, which means integration with legacy systems is critical. If key documents are locked in siloed systems and require manual download and upload, the value of automation is lost. In addition, privacy and compliance requirements must be addressed early on, especially when handling personal or regulated data. Legal and DPO teams should be involved from the very beginning.
In addition, the role of humans in the loop cannot be overlooked. Gen AI cannot make autonomous decisions. The more complex the process, the more critical it becomes to design human checkpoints throughout the workflow, both for operational and technical supervision. AI systems must be monitored and maintained over time. In our experience, companies increasingly prefer to train their internal IT teams to take ownership of post-deployment maintenance, rather than depending permanently on consultants.
Finally, to ensure a successful rollout and adoption, cross-functional training programs are essential. These helps educate employees on the basics of Gen AI, clarify realistic expectations, identify relevant use cases, and dispel common misconceptions.
One of the most widespread misunderstandings, for instance, is the belief that Gen AI can be “trained” simply by feeding it large datasets. In reality, LLMs are pre-trained during their creation, and while they can be fine-tuned, this process is highly complex and resource-intensive. Gen AI solutions are best applied where inputs and outputs can be clearly defined and consistently structured.
That said, although LLMs cannot be permanently trained without significant effort, it is possible to implement feedback loops within each solution. These loops allow the system to iteratively adjust and refine its outputs based on user validation or corrections, improving alignment with business expectations and helping mitigate biases over time. Importantly, this optimisation does not alter the underlying model itself, but enhances the performance of the solution in its specific context of use.
Methodology
Having explored four high-impact Gen AI use cases and highlighted the key challenges organisations face in implementing them, it becomes clear that success requires more than just technology. It requires structure, clarity, and the right approach. That’s why Reply has developed IDEA, a pragmatic and end-to-end methodology that stands for Inspire, Design, Empower, and Achieve. This framework guides organisations through the entire AI adoption journey ensuring that AI solutions deliver real business value, while minimising risks and avoiding the common pitfalls of isolated experiments.
1. Inspire: Setting a Strategic AI Foundation
Stakeholder engagement and high-level governance.
Identify key stakeholders (CEO, CIO, COO, CRO, CCO, heads of departments) and define a high-level governance framework, including the creation of an AI steering committee or decision body to oversee priorities, risk, and budget.
AI vision definition
Clarify the organisation’s AI ambition, for example two diverse but common approaches are
· Holistic: cross-functional solutions and high AI maturity goals
· Focused: selected high-impact use cases only
This strategic direction will guide prioritisation, resource planning, and communication.
Training and internal capability mapping
Launch transversal training sessions to raise awareness and align language across departments. Tailor deep-dives for key areas such as IT, compliance, and business functions.
Take the opportunity to manage expectations: several misconceptions are spreading about the capabilities of GenAI, Gen AI is not magic: you need a clear process, defined input and specific prompts to develop quality AI solutions.
Tips: (1) Run post-training bottom-up surveys to collect potential use cases from departments—this boosts engagement and uncovers real needs. (2) Assess internal tech team readiness to support AI maintenance and start evaluating the trade-off between using consultants and allocating internal resources.
Cloud and data strategy alignment
For each use case, assess data sensitivity and determine the most suitable architecture. Consider in general:
· Low-sensitivity or external data → external LLM via API
· Medium sensitivity → managed LLMs from trusted cloud providers
· High sensitivity or token-heavy use cases → consider hosting open-source LLMs on-prem or on private cloud with dedicated GPU infrastructure
Evaluate options based on GenAI application type (e.g., summarisation, search, generation, coding) and industry benchmarks.
Cost-benefit evaluation
For each AI use case, it is essential to assess three key dimensions: costs, expected benefits, and associated risks.
Cost estimation should consider the following rationals:
· Tokenisation costs vs. GPU investment
· Consultant effort vs. internal build
· Time & involvement required from internal teams (business + tech)
· Long-term maintenance effort
On the benefit side, organisations should quantify potential gains in terms of process efficiency and improved quality.
Finally, structure a risk assessment for each use-case: data sensitivity, process regulatory requirements, critical deliverables (such as responses to claims, objections, or regulatory filings) etc.
Tip: (3) Start defining appropriate checkpoints, audit trails, and human-in-the-loop mechanisms, which in turn affect the overall effort and operating cost of the solution.
2. Design: From Use Case Selection to Project Structuring
Use case prioritisation and backlog creation
With initial use cases and technical hypotheses identified, the next step is to define a structured selection methodology to build the AI backlog. Selection criteria typically include:
· Technical and infrastructural feasibility
· Cost and effort estimates
· Risk profile
· Cross-functional applicability
Tip (4): Involve domain experts to assess the real added value of each use case from a business perspective—this increases relevance and early buy-in.
AI governance definition
Establish a clear AI governance framework that defines:
· Roles and responsibilities (e.g. product owner, risk officer, model owner)
· Validation workflows and documentation standards
· Model monitoring, explainability, and bias mitigation practices
· Interaction points with compliance, risk and internal audit
This governance ensures traceability, accountability and sustainable deployment, especially for high-impact or sensitive use cases.
Backlog validation and project structuring
The prioritised backlog should be formally validated by the AI steering committee or equivalent decision body. This should be followed by a structured communication phase across departments to explain the rationale behind the selected use cases. At this stage, the AI initiative is translated into a concrete implementation project, including:
· Choice of delivery methodology (e.g. agile or iterative waterfall)
· Definition of sprints or implementation phases
· Assignment of responsibilities and coordination mechanisms
Data preparation and technical scoping
Before implementation, ensure data quality as well as that sources are clearly mapped, accessible, and aligned with the model's input requirements. This includes:
· Identifying existing APIs or planning bulk data extraction methods
· Resolving data format or quality issues
Assessing integration points with internal solutions
Tip: (5) The development of pilot use cases can begin even before full validation is complete. This allows teams to build internal engagement, test initial assumptions, and accelerate delivery once the full roadmap is approved.
3. Empower: Build, Test and Embed AI Solutions
Develop the selected use cases
Move from concept to working prototype by developing the prioritised use cases using the data and infrastructure previously mapped.
Tip (6): Consider decoupling complex workflows into smaller, more structured tasks. GenAI models are powerful but not truly “intelligent” — simplifying logic improves accuracy, reliability, and maintainability of the solution. A practical way to go further is by designing ultra-specialised agents, each focused on a specific subtask, which can be orchestrated by one or more generic agents responsible for consolidating outputs and managing the end-to-end process. This modular agent-based approach increases robustness and flexibility, especially in complex use cases.
Design and execute tests with the right counterparts
Define a robust testing approach tailored to GenAI’s non-deterministic nature, including functional checks, edge case testing, and performance monitoring. For externally exposed solutions, integrate AI red teaming to ensure resilience against poisoning attacks, protection of underlying content, and prevention of excessive billing from repeated interactions.
Tip (7): Involve domain experts early on to validate the outputs and ensure business relevance. Their involvement is crucial not just for acceptance, but also for challenging and refining the model’s behavior. This means identifying and engaging the right people already in the early project planning stages.
4. Achieve: Validate Value and Ensure Operational Continuity
Validate business impact
Assess whether the AI solution delivers the expected value. This involves both quantitative and qualitative KPIs, such as:
· Time saved on specific processes
· Reduction in error rates or manual interventions
· User satisfaction or adoption metrics
· Increase in throughput or capacity (e.g. more requests handled, more documents processed)
These KPIs should be compared to initial expectations to confirm business alignment and inform future scale-up.
Formalise the new process
Once validated, the AI-enhanced process should be documented and formalised within internal procedures:
· Clearly define when and how human validation (human-in-the-loop) is required
· Integrate the new process into existing operational workflows
Deliver targeted training
Ensure smooth adoption and sustainability through tailored training:
· End-user training on how to interact with the solution (e.g. prompts, validation steps, escalation rules)
· Technical team training to enable ongoing maintenance, such as adapting inputs, retraining logic, or extending capabilities
Ensure lifecycle ownership
Assign clear responsibilities for monitoring, retraining, and continuous improvement. This includes:
· Setting up feedback loops from end users
· Periodic performance reviews and revalidation
· Technical monitoring (e.g. API uptime, output consistency)
Conclusions
Gen AI use cases are everywhere: exciting, promising, and full of potential. But identifying them is just the tip of the iceberg. Turning them into real business value requires the right methodology and the right partner. With our structured approach and hands-on experience, Reply helps organisations move from idea to execution, making AI work, for real.