Organisations across the globe have begun to gain experience and confidence in leveraging Artificial Intelligence (AI), integrating both tested and novel ways to improve scalability, decision-making, customer experience, and much more. Yet, according to Stanford University's 2024 AI Index report, only 55% of businesses have implemented AI in at least one business unit or function. When looking at specific AI capabilities, the Financial Services sector is leading, with a 46% embedding rate for Robotic Process Automation (RPA). 

The challenge that many businesses may face shortly isn't identifying opportunities to leverage AI further but instead modernising their existing IT ecosystem so it can accommodate new functionalities. Artificial Intelligence success requires a solid foundation for seamless data access, scalability, and integration. Legacy software often comes with problems such as data silos, security vulnerabilities, and system inefficiencies and was never designed to support AI-driven solutions. 

This gap between outdated software and cutting-edge AI capabilities creates significant roadblocks that organisations must address before AI can deliver on its full potential.  

In this article, we'll explore the obstacles legacy systems pose for Artificial Intelligence projects, how they can be overcome, and why CIOs and CTOs should consider application modernisation the first step on their AI adoption roadmap. 

The Legacy Challenge: Why AI Fails Without Modernisation 

Legacy applications are often deeply embedded within an organisation's operations, shaping whole teams' workflows and even skill sets. Either due to the associated risks, costs, or the required technical know-how, these applications have remained virtually unchanged for years. 

Across our more than 20 years of activity, we've worked on plenty of legacy system modernisation projects, and the trend is clear to see—organisations outgrowing their IT ecosystem. AI adoption is only one of the latest ways in which businesses start feeling the constraints of their old apps, usually in the form of one or more of the following challenges: 

Data Silos and Poor Integration 

Data is essential for AI to function effectively. AI models need large, quality datasets to train on, analyse, and provide increasingly insightful conclusions. 

One of the most common aspects of legacy software architecture is the creation of data silos, compared to today's philosophy of making information readily available to all relevant points across an organisation. 

While this gap can be closed without modernisation, the solution can become more complex, costly, and difficult to manage. A business would need to develop custom bridges and tools to integrate into their siloed databases to extract information and send it to the AI model. 

Modernising the system from the ground up removes the roadblock altogether, enabling further scalability and removing technical debt before it becomes even harder to manage. 

Limited Scalability and Performance Issues 

While on the topic of scalability, legacy apps often run on on-premises hardware that lacks the power needed for AI workloads and can't easily scale up. This results in performance bottlenecks, slowing down AI implementation and limiting its potential impact. 

With cloud-based ecosystems becoming more common, efficient, and cost-effective, transitioning to a more modern architecture ensures not only that the AI adoption project yields better results but also that costs stay proportional to usage. 

Technical Debt and High Maintenance Costs 

One of the strongest arguments for application modernisation projects is the technical debt that old apps gather over time. Outdated tools, incomplete documentation, and inefficient code create additional complexity, leading to higher costs for maintenance, updates, and integrations. 

In relation to Artificial Intelligence, every outdated coding practice or patch focused on the short-term makes it harder to leverage AI functionalities and diminishes the result. With the general focus on using AI to simplify processes and lower costs, technical debt has the exact opposite effect. 

Security Risks and Compliance Challenges 

Security protocols are continuously improving to keep up with the bad actors that constantly find new exploits due to the increasing level of digitalisation. With this cybersecurity arms race in the background, legacy systems pose severe risks due to outdated security and potential vulnerabilities.  

Artificial Intelligence models, particularly those handling sensitive data, require robust security and compliance frameworks to ensure data privacy and regulatory adherence.  

In essence, AI integration can potentially worsen pre-existing vulnerabilities and lead to fines due to non-compliance with cybersecurity laws. 

Accesa's Approach: Modernising for Artificial Intelligence Optimisation 

As with any modernisation project, a structured approach and a transparent, result-focused roadmap are crucial to achieving the maximum positive impact. 

In the context of enabling a seamless AI adoption into the organisation, our strong tech teams would customise the following approach to the client's specific objectives and challenges: 

01 Discovery & Roadmap Creation 

We begin with a comprehensive assessment of the existing applications and infrastructure, focusing on pain points, inefficiencies, and opportunities for improvement. Aspects such as the data quality and how it's stored, the ecosystem's technical debt and other systems that may prove challenging for AI integration are assessed and catalogued. 

Next, we begin mapping AI opportunities. Based on the organisation's activity area, objectives, and the current state of their legacy systems, we identify business areas where AI can drive value and set project goals. 

Based on this assessment, we create a tailored modernisation and AI adoption roadmap that outlines the specific steps needed to achieve the project’s goals, including timelines and resource allocation. 

02 Modernisation and Data Preparation 

In this phase, we implement the strategies defined in the roadmap. Depending on the organisation's requirements, we may re-platform applications to more suitable environments, refactor existing code to enhance performance and maintainability, or completely replace outdated systems with modern solutions.  

We break down data silos while ensuring all information is safely and securely stored while we develop the new architecture that better enables AI integration. In this stage, we also clean, normalise, and prepare data to integrate into the new system and make it usable for AI models.  

03 Data Migration 

We facilitate the applications' migration to a modern infrastructure—whether cloud-based, hybrid, or on-premises. Our team ensures a seamless transition with minimal disruption to operations, leveraging best practices in data migration and system integration to guarantee that all functionalities are preserved and enhanced. 

At this point, the modern systems at least match the legacy software in functionality but offer increased scalability, lower costs, and an enhanced user experience, and they can easily integrate with new applications. 

04 AI Design, Pilot, and Deployment 

Our Artificial Intelligence specialists, in collaboration with the client, select the AI model/s that would best meet the project's objectives. Various models, such as Machine Learning (ML), Deep Learning (DL), Large Language (LLMs), and more specialised solutions each have their strengths and weaknesses. As such, it's crucial to choose and prioritise the models that can scale with the business and create impact in the long term. 

To validate the future outcomes with stakeholders as soon as possible, our AI team focuses on creating Proofs of Concept (PoCs) for the most promising opportunities as soon as possible. This process is also invaluable in creating interest and encouraging users to start understanding the new functionalities and thinking of AI as a catalyst for broader innovation. 

Based on the PoC's results, we implement the most promising models into the ecosystem, closely managing and monitoring the integration to ensure a tight coupling between AI models and business workflows. 

05 Scaling and Training 

As we work through the roadmap and AI opportunity list, we introduce new Artificial Intelligence models or scale the existing integrations to new business areas, facilitating widespread change and enhancing operational efficiency. 

We also provide comprehensive training and support to ensure that the new system's users are fully equipped to utilise both the new application and its AI functionalities. 

Moreover, we encourage data literacy and AI understanding across departments and the creation of feedback loops. This step is crucial in ensuring not only efficient support from our team but also that business users consider future innovations and scalability opportunities to expand the organisation's competitive edge. 

06 Continuous Improvement and Support 

Our ongoing support services include troubleshooting, performance monitoring, and updates to keep the applications running smoothly. We are committed to helping our clients maximise the benefits of modernisation and AI models long after release by leveraging feedback and insights to continuously improve the software. 

Our long-term goal is to make sure that the new applications evolve as business needs change and technology evolves. 

Why CIOs and CTOs Should Prioritise Modernisation Over Short-Term Fixes 

IT leaders often face pressure to deliver quick AI wins. However, implementing AI without first addressing the legacy system challenge is a short-term fix that can lead to long-term problems or even outright failure.  

We have already reviewed the negative effects of building AI solutions on unstable foundations. Now, we'll focus on the benefits that CIOs and CTOs gain by adopting a structured approach and "set the stage" before diving into AI-powered innovation: 

Long-Term Cost Savings 

While modernisation requires upfront investment, it ultimately reduces costs: 

• Maintenance becomes more manageable and less expensive; 

• Functionalities become more reliable and scalable; 

• The risk of incurring fines due to outdated cybersecurity practices becomes much lower.

Companies that modernise before implementing AI solutions avoid expensive patchwork integrations, continuous fixes and technological bottlenecks, leading to better ROI. 

Future-Proofing the Organisation 

Despite the misconception that application modernisation projects focus on bringing the IT infrastructure to today's standards, a big part of the planning and work that go into modernising applications is to ensure that they can scale and integrate with future tools.  

As AI is a constantly evolving technology, a modern IT infrastructure ensures that the organisation doesn't run into new bottlenecks or roadblocks. 

Better Decision-Making and Competitive Advantage 

Modernised applications provide better data visibility, enabling AI to generate higher-quality insights. This leads to smarter decision-making, improved customer experiences, and enhanced operational efficiency.  

It's also important to consider stakeholder perception, especially with the first AI integration project. Lacklustre results may discourage the organisation from pursuing future AI-powered features, while better results may lead to more interest and buy-in for future endeavours. 

Build the Right AI Foundation with Modernisation 

Artificial Intelligence's potential for business innovation can't be understated, but it's not a catch-all solution to implement whenever.  

Organisations looking to leverage AI must first look at their IT infrastructure and ensure they have a strong digital foundation. Application modernisation ensures scalability, data accessibility, security, and agility, and that goes double when CIOs and CTOs are considering ways to leverage Artificial Intelligence. 

The first step towards AI-driven innovation is ensuring that the IT ecosystem can support it. Our strong tech teams have extensive experience with both legacy system modernisation as well as Artificial Intelligence adoption and are ready to support your organisation on their AI journey.