At the same time, it is worth noting that the perception of AI varies depending on the audience. For a non-technical person, AI may simply be associated with a conversational tool like ChatGPT; for more technically aware stakeholder, it might mean the application of large language models across business processes; while others may see it as a way to summarise meetings, emails, or documents.
This diversity of perception also influences how different groups interpret opportunities and risks connected with AI adoption.
Why skipping IT modernisation sinks AI ROI?
Why skipping IT modernisation sinks AI ROI?
Skipping IT modernisation undermines AI capabilities because legacy systems and outdated technologies lack the speed, scalability, and integration required to support advanced algorithms, data pipelines, and modern AI tools.
Organisations often encounter poor data quality, bottlenecks, unreliable outputs, and inflated costs – eroding the very return on investment that comes from implementing AI effectively. In the current AI revolution, efficiency and high quality data are not just nice-to-have; they are foundational.
What counts as 'IT modernisation' for AI?
IT modernisation for AI goes beyond upgrading infrastructure; it’s about creating a digital backbone that makes AI systems scalable, reliable, and business-ready, enabling organisations to leverage AI effectively across operations.
Key pillars include:
Enterprise data platforms
Enterprise data platforms centralise, clean, and govern data, ensuring models are trained on accurate, consistent information for trustworthy insights, reduced bias, and better AI capabilities.
DevOps practices
DevOps practices introduce automation, collaboration, and rapid iteration, ensuring AI systems evolve in line with business needs and modern technologies. It also covers MLOps (Machine Learning Operations) which standardise model deployment, monitoring, and continuous improvement, maintaining accuracy, reliability, and reducing the risk of errors from human oversight.
Modernisation is not just about technology – it is about preparing the organisation to leverage AI in a fundamentally different operational reality.
Legacy systems stack problem: latency, data silos and brittle integrations
Legacy IT stacks and legacy systems are a major drag on AI success. Outdated servers and rigid databases introduce latency, slowing model training, analytics, and real-time decision-making. Data silos prevent AI from accessing a unified enterprise dataset, resulting in skewed or unreliable outputs.
Brittle integrations mean every new AI tool or update risks breaking existing workflows, creating costly fixes and delays.
Without addressing these foundational issues, organisations struggle to scale AI systems, stifling both agility and ROI.
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The hidden cost of technical debt on AI performance and reliability
Beyond financial costs, technical debt carries strategic consequences: AI credibility declines, momentum stalls, and organisations risk falling behind competitors in business transformation.
It is therefore of utmost importance to reduce technical debt at an early stage to ensure AI systems can scale reliably, maintain high-quality outputs, integrate smoothly with legacy systems and existing processes, and allow software development teams to focus on innovation rather than firefighting.
Early mitigation of technical debt improves system stability, enhances data quality, and strengthens governance, creating a resilient foundation that maximises the return on investment from implementing AI initiatives.
Building the right ecosystem for Artificial Intelligence
A common question from business leaders is, ‘Which AI should we implement?’. The more important question however is, ‘How do we build the right ecosystem for our business to thrive?‘.
Successfully preparing for AI begins with foundational readiness. Before introducing AI agents, organisations must ‘clean the house’ – organising data, processes, and infrastructure to reduce the risk of compounding mistakes.
AI technology is not deterministic: outputs can vary widely, making scale difficult without strong foundations. Even if adoption occurs unevenly across departments, a solid foundation ensures resilience, smoother integration, and long-term competitiveness.
Equally important is a total metrics approach. Traditional KPIs may not apply, especially when AI replaces or changes human tasks. Organisations need low-level metrics that track process readiness, data literacy, and integration health, alongside higher-level business outcomes.
By measuring both progress and safeguards – knowing what to adjust if expectations are not met – companies can guide AI adoption safely and effectively, even in the face of early failures.
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Data readiness first: governance, quality and lineage for trustworthy AI adoption
AI cannot succeed without high-quality, well-governed data. Organisations must:
- Implement data governance frameworks to define ownership, security, and compliance.
- Ensure data quality, addressing duplication, inconsistencies, or gaps that can distort model outputs.
- Track data lineage, understanding where data comes from, how it is transformed, and where it flows, building transparency and trust.
Without these foundations, even the most advanced AI systems risk producing unreliable or biased results, threatening adoption and confidence.
Security, privacy and compliance: model risk management in regulated sectors
In regulated industries like finance, healthcare, and government, AI adoption hinges on strict attention to security, privacy, and compliance. No matter which industry you’re in, sensitive data must be protected, and privacy standards rigorously maintained.
Organisations also need model risk management, monitoring for bias, drift, or unintended behaviours that could trigger regulatory penalties or reputational damage. Embedding robust controls into the AI lifecycle allows innovation while ensuring adherence to legal and ethical obligations.
Operating model shift: platform teams, product thinking and FinOps for AI
Scaling AI successfully requires more than technology upgrades; it demands a fundamental shift in the operating model that aligns teams, processes, and resources with the demands of AI.
- Platform Teams provide shared, reusable infrastructure and services that accelerate AI delivery across business units. By centralising capabilities, platform teams reduce duplication, integrate with existing processes, and allow teams to focus on building AI systems rather than reinventing the underlying stack.
- Product Thinking positions AI initiatives as evolving, outcome-focused solutions rather than one-off projects. It encourages continuous iteration, ensuring AI aligns with business goals while accommodating changes in software development cycles and organisational priorities.
- FinOps Practices introduce financial accountability to cloud, AI, and platform investments, ensuring costs are optimised and tied directly to business value. This approach helps organisations manage spending on legacy systems, modern AI tools, and high-performance infrastructure required for scaling AI capabilities.
Together, these shifts foster organisational agility, enabling AI systems to maximise impact while maintaining control over complexity, cost, and integration with existing processes – ultimately turning AI from a pilot experiment into a reliable driver of business transformation.
Buy, build or partner? Where to prioritise modernisation for fastest value
When modernising for AI, organisations face a critical buy, build, or partner decision.
Quick wins – like adopting cloud services or pre-built AI tools – deliver rapid value and proof points. Strategic bets, such as building bespoke data platforms or custom MLOps pipelines, create the foundation for long-term competitive advantage. Prioritising investments means balancing immediate impact with sustainable capability, ensuring early successes fund deeper, transformative initiatives.
AI is a multiplier, amplifying both strengths and weaknesses in an organisation. Without modernised IT, clean data, and aligned operations, AI systems risk magnifying existing inefficiencies. By investing in foundational readiness – through IT modernisation, governance, security, and an evolved operating model – organisations position themselves not just to adopt AI models, but to thrive in a fundamentally transformed business landscape.
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