Integrating AI with legacy systems can be challenging for UK SMEs due to outdated technology, tight budgets, and limited in-house expertise. However, with the right approach, these hurdles can be managed effectively.
Key challenges include:
Solutions to consider:
One of the first challenges SMEs face when introducing AI is pinpointing where their older systems fall short. Many legacy platforms simply aren’t built to work with modern AI tools. They often lack APIs, rely on outdated protocols, and have rigid architectures that make integration a headache.
Compatibility issues can crop up in various areas - outdated operating systems, old databases, unsupported programming languages, or even hardware limitations. For instance, a legacy system might house critical customer data but offer no simple way to access it in real-time. This forces businesses to resort to clunky workarounds like nightly CSV exports or manual database dumps.
Another common problem is that these systems don’t have the processing power, memory, or storage to handle AI workloads locally. This means businesses often have to rely on cloud-hosted AI models, which then need to be connected to the legacy system through sometimes unreliable integration methods. Identifying these roadblocks is the first step toward creating a smooth and practical AI integration plan.
Once you’ve identified where the gaps are, there are several ways SMEs can work around these limitations without overhauling their entire system. Instead of a complete replacement - which can be both expensive and disruptive - SMEs can take a gradual, low-risk approach to integration.
One effective strategy is using middleware or integration platforms to bridge the gap between legacy systems and modern AI tools. Platforms like Zapier or MuleSoft act as translators, enabling communication between systems that otherwise wouldn’t work together, all without requiring major changes to the existing code.
For businesses with limited IT resources, batch data transfers can be a reliable alternative. Rather than trying to achieve real-time integration, companies can schedule regular exports - like weekly CSV files or database dumps. These can then be fed into AI models running separately. The AI processes the data offline, generates insights or predictions, and sends the results back to the legacy system through simple file imports or API calls. This method keeps things straightforward while still delivering value.
Another option is phased modernisation. Instead of replacing an entire ERP or CRM system, SMEs can focus on upgrading specific parts that are relevant to AI - such as reporting tools, customer-facing interfaces, or data pipelines. This step-by-step approach allows businesses to test AI capabilities in smaller, manageable chunks, proving their value before committing to larger investments.
Wingenious offers tailored solutions to help SMEs navigate these challenges. Our team conducts system audits to pinpoint compatibility gaps and create a roadmap that prioritises low-risk, high-impact AI initiatives. Using phased integrations and middleware solutions, we ensure minimal disruption to your existing operations.
Our AI Readiness Assessment identifies which platforms have APIs, where data is siloed, and which integration points can be leveraged. Meanwhile, our AI Implementation Planning service outlines practical strategies, such as batch data exports or middleware layers, to get you started.
For more complex challenges, our Platform Integration consultancy provides hands-on support. Whether it’s wrapping legacy functions with lightweight APIs or establishing secure cloud connections for AI workloads, we help design solutions that work for your specific needs.
As Stella Davis from a fashion ecommerce brand shared: "We started with some basic low effort, high gain automations to test the water. Now we have two more projects on our Wingenious AI roadmap."
This step-by-step approach - starting small, proving value, and scaling gradually - allows SMEs to overcome technical incompatibility while keeping their core systems stable. It’s about making progress without unnecessary risk.
Even after you've tackled the technical aspects of integrating AI with your legacy systems, another major issue often emerges: the state of the data itself. Legacy systems are notorious for holding years - sometimes decades - of messy, inconsistent, and poorly maintained records. For many SMEs, this means their data is far from AI-ready.
One of the biggest culprits? Data silos. Picture this: sales data stored in an outdated on-premise database, inventory details scattered across spreadsheets, and customer information locked in a separate CRM tool. When these systems don’t communicate, AI models are left working with incomplete information. The result? Forecasting errors, missed opportunities, and recommendations that fail to capture the full picture of your business.
Then there’s the headache of inconsistent formats. One system might record dates as DD/MM/YYYY, while another uses MM/DD/YYYY. Product codes, customer names, and even measurement units often vary across departments. Without standardisation, AI struggles to interpret the data, and errors during integration can slash model reliability by as much as 30–50% in predictive tasks.
On top of that, poor-quality data compounds the problem. Duplicate records, missing values, outdated entries, and human errors build up over time in legacy systems. AI models trained on such "dirty" data produce unreliable outputs, eroding trust in their predictions. A 2024 Zapier survey revealed that 29% of enterprises cite data quality issues as a major roadblock to AI adoption. For SMEs with tighter budgets and smaller teams, the impact can be even more severe.
It all boils down to this: AI is only as good as the data you feed it. If your legacy systems are riddled with gaps, inconsistencies, and errors, no algorithm - no matter how advanced - can deliver reliable insights. Cleaning up your data is not optional; it’s the bedrock of any successful AI initiative.
The good news? You don’t need a massive overhaul to get started. A targeted approach to data preparation can go a long way.
Start with a data audit. Map out critical data sources, such as sales histories, stock levels, and customer transactions. Take a sample - around 10–20% of records - and check for issues like missing values, duplicates, and errors. Pay attention to outdated information and inconsistencies in naming conventions, as these can skew AI predictions.
Focus your efforts on high-value data sources. Instead of trying to clean everything at once, prioritise the datasets that directly affect revenue or operations. For instance, customer behaviour logs or sales orders are often more impactful than old audit trails or rarely accessed legacy records. Remember, a small percentage of your data - typically around 20% - often drives the majority of AI value.
Next, clean and deduplicate your data. Use techniques like approximate matching to combine near-identical records (e.g., "Jane Doe" and "J. Doe"). Correct obvious errors, such as invalid postcodes or nonsensical dates, and fill in missing values where possible. If certain records are incomplete, flag them to prevent them from contaminating your AI training.
Don’t forget to standardise key fields. Use consistent formats for dates (e.g., DD/MM/YYYY), currency (GBP), and other critical data points. Employ ETL (extract, transform, load) processes to convert fragmented data into structured formats like CSV or JSON. Even a simple data dictionary - outlining what each field represents and who manages it - can greatly enhance the clarity and usability of your datasets.
These steps set the stage for expert input, helping you maximise the potential of AI tools.
Wingenious offers tailored services to help SMEs overcome legacy data challenges and make their data AI-ready - all without disrupting day-to-day operations.
Our Data Cleaning and Deduplication service tackles messy legacy data head-on. We eliminate duplicates, standardise inconsistent formats, and correct errors, ensuring your AI models are trained on accurate and reliable information from the start.
For businesses struggling with fragmented data across multiple systems, our Data Transformation service brings everything together. We handle the technical heavy lifting - normalising schemas, aligning data types, and converting legacy formats into unified, AI-compatible structures. All this is done while adhering to UK-standard formats, so your data is ready for modern tools without the need for a complete system overhaul.
Once your data is cleaned and organised, our Actionable Data Dashboards service helps you visualise the results. These dashboards provide a clear, real-time view of key metrics derived from your newly prepared data. They not only build confidence in the quality of your data but also help identify high-value sources for future AI projects.
As Martha Jones, founder of an organic product business, puts it: "Working with Wingenious has been a game-changer for our company. Their simple AI solutions have given us a significant competitive advantage in the market."
For small and medium-sized enterprises (SMEs), integrating AI often feels like an uphill battle - not just because of technology, but because of tight budgets and limited in-house expertise. Research highlights these two factors as the biggest hurdles to AI adoption, even more than technical challenges. Many SMEs are already stretched thin maintaining outdated systems, leaving little room to invest in AI tools, infrastructure, or the specialised staff needed to make it all work.
AI projects demand significant upfront costs. From software and cloud services to integration efforts, the expenses quickly add up. Then there are ongoing costs like maintenance, updates, and training. Add to this the challenge of hiring experienced data scientists or machine learning engineers, whose salaries are often out of reach for smaller firms. For many SMEs, securing additional financing to cover these costs is simply not an option.
The skills gap further complicates things. Without internal expertise, SMEs may struggle to determine whether their existing data is usable, select the right tools, or integrate AI effectively into their workflows. IT teams, already swamped with everyday tasks, often lack the specialised knowledge to evaluate vendors or oversee AI projects. This can lead to initiatives that either never get off the ground, stall after initial testing, or fail to deliver meaningful results.
A practical way forward is to start small. Focus on a specific, manageable use case - like automating invoice processing or prioritising emails - that can deliver quick, measurable results. This phased approach allows SMEs to demonstrate value with a modest pilot project before committing to larger investments. Look for solutions with clear payback periods, ideally within months, and consider using subscription-based, off-the-shelf AI tools. These tools turn hefty upfront costs into predictable monthly expenses. Once these initial steps succeed, the next logical step is to bring in external expertise.
Building an in-house AI team is expensive. Between salaries, benefits, and training, the annual cost of a team of data scientists, machine learning engineers, and architects can easily exceed £200,000. For most SMEs, this simply isn’t feasible. Even if the budget allows, hiring is often slow and uncertain, and many SMEs don’t have a steady stream of AI projects to justify permanent roles.
This is where external AI consultancies come in. By hiring specialists on a project basis, SMEs can access expert guidance for strategy, feasibility analysis, and integration without the financial strain of full-time hires. Consultants bring an unbiased perspective, up-to-date knowledge, and experience with the latest tools. For SMEs dealing with complex legacy systems, this outside expertise can help avoid costly mistakes and focus resources on areas with the most potential for impact.
External consultants are especially valuable in the early stages of AI adoption. They can help map out processes, assess technical feasibility, design pilot projects, and set realistic goals. As AI becomes more integrated into day-to-day operations, SMEs can gradually build internal capabilities. However, for initial projects, consultancy is often a more cost-effective and lower-risk option than creating an in-house team from scratch.
To ensure long-term independence, it’s important to structure consultancy engagements around knowledge transfer. This includes clear documentation, practical training sessions, and collaborative workshops where your team learns to manage AI tools. Time-limited projects with defined deliverables - such as a strategy document, prioritised use cases, or a training plan - can empower your team to continue improving AI solutions after the consultancy ends.
Wingenious offers flexible, tailored solutions to help SMEs overcome these challenges. Their modular services are designed to provide targeted, time-limited support, helping businesses build AI capabilities without the burden of ongoing employment costs.
As Briana Jones, a sales manager, shares: "Creative, ingenious and highly effective CRM automations have recovered time spent on mundane tasks allowing my team to focus on sourcing and converting leads."
When it comes to adopting AI, technical readiness is only part of the equation. Cultural resistance can often be a bigger hurdle. Employees who’ve spent years working with familiar legacy systems might see AI as a threat rather than a tool for improvement. Common concerns include fear of job loss, anxiety about learning new tools, and a general preference for sticking with what’s familiar.
The solution? Involve your teams early. Instead of presenting AI as a top-down directive, host workshops with frontline staff to map out existing workflows. Identify repetitive, time-consuming tasks that AI could take over. By clearly explaining that AI is there to handle the mundane work - freeing up employees to focus on more meaningful, high-value tasks - you can start building trust from the ground up.
For many non-technical users, especially those used to older systems, trusting AI can feel like a leap of faith. But trust grows when AI outputs are transparent, easy to understand, and verifiable. Simple measures like showing confidence scores alongside predictions, letting users review the data behind recommendations, and involving humans in decisions with higher stakes can go a long way in building credibility.
Pilot projects are another great way to showcase AI’s potential. Start small by tackling a specific, measurable issue - like cutting down on manual data entry errors or speeding up email response times. Track metrics such as time saved, error rates, or customer satisfaction to demonstrate value. Running AI alongside existing processes allows employees to compare results firsthand, helping them see the benefits clearly. Sharing these pilot outcomes using straightforward dashboards or brief internal case studies - complete with honest discussions about any challenges - can further show that AI is more than just hype; it’s a practical, evidence-backed tool. These early wins pave the way for structured change management and training.
Introducing AI successfully isn’t just about the tech - it’s about people. That’s where Wingenious steps in, offering tailored support to help SMEs address resistance and build trust across their teams. Their Introduction to Artificial Intelligence service breaks down AI concepts into simple, relatable terms. Using examples tied to an organisation’s current systems and processes, this training helps reduce fears by showing what AI can - and can’t - do. It also clarifies responsibilities like data privacy and highlights when human oversight is essential.
For more hands-on support, the AI Tools and Platforms Training programme offers role-specific workshops. These sessions let employees experiment with AI tools within the context of their daily tasks, helping them build skills and confidence without overwhelming them with technical jargon. Meanwhile, the Process Optimisation consultancy takes a closer look at current workflows. By identifying low-risk opportunities for AI and creating detailed change management plans - including templates, pilot designs, and measurable goals - this service ensures a smoother transition to AI-driven processes.
Older systems were designed with a focus on physical security, often neglecting the digital protections we now consider essential. Features like modern encryption, strong authentication, and detailed logging were typically absent. When these legacy systems connect to cloud-based AI, sensitive data can extend beyond internal networks, creating new vulnerabilities. This shift introduces more entry points - such as APIs and third-party services - making breaches more likely.
Small and medium-sized enterprises (SMEs) face particular challenges here. According to a Zapier survey, 78% of organisations struggle to integrate AI into their tech infrastructure, with 27% identifying IT bottlenecks as a key issue. For instance, legacy databases that rely on unencrypted protocols can unintentionally expose sensitive data when linked to AI APIs. Outdated authentication systems only compound the problem, potentially allowing unauthorised access. Given that UK GDPR fines can reach up to 4% of global turnover, even a minor breach could result in severe financial penalties.
These risks highlight the need for strong governance to protect data effectively.
For SMEs, implementing AI governance doesn’t require a large-scale compliance team. The first step is to categorise your data - determining whether it is public, internal, or sensitive. This helps establish what information can safely be shared with external AI tools. Role-based access and multi-factor authentication should also be enforced at key integration points.
A cautious, phased approach works best. Start with low-risk, anonymised datasets, such as sales figures or operational statistics. This allows you to test AI integrations without exposing critical data. Over time, you can refine governance processes while gaining confidence in your system. Simple but effective measures like maintaining audit trails, conducting basic Data Protection Impact Assessments (DPIAs) for higher-risk cases, and keeping privacy notices up-to-date ensure compliance with UK GDPR.
To support these governance efforts, Wingenious offers practical tools and services for secure AI adoption. Their AI Readiness Assessment identifies potential vulnerabilities and gaps in UK GDPR compliance, providing prioritised recommendations to strengthen your data controls and kick-start classification efforts.
The Workflow Tracking service helps SMEs monitor AI data interactions by maintaining detailed audit trails. Meanwhile, Workflow Continuity Solutions ensure a secure transition when linking legacy systems with AI. For example, an SME retailer could use these services to track how ecommerce data flows through AI analytics pipelines, flagging any anomalies for review.
Bringing AI into legacy systems doesn’t have to mean breaking the bank or tearing everything down. With 78% of organisations reporting challenges in AI integration - from skill shortages to data quality and infrastructure issues - it’s clear that tackling this process can feel daunting. However, SMEs can make real progress by taking a thoughtful, phased approach that minimises risk and keeps costs manageable.
Start by pinpointing areas where AI can make a noticeable difference, like saving time, cutting down errors, or increasing revenue. Take stock of your current systems and data, and focus on what’s feasible now instead of waiting for everything to be perfect. A small pilot project is a great way to get started - think along the lines of an AI-powered report, a tool for automating workflows, or an enhanced helpdesk system. Set clear metrics for success, such as hours saved each week or a reduction in errors, and use simple integration methods like APIs or CSV files to keep things affordable. Monitor the results closely and make adjustments before scaling up.
Leadership backing is essential, as are basic governance measures and ongoing training, to ensure trust and alignment with business goals. By positioning AI as a tool to handle repetitive, low-value tasks and support your team rather than replace them, you can ease concerns and foster buy-in. Involving employees early in the process - whether in identifying pain points or testing solutions - will further build confidence while keeping costs predictable in pounds sterling (£). These steps lay the groundwork for seeking targeted external expertise when needed.
If you’re ready to act on these steps, Wingenious can provide the guidance and training needed to make your AI adoption smooth and secure. Their AI Strategy Development service helps you evaluate your legacy systems, identify impactful and low-risk opportunities, and craft a phased plan that matches your budget and capacity. Through their AI Readiness Assessment, they highlight vulnerabilities and compliance gaps while pointing out areas for quick wins, giving you a clear and practical starting point.
For teams grappling with skill gaps or resistance to change, Wingenious offers Introduction to Artificial Intelligence and AI Strategy Workshops. These hands-on sessions help your team build the confidence and skills needed to embrace AI. Their AI Implementation Planning service ensures that your workflows and data preparation are guided by a solid strategy, avoiding the pitfalls of ad-hoc experimentation. This consultancy-first approach enables you to modernise your legacy systems with AI while keeping your options flexible and costs under control.
For SMEs, the first step is to take a close look at their existing systems. This helps pinpoint where AI could bring improvements and highlights any potential obstacles to integration. Understanding these compatibility issues upfront can make the transition smoother.
Creating a custom AI strategy is the next important move. This should focus on specific areas where AI can be integrated to support the business’s goals. Working with experts like Wingenious can be a smart choice, as they can help craft tailored, data-driven solutions that fit seamlessly into current workflows while keeping disruptions to a minimum.
Adopting a phased approach is also key. By starting with smaller, more manageable AI implementations, SMEs can modernise their systems step by step, making it easier for teams to adapt to the new technologies.
Improving the quality of data in legacy systems is a crucial step for integrating AI effectively. Begin with thorough data audits to pinpoint errors, inconsistencies, and any missing details. Creating consistency is key, so standardise data formats and definitions across all systems.
Next, focus on data cleansing - eliminate duplicates, fix inaccuracies, and address gaps in the information. To maintain this quality, establish clear and enforceable data governance policies. These policies not only safeguard data integrity but also ensure compliance with relevant regulations.
Finally, make data quality an ongoing priority. Regularly monitor and validate your data to quickly resolve any emerging issues, keeping your systems ready for AI advancements.
Small and medium-sized enterprises (SMEs) can address budget constraints and skill shortages by crafting an AI strategy that focuses on affordable, impactful solutions. A great starting point is automating repetitive tasks. Not only does this free up valuable time, but it also helps minimise human error. Plus, these types of solutions are often easy to implement and don’t require advanced technical expertise.
To tackle skill gaps, look into training your current team in AI tools and techniques. Offering AI training programmes can ease the adoption process while equipping your staff with the knowledge to make the most of AI technologies. If you need extra guidance, consulting services can provide tailored advice, ensuring your AI efforts are aligned with your specific business objectives.
Our mission is to empower businesses with cutting-edge AI technologies that enhance performance, streamline operations, and drive growth. We believe in the transformative potential of AI and are dedicated to making it accessible to businesses of all sizes, across all industries.
