Legacy IT infrastructure is often the single biggest barrier to AI adoption in small to midsize enterprises (SMEs). Older software isolates critical business data, preventing the cross-functional visibility that AI needs to operate. AI applications also demand high-speed processing and flexible compute power that older hardware cannot provide.
A strategic approach can help SMEs overcome these roadblocks without breaking the bank. By focusing on data quality and identifying low-effort, high-impact use cases, SMEs can prove the benefits of AI in their operations. They can then move toward centralizing operational data and investing in AI-powered software that helps them gain real business value from AI.
Poor Data Quality and Fragmentation
AI operates on a “garbage in, garbage out” principle. It cannot generate accurate insights from flawed foundations.
Older applications often store data in proprietary structures that external AI tools cannot interpret. Data is often siloed in separate databases. Employees must manually copy and paste data across systems, creating operational bottlenecks.
Outdated entry forms lack strict formatting rules, resulting in mixed phone numbers, missing dates and duplicate profiles. Discrepancies between legacy software and standalone cloud apps make it impossible to establish a single source of truth. Training an AI model on inaccurate data causes it to make inaccurate forecasts.
Inconsistent manual processes stop automation initiatives before they can launch. Employees often create undocumented shortcuts to bypass flawed legacy software. When every employee performs the same task differently, it is impossible to automate.
Pushing the Limits of Legacy Infrastructure
Modern AI applications demand high-speed processing and flexible compute power that old hardware cannot provide. Local servers lack the specialized graphics processing units (GPUs) required to run advanced local models efficiently. Fixed infrastructure lacks the scalability needed to handle the sudden data spikes created by AI.
Security and compliance create legal and financial risks. Unsupported operating systems contain unpatched vulnerabilities, making data targets for ransomware during AI transfers. Legacy software often operates on an all-or-nothing permission model, failing to restrict sensitive data from AI training loops. Older systems cannot track data lineage, making it difficult to comply with modern privacy laws.
Connecting modern AI to rigid, outdated technology drains limited SMB financial resources. SMBs must hire software developers to build custom code wrappers just to move data out of old systems.
Developing an AI Strategy
Many SMEs don’t know where to begin, leaving them trapped in pilot projects. The best approach is to evaluate existing systems and software before buying anything new.
The first step is to document common daily tasks and identify where employees manually type or move data. Business leaders should then pick one high-volume, simple process to focus on.
Stakeholders should identify every tool, app and spreadsheet used for that one process. The IT team should determine if each tool has a way to connect to automation platforms and note any older software that doesn’t sync with outside tools.
SMEs should not try to build a custom AI app at this stage. AI features built into tools such as Microsoft 365 provide a great starting point for automating tasks to see if AI actually saves time or improves accuracy.
Moving to Full-Scale AI-Powered Operations
When it’s time to invest in a standalone or customized AI tool, SMEs should follow a “data-first, AI-second” approach. Managers should allocate time and resources to clean up old data and mandate that data be entered in standardized formats. SMEs should also deploy automated validation software that rejects poorly formatted entries at the point of input. They should also make “AI compatibility” mandatory for any new software procurement.
Operational data should continuously stream into a centralized data warehouse or data lake. It is critical to implement role-based access control to dictate exactly which employees and AI agents can see specific data points.
A pivotal decision is choosing where data and AI workloads will live. For most SMEs, cloud-native environments are the catalyst for AI success, though specialized niches still require on-premises solutions.
How Verteks Can Help
Verteks has helped SMEs in a wide range of industry sectors operationalize AI. Let’s discuss your business needs so we can identify potential use cases and prepare your systems and data to support AI-powered applications.
