By Satish Thiagarajan, founder of Brysa
AI is the most exciting development most SMBs have faced for decades. The potential for innovation, prediction, and personalisation, the never-ending hype. Everyone wants a piece of it. But many simply aren’t prepared for what that really means: groundwork. Without strong systems and clean data, AI simply can’t deliver on its promise. Investment is wasted and businesses fail to reach their full potential. Which is why the sensible path to AI for SMBs begins with getting the foundations right.
AI and where SMBs are going wrong
The most common AI mistake SMBs make is in adopting tools before fixing their underlying systems. With AI widely positioned as a fix-all, it’s easy to understand why this happens. But without a strong foundation of clean, unsiloed data, connected systems, integrated workflows, defined goals, and a clear adoption strategy, AI simply can’t work. Any work it does produce will be ill-informed and inaccurate. It’s time for a new approach.
Understanding the value of systems
Every business new to AI almost always puts the focus on finding the “best” AI tools. Whether it’s a chatbot, predictor, or generative tech. But the reality is that success is far more likely to hinge on the system that supports the tool rather than the sophistication of the tool itself. Because AI doesn’t operate in isolation; it relies on the systems that feed it. With that in mind, the clever business begins by strengthening the foundations that its AI tools will be built upon, rather than obsessing over the AI model it will eventually onboard.
The foundational pillars of successful AI adoption
If you want to ensure the success of an AI adoption, there are three core areas that you need to have in place.
Integrated systems
AI needs data. That’s what makes it work. Too often, over time, a business’ data becomes siloed. Each department uses its own software, systems, and processes. Communication is made in a variety of ways. And data and insights become segmented. Implementing a CRM, like Salesforce or Microsoft Dynamics 365, creates a centralised, interconnected system that enables data to flow seamlessly between departments. And creates an information hub for your AI tools to feed on,
Clean, structured data
Without strong data hygiene, your AI system will never be accurate. So, standardise your formats, clean the data you have, remove duplicates, and ensure that all data is accessible.
Automated workflows
When you automate repetitive tasks, you not only ensure that your AI system has access to a consistent flow of data, you benefit from freeing your team to focus on more important, human-centred work.
Together, these pillars provide a foundation that enhances operational efficiency while amplifying the potential value of AI. And they support each other, too. Strong clean data builds better predictions and avoids false starts. Integrated systems, feeding into a versatile CRM, create a unified feedback loop that can constantly improve your business. While automated workflows ensure that insights are turned into consistent actions, rather than being overlooked. Together, this compounds the value that AI can bring to a business.
How to make your business AI-ready
This part starts with the adoption of a strong CRM. Then you can move on to executing an actionable plan that audits data quality, integrates disconnected systems, automates repetitive workflows, and establishes strong data governance, so customer data is unified, standardised, and reliable. From there, you need to define clear, measurable AI use cases, enable analytics and dashboards to assess readiness, and train teams in data and AI literacy to ensure meaningful adoption. However, knowing what to fix is only half the challenge; implementing these changes across your business can require deep technical and operational understanding, and this is where many SMBs call in external experts for support.
We usually recommend the following steps:
Workflow mapping and optimisation
Map how work actually happens across your teams. Take the time to identify bottlenecks, repetitive tasks, and manual handoffs that slow things down. Then, redesign these workflows enhancing clarity and scalability.
CRM implementation
Once your workflows are optimised, you’re ready to onboard your new CRM, creating your companywide operational hub. This not only supports your teams and new automated workflows, but allows you to eliminate fragmented tools, so you’re better able to support data-driven decision-making.
Data hygiene and integration
Finally, it’s time to audit, clean, and standardise your data. Once that’s happened, you’re ready to integrate it across your finance and service systems, creating what we like to call “a single source of truth” for your business. This will provide the basis for your new AI system.
It would be amazing if we could all just purchase “the best” AI tool for our businesses, like a plug-and-play piece of hardware, and let it do its thing. And maybe that will become a possibility further down the line; a self-implementing technology that cleans up legacy systems and gets to work for you. But for now, if you want AI to support your business, you first have to put in the systems to support it. For us, that means starting with the right CRM for your business, and ensuring that you have clean enough data to serve it.
