Artificial intelligence is rarely out of the headlines. Every week seems to bring another new tool, feature or prediction about how AI will transform work.
But for many small organisations, the reality feels quite different.
While headlines focus on the capabilities of AI, many businesses are still tackling more fundamental questions:
- Where would AI actually add value?
- Do we have the right data to use it effectively?
- How do we know if the outputs can be trusted?
- Where do we even start?
Recent announcements suggest things are beginning to shift. Rather than focusing solely on technology, there is growing recognition that practical skills, accessible data and good information management are what determine whether AI succeeds or fails.
The UK Government’s announcement of new practical AI training schemes in Barnsley is one example of this changing approach in that it’s helping people apply AI to real-world tasks such as job searching and interview preparation.
For smaller organisations, this is a welcome shift because the biggest barrier to AI adoption isn’t usually the tools themselves but everything that comes before them.
Why so many AI projects struggle
Research consistently shows that data rather than technology is the key obstacle to successful AI adoption.
AI systems rely on information being available, accessible and reasonably accurate. If customer records are incomplete, information is spread across multiple systems, or key knowledge exists only in someone’s inbox or head, AI has very little to work with.
If you were delegating tasks to a human assistant and they had a pile of incomplete paperwork, conflicting instructions and outdated information to go on, you wouldn’t get good results – the same goes for AI.
If the underlying data quality isn’t there the result is frustration, not transformation.
The growing focus on data integration
One of the most encouraging developments I’ve seen recently is a growing focus on making data easier to find, access and connect.
The UK Data Service has recently secured funding through Horizon Europe to develop AI-enabled tools and services that improve access to data and support integration across multiple repositories.
While this work is aimed primarily at researchers, it reflects a broader trend.
The conversation is moving away from “Which AI tool should we use?” and towards “How do we make better use of the information we already have?”
For small organisations, disconnected data is often one of the biggest obstacles to effective decision-making. Customer information may sit in a CRM, marketing activity in another platform, financial data elsewhere and valuable insights in spreadsheets or documents.
When information exists in silos, it becomes difficult to build a complete picture of what’s happening. AI can help connect dots, but only if the dots are visible in the first place.
Accessible evidence matters more than ever
Another challenge facing smaller organisations is access to evidence.
Large organisations often have research teams, analysts and specialist software. Smaller organisations rarely do. This can lead to decisions being made on instinct, assumptions or anecdotal evidence rather than reliable information.
The good news is that there is a wealth of publicly available data, research and evidence that remains underused.
Government statistics, sector reports, labour market intelligence, public opinion surveys and open datasets can all provide valuable context for decision-making. Yet many organisations either don’t know these resources exist or lack confidence in how to use them.
This is one reason why initiatives that improve access to data are so important. Better access doesn’t just benefit researchers. It helps democratise evidence and makes it easier for organisations of all sizes to make informed decisions.
The organisations that thrive in the age of AI won’t necessarily be those with the biggest budgets, but the ones that can find, interpret and apply evidence effectively.
Most people don’t need to become AI experts; they just need to become more confident AI users.
That means understanding how AI can help with everyday tasks such as:
- Summarising research
- Drafting content
- Analysing feedback
- Identifying patterns in data
- Supporting planning and decision-making
- Generating ideas and alternatives
The goal isn’t to replace the need for your expertise but use your expertise to become more productive.
What matters is knowing when and how to use it.
What smaller organisations should prioritise before investing heavily in AI
If you’re looking at expanding your use of AI, I recommend starting with your foundations.
Ask yourself:
- Is your data organised?
Can you easily access customer, operational and marketing information when you need it?
If not, improving information management may deliver greater benefits than purchasing another AI tool.
- Are your systems connected?
Are there opportunities to reduce duplication and improve data flow between the systems you’re using?
Integrated information creates a stronger foundation for both decision-making and future AI use.
- Do you have access to external evidence?
Are you making use of publicly available datasets, industry reports and government statistics.
Good decisions require an understanding of the wider market context.
- Are you confident using AI?
Focus on practical skills rather than technical expertise.
Help your team understand where AI adds value and where human judgement remains essential.
- Have you identified a real business problem?
AI works best when applied to a specific challenge. Start with a problem you want to solve, not a technology you want to implement.
The organisations gaining the most from AI are not the ones investing the most money but the ones investing time in understanding their information, improving data quality and building practical skills.
AI may be changing rapidly, but the fundamentals will stay the same:
- Good decisions need good information.
- Technology works best when it solves a real problem.
- Evidence is one of the most valuable assets any organisation can have.
AI was used in the curation and editing of content for this newsletter.