They Prioritize Strong Data Foundations
AI’s capacity to improve business performance and productivity is no secret. So, naturally, many companies are eager to keep on top of new tech. Yet, despite 82 per cent of businesses having already invested in AI strategies, over half of business leaders still aren’t sure how to use the technology effectively.
Getting AI Right Requires a Considered Approach
Getting AI right requires a considered approach. In other words, companies must identify the right use cases, the right AI tools for said use cases, and the right data to underpin models to unlock the technology’s full potential. Drawing on my years of experience working with government bodies, global corporations, and FTSE 100s, and what I’m hearing from leaders tasked with operationalising AI in their businesses, I’ve shared below the four approaches that the most promising AI projects have in common, to help other businesses leverage the tech securely and effectively.
They Prioritize Strong Data Foundations
Garbage in, garbage out; the phrase is well-known in the tech world. An AI model’s output depends on the quality of its input, so if you have poor quality or incomplete data feeding an AI model, you’re unable to get the most out of it. Less than half of data leaders say their organisations have the right data foundation for Gen AI.
The problem is that most companies’ digital infrastructure is made up of different, disjointed systems that don’t talk to each other. This means AI that is deployed in one area of the organisation is unable to access information stored in other systems, which is needed to generate the most accurate and relevant results.
They Start with: "What Would I Ask of AI?"
AI tools that summarise documents may be useful, but if this is the only way a business uses AI, they risk severely underutilising its capabilities. Instead of choosing tools based on their popularity or marketing appeal, AI project leaders who want to truly maximise the technology’s capabilities should think first about what they would ask of AI.
They Put Data Security First
Some AI, including popular tools like ChatGPT, are cloud-based. The issue with sending corporate data to a public cloud is that you’re potentially left open to loss of IP. Plus, many generative AI models are trained on the information inputted into them, which could put company data at risk. Even when employees aren’t directly sharing private company information, these tools can potentially start to learn about the company and its objectives through dialogues with staff asking work-related queries.
They Are Underpinned by Education
A sizeable 40 per cent of workers say they don’t know how to effectively use gen AI at work. This knowledge gap can severely hinder progress with AI, and means staff and the business miss out on productivity gains. It could also cause security risks. For example, if staff input sensitive data into public AI tools like ChatGPT.
Conclusion
The potential value of AI is growing as fast as the technology is advancing. Organisations that take a considered, business-led approach to AI will be able to truly reap its rewards.
FAQs
- What percentage of businesses have already invested in AI strategies?
82 per cent - What percentage of business leaders are unsure how to use AI effectively?
Over half - What is the main challenge with using AI in business?
Garbage in, garbage out; poor quality or incomplete data feeding an AI model - What is the key to maximising the technology’s capabilities?
Identifying the right use cases, the right AI tools, and the right data - What percentage of workers say they don’t know how to effectively use gen AI at work?
40 per cent
