From dashboards to decisions - why Artificial Intelligence is the next Business Intelligence revolution

Cast your mind back to the early 2000s. Business Intelligence (BI) was having its time in the spotlight as every team wanted data at their fingertips. BI adoption skyrocketed as data hungry teams fought to utilise and make sense of siloed data - that is before IT teams stepped in to bring order to the chaos by centralising these tools and standardising processes to allow businesses to unlock value at scale.

Sound familiar? Fast-forward to today and we can see Artificial Intelligence (AI) following a similar path. But this time, the potential to transform businesses is even greater. AI is not just another new tool - it’s the natural evolution of BI which allows businesses to turn data into smart decisions. Unlike BI, AI does not require human interpretation and it can analyse and predict on its own. But, just like we saw with BI, success with AI is not guaranteed. 

While BI helped businesses to understand what happened, AI takes this one step further by telling businesses what to do next. But AI is only effective when used in the right way. This starts with knowing what AI should do for your business, which outcomes matter, and preparing your data accordingly. 

Understanding what AI should do for your business

Unsuccessful AI implementation stems from businesses jumping straight in before considering what it should do for your business. It’s easy to get distracted by the noise and implement AI for the sake of it, but real value can only be unlocked when you align AI’s capabilities with your wider business needs. And this should not be rushed.

Businesses must take a step back and take the time to fully understand where the pain points, opportunities and inefficiencies are within the organisation to identify the areas where AI can truly make a difference. This process should not be limited to one team or division - this activity should be business-wide to ensure a difference is felt across the organisation.

So before you rush in, take a step back and remember that AI isn’t a silver bullet - it’s a strategic tool which depends on how and why it’s being used. Start with the problem rather than the technology to build AI that delivers real impact.

Priming your data to ensure success

Once you have identified the opportunities within your business, the next step is to make sure your data is ready to solve them. And this is where many businesses struggle with 78% of businesses stating data readiness is the biggest barrier to unlocking value from AI.

This statistic isn’t surprising - AI is only as good as the data you feed it and if the data is unstructured, even the most advanced models will struggle to deliver what a business needs. Most businesses are sitting on a mess of data scattered across siloes - and if your input is a mess, your output will be too.

A good starting point for businesses is to understand what data you have, where it comes from and its intended use. To help businesses understand what data they have, they must enforce a dedicated data lineage and data change function. This involves tracking data through its entire lifecycle, and by creating a clear trail of this information, businesses can understand the data’s source and monitor any changes to ensure its Machine Learning (ML) model runs as smoothly and efficiently as possible.

Businesses should also leverage semantic modelling to improve the quality of their data. This process involves representing data in a way that accurately captures its source, which in turn allows you to understand its significance and intended use. This will give businesses a clearer picture of their data and allow them to use and process it efficiently which will strengthen their ML models.

These functions will provide businesses with a stronger and more reliable foundation for AI implementation.

Designing for outcomes, not just outputs

The true power of AI lies in its ability to deliver outcomes that are tailored to your unique business needs. Just as traditional BI tools had to be customised to fit each organisation’s data strategy, AI must also be shaped around your business.

Many organisations implement off-the-shelf AI solutions in a rush to capitalise on the AI hype, but this rarely results in the desired outcomes. This is where an agnostic approach to AI becomes crucial. 

By taking an agnostic approach to AI, businesses will not be tied to one specific model, platform, or provider. They will be free to plug and play different models based on what delivers the best results for each specific use case. This enables businesses to deliver more tailored and effective solutions as they will be using the best tool for each job. And this flexibility is what turns AI into the new BI.

The next generation of BI is here, but it won’t succeed unless it is implemented with purpose, fueled by clean data and moulded around business’ unique use cases. By focusing on outcomes, adopting an agnostic approach to AI and building strong data foundations - businesses will lead the way into our AI-enabled future. Just like with BI, the businesses who adopt AI successfully won’t be the ones that adopt it the fastest, but those who adopt it the most strategically.