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Artificial intelligence (AI) and business intelligence (BI) are siblings. They share a last name and the overriding goal in using them is to improve overall business processes; to find efficiencies, new solutions, and improve insights.

The last 24 months has seen an explosion of AI in the press and public knowledge because of the launch of large language models (LLM), such as ChatGPT or Google Bard, and text-to-image models such as DALL-E and Stable Diffusion. We’ve even seen AI create cover songs by artists who died long ago.

The broad public impact is matched by a similar impact in business too. According to McKinsey’s research across 63 use cases, the value of AI productivity increase has gone from 15% to 40%,making AI investments more than twice as valuable for businesses. The same research suggests that this is equivalent to $4.4 trillion in global corporate profits annually.

The potential for new forms of AI, such as generative AI, to be used in BI to create additional value is significant. Here’s several use cases to highlight the potential for this technology:

AI is capable of enhancing the decision-making process for customer-facing roles

The goal for AI has always been to replicate the decision-making process that a human would make; to replicate human intelligence. By replicating the decision-making process, AI can allow many business functions to alleviate themselves of decision making. In the C-suite this wouldn’t be sensible. Yet, in many customer-facing roles, such as customer and technical support, AI would be useful.

In customer and technical support roles, decision making is an almost constant activity. Often, it’s based on piecemeal information, or information stored across several systems. Many support roles are multi-line or multi-channel too, meaning agents juggle conversations with several customers at once.

AI can help remove the pressure from these agents. It can make low-level decisions on their behalf as calls are triaged. It can source data from the disparate systems, like CRM systems, and make decisions based on it. And it can help diagnose and guide customers through issues while agents speak to other customers.

Natural language processing can boost data literacy throughout organisations

Large language models (LLM) can process and create natural language quickly and easily. Humans can type sentences in day-to-day language and the programmes, such as ChatGPT, can “understand” it and respond just as a human would. It’s fair to say that if you weren’t told, you’d never know you were chatting with a human and not a bot.

The ability to “talk” with an AI in this way radically transforms the way employees can interact with their computers, and in turn how they can interact with datasets. For example, natural language processing has allowed AI assistants to analyse data from conversational prompts. Employees faced with large datasets and no formal training in querying data can instead ask their assistant, “What’s the largest account from the last 12 months and how much has it increased in that time?” As we’ve written about previously, this helps to boost data democracy, making data more accessible and useable, which also increases its value by putting it in the hands of more people.

Personalised dashboard and reports

As well as allowing users to query data in conversational language, AI can also allow users to code in conversational language. This is helping to boost the amount of no-code or low-code tools available for BI applications, which makes personalising BI systems easier and more common.

Employees can, for example, use natural language prompts to request an AI to create a dashboard KPI tracker or report without having to build it themselves. BI dashboards and reports have saved decision makers massive amounts of time. AI now allows them to customise the dashboards in an unprecedented way.

Automation and data processing

It’s estimated that up to 90% of organisations’ data is unstructured, with that number growing each year. While unstructured data typically needs to be structured to be made useful, AI can now analyse unstructured data sources to extract insights from it. That means vast amounts of new data, insights, and value can be unlocked from within your organisation. For example, thanks to the use of AI, organisations can unlock extra insights from vast databases of emails, call centre transcripts, audio, video files and more.

What’s more, AI is capable of automating many of the data processing functions that humans would have to perform, saving organisations considerable time. For example, rather than ‘tagging’ audio conversations with keywords and sentiments, AI can detect, tag, and analyse data from audio automatically.

For over 20 years, change++ has helped its organisations take advantage of the newest technologies available to enhance their business processes. Generative AI and machine learning are the latest developments, with transformative potential for all businesses and sectors. Get in touch to explore the possibilities for your business.