In the contemporary business landscape, the combination of Artificial Intelligence (AI) and Business Intelligence (BI) working in concert has the potential to make every action more data driven, massively enhancing the productivity and effectiveness of workers. The implementation of AI in this way is revolutionising the way employees use and interact with data, and its adoption will propel early adopters far ahead of their competitors.
The Evolution of Business Intelligence
BI has long been at the forefront of the data-driven decision-making trend. However, the advent of AI is not merely enhancing service delivery; it is challenging the very foundations of conventional data handling methods and software development. Where BI represented the initial wave of data delivery, AI is a transformative force that is already reshaping the software landscape.
Static, one-size-fits-all dashboards and business reports were the norm for a long time. Although traditional BI solutions started to gradually incorporate more ways to tailor the experience, software developers were hitting the limits of what they could customize.
Typically, interface customisation was hard-coded, and based on fixed user profiles that required weeks of developer time to fine tune. However, with AI it is now possible to make interfaces much more tailored to the user with highly accurate personalisation that is much more granular than it ever could be if built using traditional software development methods.
This is because AI has changed the game when it comes to data analysis. Previously, the role of analysing data was the domain of specialist teams who would interpret vast datasets and convey their insights to decision-makers. This process was not only time-consuming, but also bottlenecked by the availability and expertise of the analysts.
BI solutions offered some of that functionality at a user level but it was a linear progression. Users still needed knowledge of and access to specialized BI tools. Thanks to AI, this progression has led to an evolution that is exponential. Today, AI interfaces are capable of delivering highly accurate insights directly to the end user within their flow of work, bypassing the need for separate tooling, human intervention and hyper-personalising the output.
Defining Hyperpersonalisation
Hyperpersonalisation is a significant leap forward for BI, and AI is enabling it. Previously, users had limited customisation options that typically revolved around basic templates, sliders, and user settings, each demanding substantial development resources. Now, AI can facilitate dynamic customisation that extends beyond mere visual adjustments to include things like the frequency of dashboard refreshes, adaptive palettes for colour blindness, and even previously unattainable language options.
These language customisations are not just regional dialects or a wider pool of languages, but written outputs that can be tailored to the education level of the reader so that the data isn’t just being served to the end user ‘as is’, and is converted into the most understandable format. For example this might be an interactive graph, or text, depending on the context.
From a developer’s perspective, AI also enables a more nuanced approach to interface management. Developers and users alike can now determine which interfaces they need to give live updates and which ones they can access upon request. This level of control is pivotal in optimising the user experience and democratising the power of data to enable better, faster decision making.
Smaller Teams, Bigger Leaps
AI presents a golden opportunity for smaller teams to technologically leapfrog established market players. So far, AI is not replacing jobs, but accelerating them, particularly in software delivery. It is a technology that has arrived at the right time. MACH architecture (Microservices, API first, Cloud Native and Headless) are increasingly becoming the norm in software and this architecture makes it relatively straightforward to build AI-accelerated components and fit them into a larger tech stack.
Headless and API first are the main two aspects that lend themselves to AI. Providing the ability to match graphics to company branding via a headless design philosophy enables SaaS vendors to sell white glove services with far less developer time required because the data can be plugged into an existing front end. Similarly, APIs make it possible to connect various AI services without vendor lock in. As proprietary models become more common for businesses, the API can be switched to a different model as required without excessive rebuild time.
The result is that businesses that have a more integrated, closed solution have to do more work to integrate AI, while smaller teams, with fewer legacy systems to incorporate can be agile. For product delivery this results in teams that can quickly compose and ship bespoke solutions in a matter of days, or even hours.
The Agentic Frontier
The concept of agentic technology represents the next frontier where AI operates independently of human oversight. This presents a proportionally higher risk, as it removes the human from the loop. In the realm of BI, the technology is not yet mature enough to fully replace human workers; instead, it serves to augment their capabilities. Building reports in a matter of hours and then automating that reporting process is entirely within the realm of current AI technology and it will only become more powerful over time.
The integration of AI into BI tools is creating a new tier of BI applications. This real intelligence is not only accelerating decision-making processes but also personalising the user experience to an unprecedented degree. As AI continues to evolve, it promises to redefine the landscape of BI and analytics for good.
- Data & AI
- Digital Strategy
