Integration Impasse

With CIOs under extreme pressure to enable AI and generative AI capabilities, they’re also scrambling to eliminate data silos. After all, AI needs data – lots of it – and with so much of that data locked in inaccessible silos, AI won’t be as effective.

Recent industry surveys indicate that about half of CIOs are prioritizing data platform overhauls to provide a unified data platform. The solution seems obvious – break down those silos to enable business growth. With all the data in one place and easily accessible, IT can innovate faster and unearth more insights that will drive revenues and open up new opportunities.

Unfortunately, data transformation initiatives often span multiple years and multi-millions of investments, creating tension with the pressing need to leverage AI, particularly generative AI, for immediate competitive advantage. More than 60% of companies surveyed earlier this year by Bain & Company named generative AI as a top three priority and 87% were already developing, piloting or deploying it. 

They fear competitors who deploy generative AI faster than they do will gain competitive advantages that the enterprise may never be able to match. Employees will also apply pressure to adopt generative AI. After all, the rise of generative AI is creating new expectations among everyday users, following a growing demand for natural language interfaces for consumer AI assistants like Siri or Alexa. And, of course, enterprise customers have come to expect generative AI capabilities, just like employees.

Related:Insights from Gartner’s Hype Cycle for Artificial Intelligence

This presents both an opportunity and a challenge for IT leaders who must balance the rapid deployment of innovative generative AI projects with gargantuan data transformation efforts. It’s a difficult balance for a CIO to achieve. Transforming the data infrastructure while simultaneously implementing cutting-edge AI technologies are two goals that are inevitably at odds with each other.

Given these conflicting priorities, many organizations find themselves caught between two extremes; waiting to deploy generative AI until data transformation is complete, which could take years, or deploying generative AI without full access to enough data to truly represent accurate insights. This struggle often results in prolonged data transformation projects that outlast the tenure of the executives who initiated them, leading to strategy shifts and further delays. All the while, generative AI deployment projects will languish or, if they do go forward, the lack of easily accessible data will hamstring the AI’s ability to provide accurate insights and sufficient value.

Related:The Pitfalls of AI – Getting Lost, Stuck or Burnt Out

A Third Way: Leverage AI With Contextual BI

With the latest innovations in AI-powered business intelligence (BI), CIOs don’t need to choose between a massive, years-long data transformation project and fast, efficient deployments of generative AI. A fully modern BI platform can easily integrate data – no matter where it is stored – with AI to deliver convenient, relevant insights to users within their existing apps and workflows.

Modern AI+BI goes beyond the old idea of BI as a series of destination dashboards that data analysts build to visualize heaps of complex data into charts and graphs. Instead, it offers no-code web interfaces to deliver bite-sized, contextual intelligence in real time, allowing organizations to reap the benefits traditionally associated with data centralization, but with significantly less disruption, cost and time investment.

The key to making organizations truly data-driven is making data easily accessible, reliable and actionable to any employee, from the C-suite to frontline workers. Traditionally, BI handled the reliability part well, but it wasn’t built for widespread access or actions. The integration of generative AI and browser-based or application-embedded contextual intelligence changes that.

What is contextual intelligence? It is the ability to serve up relevant information in the context of what a user is currently doing, regardless of what system or device type they are using. In concrete terms, picture an application in which you simply hover over any keywords on the screen – such as customer names, product SKUs and accounting codes – and instantly see relevant analytics data about those objects pop up in a window. That, alone, is a powerful capability, but with the addition of generative AI, employees can ask questions about what they see using natural language to gain deeper insights.

To illustrate, consider a sales representative preparing for a call with a customer, who wants to make sure they have a firm grasp on their contact’s role, historical orders, contract terms and support tickets. Usually, this would require thirty minutes or more switching between applications and manually copying data from one system to another or a notepad. 

With modern, contextual BI, it’s as easy as hovering over the customer’s name to view CRM profile data, then moving the cursor to a product name to see quarterly purchase orders from the ERP system, then hovering over an incident number to see the last support ticket logged in the help desk system. If these insights aren’t enough, whatever additional information the sales rep needs can be retrieved instantly by typing their questions into a chat box using generative AI.

In effect, the new world of contextual, AI-powered BI follows the 80/20 rule, delivering 80% of the value of an enterprise-wide data integration project at 20% of the cost and effort. The need for comprehensive data management will always be important and there are many other benefits of digital transformation, but CIOs don’t need to delay generative AI projects until the completion of a giant data centralization effort. 

By adopting a more flexible approach that incorporates generative AI and next-generation, contextual BI tools, businesses can navigate the complexities of modern data ecosystems while driving innovation and maintaining a competitive edge in an AI-driven world.