By Saurabh Abhyankar, EVP and chief product officer, MicroStrategy
In today’s rapidly evolving business landscape, organizations find themselves caught between two seemingly conflicting goals: the urgent need to deploy AI technologies, particularly generative AI (GenAI), and the long-term goal of breaking down data silos through comprehensive data integration initiatives. After all, AI requires a massive amount of data, so one would imagine deployment would need to wait until the completion of a data unification project, but the pressure to deploy GenAI immediately is intense. This tension creates a significant challenge for CIOs and IT leaders who must navigate these competing priorities while driving innovation and maintaining a competitive edge.
The data integration dilemma
Recent surveys indicate that approximately half of CIOs are prioritizing data platform overhauls to create unified data ecosystems. The driver is clear. By consolidating data into a single, easily accessible platform, companies will be able to accelerate innovation and uncover valuable insights that will drive growth and open new opportunities.
However, these data transformation initiatives often come with a hefty price tag and can span multiple years, which conflicts with the pressing need to implement AI technologies, especially GenAI, for immediate competitive advantage. But the urgency to deploy AI quickly is intense, with over 60% of companies naming generative AI as a top-three priority for 2024 and 87% already in various stages of development, piloting, or deployment. Boards of directors and the C-suite don’t want to wait for a massive data transformation project to finish.
The pressure to adopt AI, and particularly GenAI, stems from several factors. Both consumer and business customers are becoming accustomed to GenAI capabilities in the apps they use day-to-day, so they increasingly expect GenAI capabilities in other products they purchase. But beyond appealing to customers, companies fear that competitors who deploy GenAI faster will gain insurmountable competitive advantages, due to the efficiency gains and additional insights that AI provides. Also, the pressure is not all external; the rise of consumer AI assistants has created new expectations from employees for natural language interfaces in the workplace.
This leaves CIOs in a difficult position of trying to balance the rapid deployment of innovative GenAI projects with the need for comprehensive data transformation efforts. The two goals often seem mutually exclusive, leading to a paralysis where organizations either delay AI deployment until data integration is complete — potentially taking years — or rush to implement AI solutions without sufficient access to quality data, which risks providing inaccurate or incomplete results.
The third way: Leveraging AI with advanced BI
Fortunately, recent innovations in AI-powered business intelligence (BI) offer a promising solution to this dilemma. Modern BI platforms integrate data from various sources regardless of where it’s stored, and when integrated with AI capabilities, can deliver relevant insights to users within their existing applications and workflows.
This new approach to BI goes beyond traditional dashboard-centric models, utilizing powerful data models and no-code web interfaces to provide contextual intelligence in real-time. By doing so, organizations can reap many of the benefits associated with data centralization without incurring the full cost, time investment, and disruption of a complete data overhaul.
The key benefits of combining GenAI and BI include:
- Contextual Intelligence: Users can access relevant data by simply hovering over keywords like customer names, product SKUs, or accounting codes, with analytics data instantly appearing in a pop-up window.
- Natural Language Queries: Integrated GenAI allows employees to ask questions using natural language, gaining deeper insights on demand.
- Cross-Application Functionality: The system can pull and display data from multiple sources, such as CRM, ERP, and help desk systems, providing a holistic view without the need for manual data aggregation.
- Action Initiation: Users can not only view data but also initiate actions within different applications based on the insights gained.
Consider a sales representative preparing for a customer call. Typically, they might spend 30 minutes or more switching between applications, manually copying data from various systems. With modern, contextual BI, the process becomes seamless. Noting that the rep is preparing for a call, GenAI could proactively provide CRM profile data. The rep could also request using natural language to see quarterly purchase orders from the ERP system. They could then click on an incident number to review the last support ticket from the help desk system, and finally use the GenAI-powered chat interface to ask follow-up questions and gain additional insights.
This streamlined approach not only saves time but also ensures that the sales rep has access to comprehensive, up-to-date information without the need for a fully integrated data warehouse.
A path forward
By adopting a flexible approach that incorporates GenAI and next-generation BI tools, businesses can navigate the complexities of modern data ecosystems while driving innovation and maintaining a competitive edge. This strategy allows organizations to deploy AI solutions more rapidly, meeting the urgent demand for these technologies, and leverage existing data assets without waiting for complete integration. As a result, employees can gain powerful, context-aware tools that enhance productivity. Meanwhile, IT can continue its long-term data integration efforts without sacrificing short-term AI capabilities.
As the business world continues to evolve at a breakneck pace, the ability to balance immediate AI implementation with strategic data management will be crucial for success. By embracing the power of advanced BI platforms integrated with AI, organizations can chart a course that satisfies both the need for rapid innovation and the long-term goal of comprehensive data integration.
Saurabh Abhyankar has been innovating in the analytics market for 20 years and holds a number of patents in self-service analytics, the semantic graph, and HyperIntelligence. He serves as EVP and chief product officer for MicroStrategy. Prior to joining MicroStrategy, Saurabh was the Chief Product Officer and head of engineering for MRI Software, a leader in technology solutions for real estate.
Mr. Abhyankar received a B.Sc. in Computer Science from the University of British Columbia.
