As more companies embrace artificial intelligence, and specifically generative AI (GenAI), we are headed for a landmark moment. GenAI is today mostly used with public data but when GenAI models are trained, tuned and used with an enterprise’s proprietary data the combination unlocks the hidden patterns, connections, and insights that can transform a business.
Data Goes Wide
Ten years ago, basic pattern finding was core to the idea of leveraging big data. Machine learning spotted patterns within a particular domain, like offering an online customer the right product. However, with the new computational and software innovations of GenAI, data can come from a much wider variety of sources across domains, with deep learning finding not just patterns in one domain, but also entirely new relationships among different domains.
Earlier limitations of technology and communications meant organizational designs eventually relied on creating independent, fractured data silos and leaving on the table a great potential for collective learning and improvement. GenAI, embedded in reimagined and hyper-connected business processes, as well as new business intelligence platforms can change this.
Google is among several companies working on the next generation of data analytics systems that build wide data records combining structured, unstructured, at-rest and in-movement data that ultimately the digital footprint of a company into a powerful AI model. In future the focus will need to shift from big to wide data.
From Analytics to Agents
GenAI can now be instructed to take on specific roles and achieve specific goals on behalf of humans. AI agents will be the future “do-ers”, taking on the role of personas, such as a data engineer, and executing tasks within a workflow.
Automation follows a pattern: Insights, actions and processes are abstracted and embodied in a system, new workflows are established around trust and reliability, and finally widespread adoption follows. Think of automatically scheduling maintenance on a machine in a factory, or problem-solving natural language interactions in a call center. These are examples of trusted software agents carrying out autonomous actions across an enterprise.
The goal for GenAI in analytics is to make observations and generate insights that can accelerate the work of people. People will be able to uncover new approaches, identify trends faster, collaborate in unforeseen ways, and delegate to agents that have permission to act in autonomous ways to increase organizational effectiveness.
The role of human experts will be different and require new skill sets. It’s less about doing the work and more about what a good result looks like and what the right question (or prompt) is like. For example, a sales analyst will spend less time on writing queries to gather data and more time on judging if data found by AI-driven insights offer a relevant insight. Business judgment becomes more important than technical analyst expertise.
Gen AI for analytics brings us back to really understanding the question one is trying to solve and frees us from much of the complication in the technical toolkits that took the lion’s share of our time and investments. Organizations that overly limit data access and employee empowerment are likely to become less competitive.
When things are changing in big ways, it’s useful to think about the things that won’t change, like offering value to customers, focusing on positive efficiencies, and creating new goods and services that excite people and improve lives. These core values will continue to steer the application of this new GenAI technology, and the world of business will be forever changed. GenAI represents a paradigm shift on how we will imagine and enact new ways of doing business, from enabling business users to “chat” with their business data, supercharging data and analytics teams with an always-on collaborator and automating business with AI-driven data intelligence.