Delivering revolutionary data intelligence across industries – MIT Technology Review

Industry leaders share priorities for democratizing data and AI across the enterprise.

As organizations recognize the transformational opportunity presented by generative AI, they must consider how to implement this technology in the enterprise in the context of their unique industry challenges, priorities, data types, applications, ecosystem partners, and governance requirements. Financial institutions, for example, need to ensure that data management and artificial intelligence have the built-in intelligence to fully comply with strict compliance and regulatory requirements. Media and entertainment (M&E) companies are looking to build AI models to drive deeper product personalization. And manufacturers want to use AI to make their Internet of Things (IoT) data easily accessible to everyone from the data scientist to the worker on the shop floor.

In each of these scenarios, the starting point is access to all relevant data – of any type, from any source, in real time – comprehensively managed and shared across the industry ecosystem. When organizations can achieve this with the right data and AI foundation, they have the beginnings of data intelligence: the ability to understand their data and break free from the data silos that would block the most valuable AI outcomes.

But true data intelligence is about more than establishing the right data foundation. Organizations are also grappling with how to overcome reliance on highly technical staff and create data privacy and organizational control frameworks when using generative AI. Specifically, they seek to empower all employees to use natural language to extract useful information from the company’s own data; use this data at scale to train, build, deploy and tune their own secure large language models (LLM); and to infuse company data information into every business process.

This story is available to subscribers only.

Don’t settle for half the story.
Get access to technology news without paying here and now.

Subscribe now
Already a subscriber? Sign up

In this next frontier of data intelligence, organizations will maximize value by democratizing AI while differentiating themselves through their people, processes and technologies within their industry context. Based on a global cross-industry survey of 600 technology leaders, as well as in-depth interviews with technology leaders, this report explores the foundations being built and used across industries to democratize data and AI. Following are his main findings:

• Access to real-time data, streaming and analytics are priorities in every industry. Because of the power of data-driven decision-making and its potential for game-changing innovation, CIOs need seamless access to all their data and the ability to extract insights from it in real-time. Seventy-two percent of respondents said the ability to stream real-time data for analysis and action was “very important” to their overall technology goals, while another 20% thought it was “somewhat important”—whether that means enabling real-time recommendations in retail or identifying the next best course of action in a critical triage situation in healthcare.

• All industries are looking to unify their data management and AI models. The push for a unified approach to managing data and AI assets is strong, with 60% of respondents saying a unified approach to embedded data governance and AI is “very important” and an additional 38% saying it is “somewhat important,” which suggests that many organizations struggle with a fragmented or siled data architecture. Each industry will need to achieve this unified governance within the context of its own unique systems of record, data channels, and security and compliance requirements.

• Industrial data ecosystems and cross-platform sharing will provide a new foundation for AI-led growth. In every industry, technology leaders see promise in technology-agnostic data sharing across the industry ecosystem, supporting AI models and core operations that will drive more accurate, relevant and profitable outcomes. Technology teams at insurers and retailers, for example, are looking to ingest data from partners to support real-time pricing and supply decisions in online marketplaces, while manufacturers see data sharing as an important opportunity for continuous supply chain optimization for deliveries. Sixty-four percent of respondents said the ability to share live data between platforms was “very important,” while an additional 31% said it was “somewhat important.” Additionally, 84% believe that a managed central marketplace for datasets, machine learning models, and laptops is very or somewhat important.

• The data retention and flexibility of AI in the clouds resonates with all verticals. Sixty-three percent of respondents across all verticals believe the ability to use multiple cloud providers is at least somewhat important, while 70% feel the same about standards and open source technologies. This is consistent with the finding that 56% of respondents see a unified system for managing structured and unstructured data in business intelligence and AI as “very important”, while a further 40% see it as “somewhat important”. Executives prioritize access to all organizational data, of any type and from any source, securely and without compromise.

• Industry-specific requirements will drive the prioritization and pace at which generative AI use cases are adopted. Supply chain optimization was the most valuable generative use case for AI for survey respondents in manufacturing, while it was data analytics and real-time insights for the public sector, personalization and user experience for M&E, and quality control for telecoms. Generative adoption of AI will not be one-size-fits-all; every industry has its own strategy and approach. But in any case, value creation will depend on access to data and AI permeating the enterprise ecosystem and AI being embedded in its products and services.

Increasing the value and scaling the impact of AI on people, processes and technology is a common goal across industries. But industry differences deserve special attention for their impact on how intelligence infuses data and AI platforms. Whether it’s the retail associate managing omnichannel sales, the healthcare practitioner chasing real-world evidence, the actuary analyzing risk and uncertainty, the factory worker diagnosing equipment, or the telecom field agent assessing network health, language and scenarios that AI will support vary widely as they democratize to the front lines of any industry.

Download the report.

This content was created by Insights, the custom content arm of MIT Technology Review. Not written by the MIT Technology Review editorial staff.

Leave a Comment

Your email address will not be published. Required fields are marked *