Analyst(s): Daniel Newman, Mitch Ashley
Publication Date: August 1, 2025
Futurum takes a bold stance on the critical importance of open data access for companies to become AI-powered intelligence organizations. Providing customers full agency over their digital knowledge is essential for maximizing AI’s potential to compete as AI changes businesses, markets, and industries. Open standards that avoid past data lock-in strategies and prioritize data security, privacy, and regulatory compliance are needed to enable companies to compete in an AI-powered marketplace.
What is Covered in this Article
- We are rapidly accelerating into an era where every company must become an “intelligence company,” maximizing AI in virtually every business aspect.
- Empowering customers with open data access to their full digital knowledge is critical for becoming AI-powered intelligence organizations.
- Restrictive data practices, such as data lock-in and proprietary formats, starve AI of the essential information it needs to excel.
- The future of AI demands investing collective energy into open standards that foster data access while ensuring security, privacy, and regulatory compliance.
The News: Customers are implementing AI solutions that require access to digital institutional knowledge across a broader set of systems, tools, and data sources across the organizations. Open data access is needed, balancing customers’ agency to utilize their data in AI solutions, and meet security, privacy, and regulatory requirements. Vendors must avoid past data lock-in practices and instead invest in creating and adopting emerging open standards that enable AI agent interoperability, collaboration, and access to data external to AI models.
The Call For Open Data Access That Fuels The AI-Powered Intelligence Company
Analyst Take: Futurum has taken a bold stance about AI and its importance to the future of business and the world we operate in. AI is a technology that’s disruptive far beyond merely reinventing or reimagining how businesses operate. AI is a once-in-a-generation event that compels us to intelligently and wisely use AI to re-equip our companies, customers, and employees to compete in ways only possible with AI.
We are accelerating into the era of “every company is an intelligence company,” a phrase adapted from Microsoft CEO Satya Nadella’s “every company is an intelligence engine,” and NVIDIA CEO Jensen Huang’s “every company is an intelligence manufacturer.”
Becoming an intelligence company requires more than just using AI. It is maximizing AI, leaning all in on AI in virtually every aspect of business. Developers refer to this as AI-powered and AI-native software development.
Over the past five years, the digital revolution has created a vast wealth of digital enterprise knowledge beyond the databases, applications, and systems traditionally considered data sources. Digital knowledge extends to data across collaboration, workflow, and communications platforms. Email, messaging, content management, customer systems, codified workflows, videos, document repositories, support systems, and engineering logs are a few examples of digital knowledge stores in our companies.
Open Data and The Intelligence Company
Today, we take another bold stance that open data access by customers to their complete digital knowledge is critical to becoming AI-powered intelligence companies. The strategic importance of open data has never been higher, and its accessibility is a top-level business priority. Open data access entails empowering customers with the agency to access and integrate data with the best AI innovations, whether vendor-provided, internally created, or open-source solutions.
It’s essential to take this moment to revisit restrictive practices with data. Customers push back against data lock-in within systems and online services. Data silos, proprietary data formats, restrictive APIs, download limits, and data portability fees are sources of customer frustration and loss of trust in vendors, resulting in lost business to alternatives without those restrictive practices. Customers will continue to push against practices that starve AI of the data necessary to execute their AI strategies.
Open data access occurs in the context of complying with all regulations. It is crucial to implement data security, privacy, and compliance regulations, including the U.S. Department of Justice 2025 Final Rule, EU AI Act, Digital Operational Resilience Act (DORA), General Data Protection Regulation (GDPR), and others, when opening data access. Vendors must, of course, comply with these regulations, but not use these regulations as justification to lock customers out of their data.
This access to a company’s full data set, including unstructured data from messages and documents, enables the creation of powerful AI agents to perform tasks and automate processes. These agents are not generic; they are smart because they draw from a company’s entire institutional knowledge. Locking data into proprietary systems and tools will starve AI of the very information it needs to excel in current and new markets, possible with AI. Imagine an AI agent that can analyze a decade’s worth of customer support emails, product design documents, and engineering chat logs to identify a critical product flaw that no human team could have found.
Competing in the Agentic AI Race
In this new era of agentic AI, customers’ ability to control their data is a competitive necessity. With open data access, companies can build specialized models and agents powered by their specific business processes and institutional knowledge. This is essential to competing during this rapid adoption of AI. The organizations that can leverage their unique data to create highly specific, powerful agents will be able to disrupt established markets and move faster than their competition.
While the industry has seen markets such as medical records and payment systems where data is locked into vendors’ solutions, we have successful examples where vendors share open access and compete without restrictive data practices. The shift from proprietary agents to the OpenTelemetry open standard in application monitoring and observability provides a prime example of how the industry successfully moved from competing on data control to competing innovations in using that data.
OpenTelemetry moved vendors from competing on who controlled data to vendors that can out-innovate one another. Splunk, AWS, Google, Microsoft, Dynatrace, Elastic, and dozens of competitors worked together to implement and support OpenTelemetry Protocol (OLTP), OpenTelemetry Collector, semantic conversation definitions, and API standards. These companies compete with their respective product offerings built on these shared open standards.
The Future is Open
Today is a call to open data access and open standards for AI, with vendors collaborating on challenges critical to AI’s potential. Open standards for AI and agents are forming rapidly.
The Model Content Protocol, Agent2Agent, and AGNTCY open standards have gained rapid support and adoption as vendors agree on the criticality of AI agent interoperability, collaboration, and access to data external to AI models.
Investing our collective energy into open standards that foster open data access and support security, privacy, and regulatory compliance is needed. Customers have and will demand open, interoperable standards, as they invest in implementing vendor innovations as part of their AI strategies.
What to Watch
- Follow closely the maturation of early agentic AI open standards, including the Model Context Protocol, Agent2Agent Protocol, Agent Communication Protocol, and Open Agentic Schema Framework (OASF).
- New open standards, such as AGNTCY, continue to emerge, and contributing them to The Linux Foundation or other open source project organizations is a key step to broader development and adoption.
- Prepare now to seize opportunities should competitor offerings implement restrictions that overly limit customers’ use of their data.
See the press release about the Futurum Agentic AI Open Standards Report: 1H-2025 by clicking here.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
Other Insights from Futurum:
Enterprise AI Evolution: Maximizing Data Value in a Hybrid World
From Data Chaos to AI Clarity: Activating AI Through High-Quality Enterprise Data
AWS Summit New York City: AWS Forges the Enterprise-grade Pipeline for Agentic AI
Microsoft Embraces the Development Community on the Path to Agentic AI
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
Mitch Ashley is VP and Practice Lead for the CIO & Technology Buyers and Software Lifecycle Engineering practices at The Futurum Group. A multi-time CIO and CTO with 30+ years leading technical organizations, Mitch built and operated production systems spanning cybersecurity for the U.S. Department of Defense, PKI services for the broadband and 5G industries, SaaS platforms, large-scale telecom and banking systems, and a national broadband network. His work with AI began early, developing expert systems that diagnosed and repaired complex mainframe environments. That operator foundation grounds his analysis in operational consequence, covering the technology buyer's world of software engineering, cybersecurity, DevOps, cloud, and AI.
