91视频

As organizations invest billions of dollars in artificial intelligence, most still struggle to translate those investments into measurable results. Researchers at 91视频鈥檚聽 (SEI), working with聽,听 to help organizations adopt AI in ways that deliver predictable, meaningful value.聽

顿别蝉辫颈迟别听, 95% of companies聽 from their AI efforts, and聽 have successfully scaled AI across the enterprise.聽

The new聽 will help business and government organizations assess their readiness to use AI effectively, identify gaps that may limit success and build a roadmap for achieving measurable business and mission outcomes.聽

鈥淪uccessful AI adoption goes beyond improving automation or augmenting existing processes. It means rethinking workflows and innovating ways to bolster them with AI,鈥� said聽, technical director of the SEI鈥檚 AI-Native Software Engineering directorate and the leader of the model鈥檚 development.聽 鈥淎mid the pressure to innovate with AI, organizations must ask what AI should do for the enterprise, not only what AI can do.鈥�

This pressure spans all industries, including highly regulated ones such as health, automotive and defense. Government agencies especially need a rigorous approach as they聽 AI for mission-driven applications. The AI Adoption Maturity Model combines the technical depth, operational realism and security-conscious implementation guidance that support the unique needs of defense and industry organizations.

鈥淭he SEI鈥檚 maturity models have given industries a structured way to measure readiness, reduce risk and continuously improve,鈥� said聽, director of the SEI鈥檚 Software Solutions Division. 鈥淭oday, as AI moves from experimentation into mission-critical environments, organizations need similar clarity to understand where they are, where they need to go and how to get there responsibly. This AI Adoption Maturity Model reflects the SEI鈥檚 deep experience helping organizations adopt emerging technologies safely and effectively, and it incorporates the latest insights from our ongoing research in trustworthy, secure and engineering-grade AI.鈥澛�

Bridging the gap from strategy to practice

Many businesses and government agencies lack a consistent, measurement-based approach for evaluating AI readiness and tracking progress over time. The AI Adoption Maturity Model provides a structured framework for assessing adoption across organizational and technical dimensions, helping leaders make more informed decisions about future AI investments.聽

鈥淢any AI maturity models in the market now focus on high-level strategy without considering the engineering rigor that organizations need to actually scale,鈥� said Manish Sharma, chief strategy and services officer for Accenture. 鈥淲hat we鈥檝e built with the SEI is fundamentally different. It鈥檚 grounded in decades of maturity-modeling discipline, validated through real-world pilots with Fortune 500 companies, and designed to meet organizations where they are across eight critical dimensions of AI readiness. This practitioner-focused framework helps leaders move from AI ambition to measurable, repeatable outcomes.鈥�

Building the capabilities for AI success

The AI Adoption Maturity Model is a framework for assessing an organization鈥檚 ability to perform and sustain specific technical practices to achieve organizational change and AI lifecycle engineering.聽

The model divides AI-relevant capability areas into eight core dimensions: organizational strategy, workforce and culture, workflow re-engineering, risk and governance, data, engineering, operations, and ecosystem.

Achievement of the model鈥檚 capability areas across each dimension will indicate one of five levels of AI adoption maturity: exploratory, implemented, aligned, scaled and future-ready.聽

鈥淥ur industry often assumes discipline can be automated away,鈥� said Ozkaya. 鈥淏ut sustainable AI success still depends on disciplined engineering, governance and operational practices. The ongoing struggles with ROI, value realization and fragmented adoption reinforce this reality. In this environment, measurable and adaptive approaches to maturity matter more than ever.鈥澛�

Through assessments based on the model, organizations can establish their baseline readiness to incorporate AI into workflows and tech ecosystems. That baseline enables organizations to identify use cases, institutionalize practices, focus on the value of investments and create a structured roadmap for adoption.聽

Deeply Researched, Industry Validated

When developing the AI Adoption Maturity Model, Ozkaya and her team leaned on the SEI鈥檚 history in software measurement and analysis, software architecture, cybersecurity, risk management and AI engineering. The SEI鈥檚 expertise in organizational maturity modeling 鈥� gained creating the pioneering聽, the聽 and, more recently, the聽 鈥� helped the team balance core elements of successful maturity modeling with the demands of AI adoption.

The SEI team, in collaboration with Accenture, interviewed more than two dozen executives and surveyed nearly 600 practitioners. The developers reviewed more than 100 existing AI maturity efforts worldwide, including an in-depth analysis of three dozen models. They tuned the new framework to fill key gaps in the AI maturity landscape: the lack of measurable criteria, limited adaptability to rapid AI advances, and inconsistent practice definitions. Finally, they piloted the model with several Fortune 500 organizations.

, a subsidiary of Robert Bosch GmbH and a leading global supplier of technology and services, participated in one of the pilots.聽

鈥淭he SEI AI adoption maturity assessment provided far more than a point-in-time evaluation 鈥� it gave us a structured, actionable understanding of where we are succeeding, where more attention may be needed and how to prioritize future investments for maximum ROI,鈥� said Srinivasulu Nasam, BGSW鈥檚 head of Enterprise AI Transformation. 鈥淭he process reinforced that our teams have been proactively integrating AI into engineering and operational practices with intention and measurable business value. While the assessment validated that we were progressing in the right direction, it聽also聽helped us聽create a baseline聽and calibrate our future roadmap for continuous improvement.鈥�