Movers & Shakers is where you can keep up with new CIO appointments and gain valuable insight into the job market and CIO hiring trends. As every company becomes a technology company, CEOs and corporate boards are seeking multi-dimensional CIOs and IT leaders with superior skills in technology, communications, business strategy, and digital innovation. The role is more challenging than ever before — but even more exciting and rewarding! If you have CIO job news to share, please contact me here!

Jeff Fleece named CDIO at Southwire

Southwire manufactures wire and cable used in the transmission and distribution of electricity, delivering power to millions of people around the world. Fleece most recently served as CIO of Baker Hughes, where he was responsible for leading the company’s global IT strategy and digital transformation, as well as driving internal productivity solutions. He holds a BS from the United States Military Academy at West Point, and an MBA from Texas McCombs School of Business.

Ally Financial names Mark Mathewson CIDO

Ally Financial is a diversified financial services company, providing a range of financial products and services for consumers, businesses, automotive dealers, and corporate clients.Mathewson most recently served as the divisional CIO for Capital One’s retail banking and commercial banking units, where he oversaw tech teams across the US, Mexico and India. Prior to that, he was at Fannie Mae, where he advanced through a series of increasingly senior roles spanning application development, IT governance, and project portfolio management. He holds a BA from James Madison University and an MBA from Georgetown University.

Patricia Pichardo joins Duracell as CIO

Duracell is a manufacturer and marketer of high-performance alkaline batteries, lithium coins, rechargeable batteries, and other specialty cells. Pichardo held previous roles as head of IT PMX for TE Connectivity; director, IT business solutions for McCormick & Company; and IT director, global IT business operations for Stanley Black & Decker. Pichardo holds a BS from the University of Arizona and an MBA from Johns Hopkins University.

Feed the Children announces Stephen Murray as CIO

Feed the Children is a global movement focused on ending childhood hunger in the US and globally. Working alongside communities, it provides food, essentials, and opportunities to children and families who need them most. Prior to joining, Murray held senior tech leadership roles across complex global organizations in manufacturing, media and entertainment, agriculture, and consumer products, including CIO at Cohere Beauty and leadership roles supporting The Walt Disney Company through Atos, as well as positions at The Mosaic Company and Cargill. He holds a BS from SMC University, a CIO MBA from Boston University, and an executive MBA from Rutgers University.

Tracy Weber appointed CIO at Notre Dame

The University of Notre Dame is a global Catholic research university founded in 1842. In her career with Notre Dame, Weber has advanced through several leadership roles spanning many areas of IT, including applications development, enterprise content management, platform services, collaborative IT services, and community IT experience. She holds a BA and EMNA from the University of Notre Dame.

James Watson joins Perry Homes as CIO

Family-owned and operated since 1967, Perry Homes is a woman-owned homebuilder that offers a wide selection of new home designs. Watson most recently served as CIO for Bigge Crane and Rigging, and held prior CIO roles at Total Safety US and Situs. He has a BS and an MBA from the University of Houston – Clear Lake.

Luminis Health welcomes Erica Moncrief as CIO

Luminis Health is a nonprofit regional health system. Most recently, Moncrief served as VP of IT at Adena Health System, where she provided leadership across all IT functions supporting more than 5,000 employees, 500 physicians, four hospitals, and six regional clinics. She held prior roles at Cardinal Health, InXite Health Systems, and OhioHealth. She holds a BS and MS from Ohio State University.

Daniel Curling joins Lanter Delivery Systems as CIO

Lanter Delivery Systems arranges daily deliveries for essential industries and world-class companies across North America. Most recently, Curling served as CTO for WWEX Group. Prior to that, he was CTO for Experian Health where he led a team of 350 responsible for all tech teams including application development, cloud engineering, DevOps, quality assurance, site reliability engineering, cybersecurity, enterprise architecture, and portfolio management. Curling holds a BS from Texas A&M University.

Piotr Zima named CIO for Cepheid

Cepheid is a wholly-owned subsidiary of Danaher Corporation specializing in molecular diagnostics. Most recently, Zima served as VP and CIO at Integrated DNA Technologies, another Danaher operating company. Earlier in his Danaher career, he was chief data and analytics officer at Danaher Corporation and head of data, analytics, and finance IT at Cytiva. Zima holds a BS from Cracow University of Technology, and an MBA from Oxford Brookes University.

Acentra Health appoints Michael Kingston as CDIO

Acentra Health is a technology and health solutions company dedicated to accelerating better health outcomes for government and commercial clients, and the populations they serve. Kingston most recently served as MD, Portfolio CIO Americas for The Carlyle Group, where he drove tech-enabled value creation for portfolio companies. Prior to Carlyle, Kingston held senior tech leadership positions with several globally recognized brands, including L’Oréal, Neiman Marcus, Coach, Ann Inc., and LVMH. Hold a BS from William Paterson University.

New CIO appointments, July 2026

FirstEnergy names Daniel Puscas as CIO

Eric Tagliere joins R1 as CIO

Geisinger welcomes Bill Bellando as CIO

Wasif Jamal joins WellSpan Health as CIO

Cumberland Farms taps Michael Verdesca as CIO

Teradata names Josh Fecteau as CDAO and CIO

Karen Higgins-Carter announced as CITO for Kroll

Jon Whiley joins TRG as CIO

PowerGrid Services welcomes Brad Manning as CIO

Anguel Lindarev appointed CIO for WesBanco

John Trchala named CTO at eIVF

New CIO appointments, June 2026

Citi appoints Brian Saluzzo as CIO

Imran Jalozie named CIO for Arch Insurance North America

Diebold Nixdorf welcomes Raj Singh as CIO

Jay Spears joins Topgolf as CIO

Crown Castle taps Mark Lennon for CIO

Jim Maza announced as CIO for BDO USA

TDS appoints Bill Case CIO

Viral Tripathi named CIO for Diversified

Consumer Cellular appoints Kannan Alagappan as CIO

Nadene McKenzie-Reid joins Aircastle as CIO

Northwest Bank hires Chad Ballard as CIO

New CIO appointments, May 2026

Chuck Jones joins Northrop Grumman as CIDO

McDonald’s names Mustafa Husain CIO

Turner Construction appoints Dawn Paquette as CIO

Autodesk names Mike Kelly CIO

Memorial Hermann Health System appoints Desiree Gandrup-Dupre CIO

Dycom names Regina Salazar as CIDO

Todd Treonze joins J.Jill as SVP and CIO

V2X names Mike Uster CIO

Horizon Services welcomes John Souther as CIO

Craig Richardville appointed CDIO for UF Health

Pep Boys welcomes Nik Umrani as CIO

Peter Scavuzzo elevated to CITO at CBIZ

ePlus promotes David Mellor to CIO

Julie Irish named CIO for Alteryx

AVI-SPL welcomes Doug King as CIO

Chris Stori joins Verkada as CIO

Exostar promotes Amy Hogan to CIO

New CIO appointments, April 2026

Albertsons Companies appoints Brian Rice to board of directors

Francesco Tinto joins Kimberly-Clark as CIO

University of Pennsylvania names Joshua Beeman CIO

Jim Connolly joins Legacy Food Group as CIO

AGCO appoints Jena Holtberg-Benge as CDIO

Ryan Biren promoted to CIO for THOR Industries

Genworth welcomes Morris Taylor as CIO

Barninder Khurana joins Patriot Growth as CIO

Prashant Lamba announced as CIO for RGP

New CIO appointments, March 2026

USAA names Dan Griffiths as CIO

Dori Henderson joins Brown & Brown as CITO

Bob Hardester named CIO for Advantage Solutions

Skadden adds Vince DiMascio as CDIO

Chris Hickok appointed CIO for Messer Construction Co.

Stormont Vail Health appoints Aaron Wootton as CIO

TowneBank welcomes Varun Chandhok as CIO

Todd Paladini joins Freddy’s Frozen Custard & Steakburgers as CIO

Optiv promotes Doug Goehl to CIO

Brad Rohrer joins the University of Iowa as CIO

Morongo appoints Tony Gawne as CIO

Each year the CIO 100 Awards showcase outstanding IT initiatives, and every year they illustrate the power and potential of technology to transform how people work, how organizations perform, and the value they offer to customers.

The 2026 cohort of winners is no different. Each one demonstrates how IT executives and their teams successfully move from ideation to deployment to scaling a solution for the future, overcoming challenges and driving adoption along the way to ensure their organization gets a return on its investment.

[ Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here ]

The winning initiatives come from a range of industries and utilize a host of technologies to achieve their goals, as is the case annually. A growing proportion of these stand-out projects leverage artificial intelligence, raising the bar on the art of the possible for all IT departments.

The following 10 award-winning projects serve as representatives for the outstanding work done by all the 2026 honorees.

ABB democratizes AI agent creation and deployment

Organization: ABB

Project: ABBY — AI Agentic Platform for Workforce Transformation

IT leader: Vikke Kandell, CIO

IT leaders at ABB, a manufacturer, had some big hurdles to clear when it came to building an AI strategy.

They had to overcome employee fears that AI would take away jobs, the potentially high cost of AI vendor licenses, and pressure from investors, customers, and executives to advance the use of AI in the enterprise.

“We looked at this and asked, ‘How do we address all this?’ and build something that the company is proud of,” says Babu Kuttala, vice president of data analytics and AI.

The answer is ABBY, an AI agentic platform that enables employees to create and deploy specialized AI agents for specific business tasks.

To build ABBY, Kuttala and his team used best-of-breed LLMs (about 25 in total). They built a centralized orchestration layer using generative AI that integrates internal knowledge bases with external ecosystems, creating a unified platform where agents can access enterprise data, understand required actions, and execute tasks across multiple systems. And they created preconfigured skills so that employees could build agents tailored to their workflows without having to code.

ABBY was rolled out in 2025 to 100 users but is now used by 63,000 (more than 75% of the company’s workforce, Kuttala notes) with an average of 10,000-plus workers using it daily. IT continues to add LLMs and capabilities to expand use of ABBY even further, Kuttala says.

Belcorp modernizes manufacturing with Smart Factory

Organization: Belcorp

Project: QPlant — Smart Factory

IT leader: Venkat Gopalan, Chief Digital, Data, and Technology Officer

Legacy processes were limiting Belcorp’s ability to scale and compete. Its manufacturing relied on ERP-driven processes with limited shop-floor automation and weak connectivity across production, packaging, quality, and maintenance. The company depended heavily on manual records and post-process reconciliation, resulting in fragmented data, limited real-time insight, inefficiencies, and higher risks for errors.

Smart Factory changed all that. The IT initiative reimagined how manufacturing teams work “by creating a connected, data-driven environment where production, quality, maintenance, and operations are aligned around real-time information and standardized execution,” says Venkat Gopalan, chief digital, data, and technology officer.

At Smart Factory’s core is a manufacturing execution system that orchestrates production workflows, quality processes, and operational execution, he explains. IoT-enabled equipment integration and a centralized SCADA platform provide real-time visibility into shop-floor operations, while electronic batch records digitize production execution, strengthen traceability, and reinforce compliance by design.

Integrating those operational technologies with the company’s enterprise platforms was another critical component of success, Gopalan says, creating a trusted flow of real-time data across manufacturing, quality, maintenance, and business systems. “This connected architecture transformed isolated data into actionable insights, enabling faster decision-making, greater operational visibility, and continuous improvement across the manufacturing lifecycle,” he adds.

The initiative generated more than $1 million in financial benefits in its first year alone.

“Most importantly, Smart Factory established the digital foundation for the future of manufacturing at Belcorp,” Gopalan says. “With real-time operational data and connected systems now in place, we’re well positioned to accelerate advanced analytics, AI-driven optimization, predictive maintenance, and other Industry 4.0 capabilities that will continue delivering value for years to come.”

Cohesity replatforms post-acquisition for commercial growth

Organization: Cohesity

Project: Lead to Cash Replatforming Program (Veritas Integration)

IT leader: Brian Spanswick, CIO

Cohesity set an ambitious objective: Complete an enterprise-scale lead-to-cash replatform in under six months.

That’s a tight timeline for any replatforming initiative, but Cohesity’s project had another layer of complexity. It followed Cohesity’s December 2024 acquisition of Veritas, a company twice its size in revenue, leaving Cohesity to integrate the majority of a global enterprise revenue engine into its own operating model without disrupting customers, partners, or sellers.

“We had to bring the two companies together, merge the workforces together, and create an overall harmonized organization and operating infrastructure platform,” says Eric Brown, who as CFO and COO led the project.

The program migrated heavily customized CRM, CPQ, PRM, ERP, and subscription platforms (some of which were “very brittle, very bespoke,” Brown says) to a unified SaaS CRM, CPQ, and ERP environment with uninterrupted selling, billing, and partner operations.

This was no lift-and shift, Brown stresses. “It was a business process optimization project as well. We want to run very efficiently, so we questioned everything and used the migration process to simplify and streamline the business in every possible respect.”

The initiative enabled continuity for 13,000-plus customers, protected revenue during integration, and established a scalable commercial foundation for future growth.

Brown cites several factors that contributed to success. First, leadership was upfront about what it would take to meet the deadline, a process that involved carefully prioritizing the capabilities that would appear in the first iteration. Leadership also streamlined decision-making, establishing office hours that “ran with military precision” to handle issues. And the company selected a specialized partner, requiring its top talent be assigned to Cohesity.

Dairyland Power goes agentic to protect field crews

Organization: Dairyland Power Cooperative

Project: ODIN — Organizational Effectiveness Agentic AI

IT leader: Nate Melby, VP and CIO

Dairyland Power Cooperative had amassed a large collection of field observations, incident reports, near-misses, safety rules, and work methods that could yield insights into processes and practices that could help protect its workers.

But the insights were essentially out of reach, trapped in siloes.

Dairyland’s organizational effectiveness team turned to CIO Nate Melby for help unlocking those insights. Melby then turned to agentic AI, recognizing that the technology could address the team’s need to make better use of its data.

“This was about finding insights on how to work more safely,” Melby says. “It’s about preventing incidents.”

The collaboration between the two teams created ODIN, the first agentic AI implementation of its kind in the electric utility industry.

Focused on worker safety, ODIN autonomously connects the collective safety knowledge of the organization and delivers actionable insights directly to field crews at the moment work is planned.

ODIN was developed through a hybrid approach that combined an agentic AI platform and Dairyland’s internal private generative AI platform called VoltWrite. ODIN leverages LLMs, retrieval-augmented generation, and a coordinated swarm of autonomous agents.

Agents work together to analyze internal safety data, performance history, work practices, and safety rules and then synthesize the information into clear guidance on the safest way to perform specific tasks.

ODIN has produced results, including a reduction in OSHA recordable injuries and improvements in the quality and consistency of pre-job safety briefings.

ODIN was deployed in early 2025 for use by Dairyland’s workers in transmission construction and electrical maintenance, which are the highest-risk work areas. Dairyland is looking to expand ODIN’s use to other teams.

Dow’s digital sustainability ledger drives low-carbon sales

Organization: Dow

Project: Carbon Footprint Ledger

IT leader: Deb Bauler, Chief Information and Digital Officer

Executives at Dow consider the Carbon Footprint Ledger (CFL) as more than a technology or innovative carbon accounting methodology. According to Senior Global IT Director Jeremy Preston, CFL is “a digital business capability that enables Dow to translate sustainability investments into customer value.”

CFL transformed how Dow uses greenhouse gas emissions data. It combines a methodology aligned to international standards with an enterprise-scale digital platform. It also integrates manufacturing, supply chain, commercial, and sustainability data to generate product carbon footprints under enterprise-level governance and management at scale.

In doing so, Preston says it creates “a trusted, traceable link between low-carbon processes and raw materials implemented across its manufacturing network and the lower-carbon products customers seek.”

The technology team worked closely with sustainability and business teams, collaboratively developing the capabilities needed to reconstruct product genealogy, maintain end-to-end data lineage, track low-carbon attributes across interconnected manufacturing processes, and generate product carbon footprints that can support customer offerings and commercial transactions.

CFL was built on Dow’s Integrated Data Hub and in partnership with Boston Consulting Group and Databricks.

The core CFL platform is fully deployed and supports commercial transactions today.

Preston says CFL “enables Dow to turn sustainability investments into customer value, commercial differentiation, and new growth opportunities.” Dow reports that it has driven hundreds of millions of dollars in low-carbon product sales in 2025 and 2026.

The company is now expanding its use. “We are extending adoption across additional products, manufacturing networks, business segments, and customer use cases while continuing to enhance automation, analytics, and integration with commercial processes,” Preston says.

J&J transforms quality management with AI

Organization: Johnson & Johnson

Project: Q&C Strategy

IT leader: Michael Comprelli, Vice President, Head of Technology, Technical Operations, and Risk; Joel O’Connor, Head of Technology, Medtech Quality, and Compliance

Johnson & Johnson is using AI to transform quality management through its Q&C Strategy.

QuIn is an AI-powered digital assistant that fuses human expertise with machine learning, automation, and data-driven insights to boost efficiency, reliability, and worker impact. By embedding gen AI into core quality management systems processes, QuIn proactively gathers actionable insights, increases operational efficiency, and allows teams to focus on high-value, patient-centric work.

Cora is an innovative generative AI platform that provides regulatory intelligence monitoring, impact analysis, and augmented content revision. Cora assists with document analysis, compliance comparison, stakeholder analysis, policy/standard creation, procedural/document updates, and document comparison. Cora is purpose-built for regulated environments, validating outputs against source material and offering a user experience that instills trust in the outcome.

QuIn and Cora, which automate time-intensive tasks and democratize information access, are on track to deliver significant value, with J&J reporting more than $62 million in documented true cost savings by 2028 from QuIn alone. Cora delivered $2 million in cost efficiency in 2025 and will deliver a documented cost savings of $25 million by 2028.

“Our teams proved responsible AI can be applied meaningfully in a highly regulated environment without compromising the rigor, accountability, or human judgment that quality requires,” says Michael Comprelli, vice president, head of technology, technical operations, and risk.

He continues, saying that J&J “moved these ideas beyond experimentation and into products that employees use in their daily work. We did that by bringing together Quality expertise, product management, data engineering, architecture, cybersecurity, user-experience design and AI engineering around a common purpose.”

JLL brings intelligent automation to business services

Organization: JLL

Project: Business Service Digitization

IT leader: Pinak Dash, Global Head of JLL Business Services and Legal Technologies

JLL launched its digitization initiative to drive process redesign as well as systematic AI and RPA deployment across JLL Business Services (JBS).

The initiative was designed to address inefficiencies that hampered scalability and competitive positioning. It was also designed to eliminate manual processes that consumed thousands of hours across finance, HR, legal, procurement, marketing, research, IT, and lease administration.

Pinak Dash, global head of JBS and legal technologies, says the digitization initiative had a dual-strategy combining traditional digitization with generative AI innovation to hundreds of processes.

JLL lists three innovations critical to the program’s success.

First is a hybrid platform that integrates RPA with JLL’s proprietary AI platform called Falcon, which created intelligent automation that adapts and learns. It enables real-time process automation, intelligent document processing with automated extraction/validation, and smart decision-making for continuously optimizing workflows.

The second innovation is its use of ProHance for real-time process monitoring and enabling of data-driven optimization. Sensors capture granular productivity metrics, identify bottlenecks, and provide actionable insights for continuous improvement across automated and manual processes.

Third is its custom AI assistants and transaction agents. Falcon-powered assistants provide intelligent knowledge search while specialized agents execute complex transactions across enterprise SaaS platforms. These handle multisystem workflows, reducing human touchpoints while maintaining accuracy and compliance.

Dash says the initiative has delivered quantifiable benefits through improved efficiency, accuracy, and quality of services provided to clients.

“The initiative delivers on our business goals, makes us more efficient, provides customers better service, and it opens up the capabilities and bandwidth of our people to do what they like to do and to find innovative ways to serve our business,” he adds.

Nationwide partnership platform delivers efficiencies, business growth

Organization: Nationwide

Project: Enterprise Digital Platform (EDP)

IT leader: Michael Carrel, EVP and CTO

Nationwide’s new Enterprise Digital Platform (EDP) gives the company “a scalable way to connect with external partners quickly, securely, and consistently across all areas of our business,” says company EVP and CTO Michael Carrel.

He explains that “instead of treating every integration as a custom effort, EDP creates a common front door for digital products, documentation, onboarding and governance.”

That innovation has produced better experiences for the company’s partners. It saves time for Nationwide teams, partners, and customers. And it supports faster launch times for new products and enables growth across the business.

“EDP changed the model from fragmented, point-to-point integrations into an enterprise platform built around reusable digital products. That shift lets us support a range of integration options in one governed environment, meet partners at different stages of technical maturity, and add new capabilities over time without redesigning every relationship from scratch,” Carrel explains.

EDP uses cloud-native microservices, role-based access control, and advanced analytics. Nationwide IT created modular microservices to make EDP more scalable, resilient, and adaptable. And IT decoupled it from infrastructure-specific dependencies so that it would be a platform-agnostic developer portal. That, Carrel says, reduced operational constraints across environments.

Additionally, IT shifted from a user-specific model to role-based access, which improved security, simplified administration, and better served the needs of different audiences.

Meanwhile, robust analytics delivers visibility into platform usage and performance, which Carrel says helps ensure Nationwide continuously evolves the platform based on measurable outcomes.

The core platform is fully deployed, with Nationwide planning to expand it.

“Our Enterprise Digital Platform is more than a piece of technology,” Carrel notes, “it represents a strategic enabler to support growth objectives across Nationwide’s businesses.”

PITT Ohio fast-tracks shipment requests with AI assist

Organization: PITT Ohio

Project: No Touch Email (N@TE AI)

IT leader: Scott Sullivan, President and CEO (formerly CIO)

As PITT Ohio started its AI journey in 2024, the mandate was clear: Use the technology to solve “real problems,” says Ryan Carner, director of enterprise IT solutions.

“We wanted to hit the ground running and find a problem that was solvable,” Carner says, noting that the company also wanted to use the experience to build in-house AI skills. “The idea was to find a business case for AI that would be our first but not the only one.”

PITT Ohio leaders decided to tackle what Carner describes as a “mundane but very important task for how our business operates”: handling emails to the customer service team.

The need was significant. Customer service representatives were manually processing hundreds of pickup request emails daily, each requiring five to 15 minutes to interpret and re-enter shipment details into the company’s transportation management system (TMS). The emails were complicated, containing a lot of information submitted in nonstandardized ways and varying formats. This repetitive task consumed valuable time, introduced errors, and delayed customer response.

N@TE uses generative AI and natural language processing to transform unstructured email content into structured pickup orders automatically and in real-time. N@TE scans incoming emails, extracts key shipment data, and creates orders directly in the TMS via API integration. It operates seamlessly within existing workflows, requiring no change in customer behavior or retraining of staff.

PITT Ohio deployed N@TE in 2025, and the company also secured a patent for the product that year. N@TE has produced a 30-60X increase in processing speed, 99% accuracy in extracting and populating order data, and a 70% reduction in handling costs per pickup order.

SMU builds AI adoption through grassroots ambassador program

Organization: Southern Methodist University

Project: Scaling AI Without Scaling AI: Organizational AI Scaling Through Willingness

IT leader: Jason Warner, Associate CIO

Like executives in most organizations, leaders at Southern Methodist University encountered mixed attitudes about AI. Some workers had little interest in using the tech, others were afraid it would take jobs, still others were curious about what it could do.

Associate CIO Jason Warner and other leaders decided to leverage that last group, believing the best way to get SMU faculty and staff to embrace AI was to use enthusiasts to help smooth the way.

So, instead of treating AI as a conventional technology rollout, Warner and his colleagues built opt-in communities of practice known as the AI Coalition of the Willing and Operation Copilot.

The goal, Warner says, was to build institutional capability, reduce risk, and generate momentum.

“We knew the fastest way to scale AI was to scale the willingness of people to use the technology, and not talking to people about cost savings and the like,” Warner says, adding that willing users as great ambassadors and evangelists who showcase in formal and informal ways the technology’s potential for hesitant or skeptical colleagues.

Participating faculty members have access to a licensed ChatGPT account as long as they use it. Staff members have access to Copilot accounts after taking a self-paced training course and likewise must use it to keep that access.

Warner says these willing workers are demonstrating the benefits of AI (significant time reclamation, reduced cognitive load, improved quality of outputs, expanded professional capacity).

SMU is now moving to a single solution and scaling AI, confident that its use will deliver returns following in the footsteps of the early adopters.

Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here 

Each year the CIO 100 Awards showcase outstanding IT initiatives, and every year they illustrate the power and potential of technology to transform how people work, how organizations perform, and the value they offer to customers.

The 2026 cohort of winners is no different. Each one demonstrates how IT executives and their teams successfully move from ideation to deployment to scaling a solution for the future, overcoming challenges and driving adoption along the way to ensure their organization gets a return on its investment.

[ Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here ]

The winning initiatives come from a range of industries and utilize a host of technologies to achieve their goals, as is the case annually. A growing proportion of these stand-out projects leverage artificial intelligence, raising the bar on the art of the possible for all IT departments.

The following 10 award-winning projects serve as representatives for the outstanding work done by all the 2026 honorees.

ABB democratizes AI agent creation and deployment

Organization: ABB

Project: ABBY — AI Agentic Platform for Workforce Transformation

IT leader: Vikke Kandell, CIO

IT leaders at ABB, a manufacturer, had some big hurdles to clear when it came to building an AI strategy.

They had to overcome employee fears that AI would take away jobs, the potentially high cost of AI vendor licenses, and pressure from investors, customers, and executives to advance the use of AI in the enterprise.

“We looked at this and asked, ‘How do we address all this?’ and build something that the company is proud of,” says Babu Kuttala, vice president of data analytics and AI.

The answer is ABBY, an AI agentic platform that enables employees to create and deploy specialized AI agents for specific business tasks.

To build ABBY, Kuttala and his team used best-of-breed LLMs (about 25 in total). They built a centralized orchestration layer using generative AI that integrates internal knowledge bases with external ecosystems, creating a unified platform where agents can access enterprise data, understand required actions, and execute tasks across multiple systems. And they created preconfigured skills so that employees could build agents tailored to their workflows without having to code.

ABBY was rolled out in 2025 to 100 users but is now used by 63,000 (more than 75% of the company’s workforce, Kuttala notes) with an average of 10,000-plus workers using it daily. IT continues to add LLMs and capabilities to expand use of ABBY even further, Kuttala says.

Belcorp modernizes manufacturing with Smart Factory

Organization: Belcorp

Project: QPlant — Smart Factory

IT leader: Venkat Gopalan, Chief Digital, Data, and Technology Officer

Legacy processes were limiting Belcorp’s ability to scale and compete. Its manufacturing relied on ERP-driven processes with limited shop-floor automation and weak connectivity across production, packaging, quality, and maintenance. The company depended heavily on manual records and post-process reconciliation, resulting in fragmented data, limited real-time insight, inefficiencies, and higher risks for errors.

Smart Factory changed all that. The IT initiative reimagined how manufacturing teams work “by creating a connected, data-driven environment where production, quality, maintenance, and operations are aligned around real-time information and standardized execution,” says Venkat Gopalan, chief digital, data, and technology officer.

At Smart Factory’s core is a manufacturing execution system that orchestrates production workflows, quality processes, and operational execution, he explains. IoT-enabled equipment integration and a centralized SCADA platform provide real-time visibility into shop-floor operations, while electronic batch records digitize production execution, strengthen traceability, and reinforce compliance by design.

Integrating those operational technologies with the company’s enterprise platforms was another critical component of success, Gopalan says, creating a trusted flow of real-time data across manufacturing, quality, maintenance, and business systems. “This connected architecture transformed isolated data into actionable insights, enabling faster decision-making, greater operational visibility, and continuous improvement across the manufacturing lifecycle,” he adds.

The initiative generated more than $1 million in financial benefits in its first year alone.

“Most importantly, Smart Factory established the digital foundation for the future of manufacturing at Belcorp,” Gopalan says. “With real-time operational data and connected systems now in place, we’re well positioned to accelerate advanced analytics, AI-driven optimization, predictive maintenance, and other Industry 4.0 capabilities that will continue delivering value for years to come.”

Cohesity replatforms post-acquisition for commercial growth

Organization: Cohesity

Project: Lead to Cash Replatforming Program (Veritas Integration)

IT leader: Brian Spanswick, CIO

Cohesity set an ambitious objective: Complete an enterprise-scale lead-to-cash replatform in under six months.

That’s a tight timeline for any replatforming initiative, but Cohesity’s project had another layer of complexity. It followed Cohesity’s December 2024 acquisition of Veritas, a company twice its size in revenue, leaving Cohesity to integrate the majority of a global enterprise revenue engine into its own operating model without disrupting customers, partners, or sellers.

“We had to bring the two companies together, merge the workforces together, and create an overall harmonized organization and operating infrastructure platform,” says Eric Brown, who as CFO and COO led the project.

The program migrated heavily customized CRM, CPQ, PRM, ERP, and subscription platforms (some of which were “very brittle, very bespoke,” Brown says) to a unified SaaS CRM, CPQ, and ERP environment with uninterrupted selling, billing, and partner operations.

This was no lift-and shift, Brown stresses. “It was a business process optimization project as well. We want to run very efficiently, so we questioned everything and used the migration process to simplify and streamline the business in every possible respect.”

The initiative enabled continuity for 13,000-plus customers, protected revenue during integration, and established a scalable commercial foundation for future growth.

Brown cites several factors that contributed to success. First, leadership was upfront about what it would take to meet the deadline, a process that involved carefully prioritizing the capabilities that would appear in the first iteration. Leadership also streamlined decision-making, establishing office hours that “ran with military precision” to handle issues. And the company selected a specialized partner, requiring its top talent be assigned to Cohesity.

Dairyland Power goes agentic to protect field crews

Organization: Dairyland Power Cooperative

Project: ODIN — Organizational Effectiveness Agentic AI

IT leader: Nate Melby, VP and CIO

Dairyland Power Cooperative had amassed a large collection of field observations, incident reports, near-misses, safety rules, and work methods that could yield insights into processes and practices that could help protect its workers.

But the insights were essentially out of reach, trapped in siloes.

Dairyland’s organizational effectiveness team turned to CIO Nate Melby for help unlocking those insights. Melby then turned to agentic AI, recognizing that the technology could address the team’s need to make better use of its data.

“This was about finding insights on how to work more safely,” Melby says. “It’s about preventing incidents.”

The collaboration between the two teams created ODIN, the first agentic AI implementation of its kind in the electric utility industry.

Focused on worker safety, ODIN autonomously connects the collective safety knowledge of the organization and delivers actionable insights directly to field crews at the moment work is planned.

ODIN was developed through a hybrid approach that combined an agentic AI platform and Dairyland’s internal private generative AI platform called VoltWrite. ODIN leverages LLMs, retrieval-augmented generation, and a coordinated swarm of autonomous agents.

Agents work together to analyze internal safety data, performance history, work practices, and safety rules and then synthesize the information into clear guidance on the safest way to perform specific tasks.

ODIN has produced results, including a reduction in OSHA recordable injuries and improvements in the quality and consistency of pre-job safety briefings.

ODIN was deployed in early 2025 for use by Dairyland’s workers in transmission construction and electrical maintenance, which are the highest-risk work areas. Dairyland is looking to expand ODIN’s use to other teams.

Dow’s digital sustainability ledger drives low-carbon sales

Organization: Dow

Project: Carbon Footprint Ledger

IT leader: Deb Bauler, Chief Information and Digital Officer

Executives at Dow consider the Carbon Footprint Ledger (CFL) as more than a technology or innovative carbon accounting methodology. According to Senior Global IT Director Jeremy Preston, CFL is “a digital business capability that enables Dow to translate sustainability investments into customer value.”

CFL transformed how Dow uses greenhouse gas emissions data. It combines a methodology aligned to international standards with an enterprise-scale digital platform. It also integrates manufacturing, supply chain, commercial, and sustainability data to generate product carbon footprints under enterprise-level governance and management at scale.

In doing so, Preston says it creates “a trusted, traceable link between low-carbon processes and raw materials implemented across its manufacturing network and the lower-carbon products customers seek.”

The technology team worked closely with sustainability and business teams, collaboratively developing the capabilities needed to reconstruct product genealogy, maintain end-to-end data lineage, track low-carbon attributes across interconnected manufacturing processes, and generate product carbon footprints that can support customer offerings and commercial transactions.

CFL was built on Dow’s Integrated Data Hub and in partnership with Boston Consulting Group and Databricks.

The core CFL platform is fully deployed and supports commercial transactions today.

Preston says CFL “enables Dow to turn sustainability investments into customer value, commercial differentiation, and new growth opportunities.” Dow reports that it has driven hundreds of millions of dollars in low-carbon product sales in 2025 and 2026.

The company is now expanding its use. “We are extending adoption across additional products, manufacturing networks, business segments, and customer use cases while continuing to enhance automation, analytics, and integration with commercial processes,” Preston says.

J&J transforms quality management with AI

Organization: Johnson & Johnson

Project: Q&C Strategy

IT leader: Michael Comprelli, Vice President, Head of Technology, Technical Operations, and Risk; Joel O’Connor, Head of Technology, Medtech Quality, and Compliance

Johnson & Johnson is using AI to transform quality management through its Q&C Strategy.

QuIn is an AI-powered digital assistant that fuses human expertise with machine learning, automation, and data-driven insights to boost efficiency, reliability, and worker impact. By embedding gen AI into core quality management systems processes, QuIn proactively gathers actionable insights, increases operational efficiency, and allows teams to focus on high-value, patient-centric work.

Cora is an innovative generative AI platform that provides regulatory intelligence monitoring, impact analysis, and augmented content revision. Cora assists with document analysis, compliance comparison, stakeholder analysis, policy/standard creation, procedural/document updates, and document comparison. Cora is purpose-built for regulated environments, validating outputs against source material and offering a user experience that instills trust in the outcome.

QuIn and Cora, which automate time-intensive tasks and democratize information access, are on track to deliver significant value, with J&J reporting more than $62 million in documented true cost savings by 2028 from QuIn alone. Cora delivered $2 million in cost efficiency in 2025 and will deliver a documented cost savings of $25 million by 2028.

“Our teams proved responsible AI can be applied meaningfully in a highly regulated environment without compromising the rigor, accountability, or human judgment that quality requires,” says Michael Comprelli, vice president, head of technology, technical operations, and risk.

He continues, saying that J&J “moved these ideas beyond experimentation and into products that employees use in their daily work. We did that by bringing together Quality expertise, product management, data engineering, architecture, cybersecurity, user-experience design and AI engineering around a common purpose.”

JLL brings intelligent automation to business services

Organization: JLL

Project: Business Service Digitization

IT leader: Pinak Dash, Global Head of JLL Business Services and Legal Technologies

JLL launched its digitization initiative to drive process redesign as well as systematic AI and RPA deployment across JLL Business Services (JBS).

The initiative was designed to address inefficiencies that hampered scalability and competitive positioning. It was also designed to eliminate manual processes that consumed thousands of hours across finance, HR, legal, procurement, marketing, research, IT, and lease administration.

Pinak Dash, global head of JBS and legal technologies, says the digitization initiative had a dual-strategy combining traditional digitization with generative AI innovation to hundreds of processes.

JLL lists three innovations critical to the program’s success.

First is a hybrid platform that integrates RPA with JLL’s proprietary AI platform called Falcon, which created intelligent automation that adapts and learns. It enables real-time process automation, intelligent document processing with automated extraction/validation, and smart decision-making for continuously optimizing workflows.

The second innovation is its use of ProHance for real-time process monitoring and enabling of data-driven optimization. Sensors capture granular productivity metrics, identify bottlenecks, and provide actionable insights for continuous improvement across automated and manual processes.

Third is its custom AI assistants and transaction agents. Falcon-powered assistants provide intelligent knowledge search while specialized agents execute complex transactions across enterprise SaaS platforms. These handle multisystem workflows, reducing human touchpoints while maintaining accuracy and compliance.

Dash says the initiative has delivered quantifiable benefits through improved efficiency, accuracy, and quality of services provided to clients.

“The initiative delivers on our business goals, makes us more efficient, provides customers better service, and it opens up the capabilities and bandwidth of our people to do what they like to do and to find innovative ways to serve our business,” he adds.

Nationwide partnership platform delivers efficiencies, business growth

Organization: Nationwide

Project: Enterprise Digital Platform (EDP)

IT leader: Michael Carrel, EVP and CTO

Nationwide’s new Enterprise Digital Platform (EDP) gives the company “a scalable way to connect with external partners quickly, securely, and consistently across all areas of our business,” says company EVP and CTO Michael Carrel.

He explains that “instead of treating every integration as a custom effort, EDP creates a common front door for digital products, documentation, onboarding and governance.”

That innovation has produced better experiences for the company’s partners. It saves time for Nationwide teams, partners, and customers. And it supports faster launch times for new products and enables growth across the business.

“EDP changed the model from fragmented, point-to-point integrations into an enterprise platform built around reusable digital products. That shift lets us support a range of integration options in one governed environment, meet partners at different stages of technical maturity, and add new capabilities over time without redesigning every relationship from scratch,” Carrel explains.

EDP uses cloud-native microservices, role-based access control, and advanced analytics. Nationwide IT created modular microservices to make EDP more scalable, resilient, and adaptable. And IT decoupled it from infrastructure-specific dependencies so that it would be a platform-agnostic developer portal. That, Carrel says, reduced operational constraints across environments.

Additionally, IT shifted from a user-specific model to role-based access, which improved security, simplified administration, and better served the needs of different audiences.

Meanwhile, robust analytics delivers visibility into platform usage and performance, which Carrel says helps ensure Nationwide continuously evolves the platform based on measurable outcomes.

The core platform is fully deployed, with Nationwide planning to expand it.

“Our Enterprise Digital Platform is more than a piece of technology,” Carrel notes, “it represents a strategic enabler to support growth objectives across Nationwide’s businesses.”

PITT Ohio fast-tracks shipment requests with AI assist

Organization: PITT Ohio

Project: No Touch Email (N@TE AI)

IT leader: Scott Sullivan, President and CEO (formerly CIO)

As PITT Ohio started its AI journey in 2024, the mandate was clear: Use the technology to solve “real problems,” says Ryan Carner, director of enterprise IT solutions.

“We wanted to hit the ground running and find a problem that was solvable,” Carner says, noting that the company also wanted to use the experience to build in-house AI skills. “The idea was to find a business case for AI that would be our first but not the only one.”

PITT Ohio leaders decided to tackle what Carner describes as a “mundane but very important task for how our business operates”: handling emails to the customer service team.

The need was significant. Customer service representatives were manually processing hundreds of pickup request emails daily, each requiring five to 15 minutes to interpret and re-enter shipment details into the company’s transportation management system (TMS). The emails were complicated, containing a lot of information submitted in nonstandardized ways and varying formats. This repetitive task consumed valuable time, introduced errors, and delayed customer response.

N@TE uses generative AI and natural language processing to transform unstructured email content into structured pickup orders automatically and in real-time. N@TE scans incoming emails, extracts key shipment data, and creates orders directly in the TMS via API integration. It operates seamlessly within existing workflows, requiring no change in customer behavior or retraining of staff.

PITT Ohio deployed N@TE in 2025, and the company also secured a patent for the product that year. N@TE has produced a 30-60X increase in processing speed, 99% accuracy in extracting and populating order data, and a 70% reduction in handling costs per pickup order.

SMU builds AI adoption through grassroots ambassador program

Organization: Southern Methodist University

Project: Scaling AI Without Scaling AI: Organizational AI Scaling Through Willingness

IT leader: Jason Warner, Associate CIO

Like executives in most organizations, leaders at Southern Methodist University encountered mixed attitudes about AI. Some workers had little interest in using the tech, others were afraid it would take jobs, still others were curious about what it could do.

Associate CIO Jason Warner and other leaders decided to leverage that last group, believing the best way to get SMU faculty and staff to embrace AI was to use enthusiasts to help smooth the way.

So, instead of treating AI as a conventional technology rollout, Warner and his colleagues built opt-in communities of practice known as the AI Coalition of the Willing and Operation Copilot.

The goal, Warner says, was to build institutional capability, reduce risk, and generate momentum.

“We knew the fastest way to scale AI was to scale the willingness of people to use the technology, and not talking to people about cost savings and the like,” Warner says, adding that willing users as great ambassadors and evangelists who showcase in formal and informal ways the technology’s potential for hesitant or skeptical colleagues.

Participating faculty members have access to a licensed ChatGPT account as long as they use it. Staff members have access to Copilot accounts after taking a self-paced training course and likewise must use it to keep that access.

Warner says these willing workers are demonstrating the benefits of AI (significant time reclamation, reduced cognitive load, improved quality of outputs, expanded professional capacity).

SMU is now moving to a single solution and scaling AI, confident that its use will deliver returns following in the footsteps of the early adopters.

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Die nächste große KI-Welle wird kein Chatbot auf Ihrem Laptop sein oder ein Agent, der im Hintergrund Besprechungsnotizen automatisch in Projekttickets umwandelt. Stattdessen wird sie aus KI bestehen, die Geräte steuert, sich bewegt und mit ihrer Umgebung interagiert.

Unter Physical AI versteht man die Integration von künstlicher Intelligenz (KI) in autonome Systeme, wodurch diese ihre Umgebung wahrnehmen und komplexe Handlungen in der physischen Welt ausführen können. Der Markt für Physical AI wird derzeit auf etwa 92 Milliarden Dollar geschätzt und soll laut PwC bis 2030 auf über 489 Milliarden Dollar anwachsen.

Für viele Menschen weckt der Begriff „physische KI“ Bilder von Robotern, die in einer Fabrikhalle Teile montieren, oder von selbstfahrenden Autos. Beide Beispiele gehören tatsächlich zu den wichtigsten Anwendungsfällen. Physical AI kommt aber auch in Sicherheitskameras, Ampeln, Inspektionsrobotern, medizinischen Geräten und vielen weiteren Bereichen zum Einsatz.

Was gute Physical-AI-Anwendungsfälle auszeichnet

IT-Führungskräfte sollten bei der Suche nach Einsatzmöglichkeiten für physische KI über humanoide Rotober hinausdenken, erklärt Adnan Masood, Chief-AI Architect beim Anbieter für digitale Transformation UST. „Ich gebe CIOs meist den Rat: Lassen Sie sich nicht vom Hype um humanoide Roboter blenden. Der kurzfristige Mehrwert liegt in adaptiver Automatisierung in variablen Umgebungen – dort, wo sich Bedingungen verändern, Menschen und Maschinen gemeinsam arbeiten und Ausfallzeiten hohe Kosten verursachen.“

Der ideale Einsatzort für Physical AI seien jedoch Aufgaben, die sicher, wiederholt und nachvollziehbar ausgeführt werden können und sich in bestehende Sicherheits- und Compliance-Vorgaben integrieren lassen, fügt Masood hinzu.

Damit Physical AI entsprechende Wirkung entfalten kann, müssten einige Voraussetzungen erfüllt sein, ergänzt Vikram Venkat, Investor für physische KI-Systeme bei Cota Capital.

An erster Stelle stehe ein hoher Arbeitskräftebedarf – etwa, wenn bereits Fachkräftemangel herrsche, absehbar sei oder wenn Tätigkeiten für Menschen gefährlich sind. Zudem sollte die Einsatzumgebung vergleichsweise kontrolliert und überschaubar sein, da heutige Physical-AI-Plattformen mit stark wechselnden oder unvorhersehbaren Bedingungen noch Schwierigkeiten haben. Schließlich sollte die Aufgabe wiederholbar sein, oft in großem Umfang, und klare, messbare Ergebnisse liefern, fügt er hinzu.

Für den kurzfristigen Erfolg nennt Venkat zwei weitere wichtige Kriterien:

• Einfache Implementierung: Die Einführung sollte einfach sein und nur minimale Änderungen an bestehenden Prozessen, zusätzliche Infrastruktur oder Integrationen in bestehende Systeme erfordern.
• Human in the Loop: Mitarbeiter, die in den Prozess eingebunden sind, sollten jederzeit in der Lage sein, Fehler zu korrigieren.

Die 7 wichtigsten Physical-AI-Anwendungsfälle

Trotz dieser Einschränkungen sei das Potenzial der physischen KI enorm, betont Albert Liu, Gründer und CEO des Anbieters von Edge-AI-Lösungen Kneron.

„Die meisten Menschen verbinden Physical AI zunächst mit Robotern –einfach, weil diese bislang die sichtbarste Form dieser Technologie sind.“, bemerkt er.

Bei physischer KI gehe es jedoch nicht nur um Maschinen, die sich bewegen, sondern um Umgebungen, die intelligent werden.

Nachfolgend sieben vielversprechende Anwendungsbereiche für physische KI-Systeme.

1. Fertigungsroboter

Wer beim Stichwort Physical AI an Roboter denkt, die Autos oder andere Produkte herstellen, liegt gar nicht so falsch: Tatsächlich zählt dieser Bereich zu den etabliertesten Anwendungsfeldern und mehrere Hersteller bieten bereits industrielle Robotersysteme an. Den Marktvolumen für Industrierobotik schätzt Mordor Intelligence für 2026 auf 54,3 Milliarden Dollar. Bis 2031 gehen die Analysten davon aus, dass dieser Wert auf 94,4 Milliarden Dollar anwächst.

Ein konkretes Beispiel für diesen Use Case liefert der Automobilhersteller BMW. Das Unternehmen setzt in einem Werk in den USA humanoiden Roboter ein, die unter anderem Schweißarbeiten an Fahrzeugkomponenten übernehmen.

2. Qualitätsprüfung und vorausschauende Instandhaltung

Physische KI in Produktionsumgebungen wird längst nicht mehr ausschließlich für die Montage eingesetzt. Ebenso wichtig sind Anwendungen im Materialtransport, bei der automatisierten Qualitätskontrolle sowie bei der Fehlererkennung. Insbesondere Arbeitskräftemangel und gut strukturierte Produktionsumgebungen begünstigten den Einsatz solcher Systeme, wie Venkat anmerkt.

Ein weiteres ideales Einsatzgebiet ist die vorausschauende Wartung (Predictive Maintenance). KI könne kontinuierlich überwachen, ob die Software, welche Maschinen steuert, ordnungsgemäß funktioniert, erklärt UST-Experte Adnan Masood. „Agentenbasierte Pipelines sind mittlerweile in der Lage, Hardware-Schaltpläne und Chip-Pinbelegungen nativ auszulesen, automatisch Regressionstests zu erzeugen, die Ingenieure früher von Hand programmiert haben, und Live-Telemetriedaten der Anlagen mit digitalen Zwillingen zu vergleichen“ führt er aus. Dadurch ließen sich Firmware-Fehler und Probleme bei der Signalübertragung erkennen, bevor sie die Produktion beeinträchtigen.

Ein prominentes Beispiel ist der vierbeinige Roboter „Spot“ von Boston Dynamics. Nach Angaben des Unternehmens sind bereits Tausende dieser Roboter in mehr als 40 Ländern im Einsatz – unter anderem bei Intel, Chevron, Michelin und Cargill. „Spot“ automatisiert Industrieinspektionen, unterstützt die vorausschauende Wartung und übernimmt Sicherheitsrundgänge.

Boston Dynamics vertreibt zudem „Stretch“, der das Entladen von Anhängern und Containern automatisiert, sowie „Atlas“, einen humanoiden Roboter, der Produkte heben, sortieren und montieren kann.

Auch kamerabasierte Physical-AI-Systeme gewinnen an Bedeutung. Intelligente Kameras und Sensoren können Produktionsprozesse überwachen und Qualitätsprüfungen direkt an der Fertigungslinie durchführen.

Parm Sandhu, Group Vice President für Enterprise AI, Edge Computing und Digital Innovation bei NTT DATA, erläutert:

„Unternehmen möchten sicherstellen, dass Produkte bereits beim ersten Fertigungsdurchlauf korrekt hergestellt werden. Wir setzen Foundation Models ein, die mit Kameras verbunden sind und durch selbstständiges Lernen sehr schnell die Standardabläufe einer Produktionsstation verstehen.“

3. Autonome Fahrzeuge und Drohnen

Die Vision selbstfahrender Autos prägt seit Jahren die öffentliche Wahrnehmung von physischer KI. Bereits 2025 hatte der Markt für autonome Fahrzeuge – unabhängig vom allgemeinen Markt für Physical AI –ein Volumen von mehr als 200 Milliarden Dollar, so Global Market Insights.

Doch es dreht sich dabei nicht nur um Autos: Autonome Landmaschinen, darunter Traktoren, Erntemaschinen und Drohnen, stellen einen wachsenden Markt dar, wobei die Schätzungen zur Marktgröße stark variieren. Global Market Insights schätzte den Markt im Jahr 2025 auf 70,9 Milliarden Dollar. Bis 2035 soll er den Auguren zufolge auf 144,7 Milliarden Dollar anwachsen.

Auch Drohnen, die von KI gesteuert werden, eröffnen vielfältige Anwendungsmöglichkeiten. Dazu gehören militärische Anwendungen ebenso wie Lieferdienste für Lebensmittel oder Pakete. Unternehmen wie Amazon experimentieren bereits seit einigen Jahren mit Drohnenlieferungen.

Eine Anwendung, die eine Brücke zwischen den Anwendungsfällen für autonome Fahrzeuge und der Fertigungsindustrie schlägt, sind selbstfahrende Gabelstapler. NTT DATA arbeitet gemeinsam mit dem Gabelstaplerhersteller Hyster-Yale daran, die Fahrzeuge mit autonomen Fahrfunktionen auszustatten – unter anderem als Reaktion auf den Arbeitskräftemangel, so Sandhu:

„Wenn man an die Fertigungsindustrie denkt, wurde nahezu jedes Produkt irgendwann einmal von einem Gabelstapler bewegt – oder zumindest seine Komponenten. Doch immer weniger Menschen möchten Gabelstapler fahren. Das ist ein enormes Problem.“

4. Flotten- und Lagerkoordination

Physical AI, integriert in LKWs, Roboter und intelligente Regale, kann den Material- und Warenfluss vom Lager bis zum Endkunden verfolgen und besser koordinieren. Roboter kommissionieren, sortieren und transportieren dabei Güter, während KI die Telemetriedaten von Fahrzeugflotten nutzt, um Transportrouten besser zu planen.

KI-Modelle sind mittlerweile in der Lage, Tausende autonomer mobiler Roboter über Fulfillment-Netzwerke hinweg zu koordinieren – was Masood von UST als eine Art „Flugsicherung für Roboter“ bezeichnet.

Die eigentliche Intelligenz befinde sich dabei nicht im einzelnen Roboter, sondern in der Koordinationsschicht, hält er fest. Diese entscheide, welcher Roboter welche Aufgabe übernimmt, lege die Reihenfolge der Aufträge fest und verhindere Konflikte zwischen den Fahrzeugen. „Sie lässt sich in einer Weise skalieren, wie es die Programmierung einzelner Roboter niemals könnte“, so Masood.

Dass diese Technologie inzwischen produktiv eingesetzt wird, zeigt das Beispiel des Unternehmens Symbotic. Eigenen Angaben zufolge betreibt der Anbieter von physischer KI für Lagerhäuser weltweit 22.000 autonome mobile Roboter, die allein im Jahr 2025 zusammen mehr als 200 Millionen Meilen zurücklegten. Der aktivste einzelne Roboter fuhr dabei knapp 84.000 Kilometer – mehr als zweimal um die Erde.

5. Überwachung und physische Sicherheit

BostonDynamics

Die Einsatzmöglichkeiten physischer KI im Bereich der physischen Sicherheit reichen weit über Patrouillenroboter wie Spot von Boston Dynamics hinaus. Immer häufiger werden Videokameras, Sensoren und weitere Sicherheitssysteme mithilfe von KI miteinander vernetzt. Dadurch entsteht eine kontinuierliche, intelligente Überwachung von Gebäuden, Betriebsgeländen oder ganzen Campusarealen.

Ein Beispiel dafür liefern Artificial Intelligence Technologies Solutions und die Tochtergesellschaft Robotic Assistance Devices (RAD). Beide Unternehmen verbinden Kameras, stationäre Sicherheitskomponenten und autonome Fahrzeuge zu einem umfassenden Sicherheitsnetzwerk.

Kernstück ist die agentenbasierte KI-Plattform „SARA“ (Speaking Autonomous Responsive Agent). Diese ist darauf ausgelegt ist, Kameras, fest installierte Sicherheitsvorrichtungen, autonome Patrouillenfahrzeuge, Beleuchtung, Lautsprecher, Überwachungssysteme und menschliches Sicherheitspersonal zu koordinieren.

SARA kann eingehende Sicherheitsmeldungen analysieren, Vorfälle verifizieren, direkt mit Personen vor Ort kommunizieren und – sofern freigegeben – geeignete Reaktionen automatisch einleiten. Nach Angaben der Unternehmen lassen sich dadurch wertvolle Minuten gegenüber einer ausschließlich menschlichen Reaktion einsparen.  

Ein weiteres Beispiel für den Einsatz von Physical AI im Sicherheitsbereich sind intelligente Metalldetektoren mit integrierter KI. Das Unternehmen Athena Security bietet etwa Körperscanner an, die nach eigenen Angaben verschiedenste Waffen – darunter Rasierklingen und kleine Messer – mit hoher Genauigkeit erkennen können.

6. Intelligente Gebäude und Infrastruktur

Unternehmen könnten Physical AI nutzen, um eine Vielzahl von Kennzahlen innerhalb von Gebäuden, in Versorgungsnetzen und der TK-Infrastruktur zu überwachen, wie Masood von UST anmerkt. Die KI erkenne Auffälligkeiten und könne automatisch Warnmeldungen auslösen, Sicherheitsmaßnahmen einleiten oder Gebäudeparameter wie Klima- oder Energiesteuerung anpassen.

Auch im Gesundheitswesen gewinnt diese Technologie an Bedeutung. Krankenhäuser nutzen physische KI zunehmend zur Koordination von Pflege- und Behandlungsprozessen, während Netzbetreiber KI-gestützte selbstheilende Netzwerke einsetzen, die Störungen eigenständig erkennen und beheben können.

Liu von Kneron erwartet darüber hinaus sogenannte intelligente Concierge-Systeme in Hotels, Flughäfen und Krankenhäusern, die Wegbeschreibungen geben, Identitäten überprüfen und Dienstleistungen koordinieren.

Seiner Einschätzung nach wird KI innerhalb des nächsten Jahrzehnts nahezu jeden physischen Raum durchdringen – von Drive-in-Schaltern über Restaurants und Fabriken bis hin zu Bürogebäuden.

„Menschen werden erwarten, dass eine Sicherheitskamera Absichten erkennt, anstatt lediglich Bewegungen zu registrieren; oder dass ein Krankenhauszimmer subtile Veränderungen im Gesundheitszustand eines Patienten erkennt, bevor ein Alarm ausgelöst wird.“, so Liu.

Zur neuen Normalität gehöre außerdem bald, dass ein Verkaufsregal seinen Bestand selbst verwaltet oder dass ein Gebäude Energieverbrauch, Sicherheit und Auslastung kontinuierlich optimiert.

7. Smart Cities

Auch Städte beginnen zunehmend, physische KI in ihre Infrastruktur zu integrieren. Intelligente Systeme werden beispielsweise in Verkehrsleitsystemen, Ampelanlagen und Überwachungskameras eingesetzt, um Verkehrsflüsse zu analysieren und kommunale Dienstleistungen effizienter zu organisieren.

So können etwa KI-gestützte Verkehrssysteme den Verkehrsfluss verbessern, Kreuzungen überwachen und die Verkehrssicherheit erhöhen, ohne ausschließlich auf menschliche Beobachtung angewiesen zu sein.

Ein Beispiel dafür ist der Sensorhersteller Ouster, der gemeinsam mit dem Verkehrsministerium des US-Bundesstaates New Jersey 42 Kreuzungen mit LiDAR-Sensoren ausstattete. Ziel war es, im Vorfeld der Fußball-Weltmeisterschaft Verkehrs- und Fußgängerstaus besser zu erkennen und zu steuern.

Ein weiteres Beispiel stammt von NTT DATA, das mit der Stadt Brownsville (Texas) an einem stadtweiten KI-gestützten Benachrichtigungssystem arbeitet. Dieses informiert beispielsweise städtische Mitarbeiter, wenn Abfallbehälter in Parks geleert werden müssen, und unterstützt Polizeibeamte beim Erstellen von Einsatzberichten. (mb)

Dieser Artikel basiert auf einem Beitrag von CIO.com.

The next big AI wave won’t be a chatbot in your laptop, or an agent that works behind the scenes to turn meeting notes into project tickets, but AI that takes control of devices that move and interact with the environment.

Physical AI can be defined as the integration of AI into autonomous systems, allowing them to perceive the environment around them and perform complex actions in the physical world. The physical AI market, currently valued at about $92 billion, is projected by PwC to surpass $489 billion by 2030.

For many people, physical AI may conjure images of robots building widgets on a factory floor, or a self-driving car. Both examples are among the top use cases for physical AI, but physical AI is also being integrated into security cameras, traffic lights, inspection robots, medical devices, and more.

What makes a strong use case for physical AI

IT leaders thinking about how to use physical AI should think beyond the human-shaped robots that generate a lot of attention, says Adnan Masood, chief AI architect at digital transformation provider UST.

“I usually have one caution for CIOs — skip the humanoid theater,” he says. “The near-term advantage is adaptive automation in variable environments where conditions change, humans share the space, and downtime is expensive.”

The sweet spot for physical AI is when it can run safely and repeatedly and can be audited within existing safety and compliance regimes, he adds.

For physical AI to make a big impact, a handful of conditions must exist, adds Vikram Venkat, investor in physical AI systems at Cota Capital.

First, there should be a major labor component, such as existing or expected labor shortages or conditions that make the work dangerous for humans, he says. In addition, the environment should be relatively constrained, because physical AI platforms generally aren’t yet proficient at handling highly variable environments.

Finally, the task should be repeatable, often at high volumes, and have clear measurable outcomes, he adds.

In the short term, a couple of other conditions should exist, Venkat says. First, deployments should be simple, and require minimal changes to existing processes, additional infrastructure, or integrations into existing systems. Second, humans in the loop should be able to correct errors.

Top use cases for physical AI

Despite those constraints, physical AI’s potential is huge, says Albert Liu, founder and CEO of edge AI solutions vendor Kneron.

“Most people think physical AI begins with robots, which is simply the example our minds go to since it has been the most visible until now,” he notes. “But physical AI isn’t just about the typical answer — machines that move — it’s about environments that become intelligent.”

With several caveats in mind, here are seven promising uses for physical AI systems.

Manufacturing robots

When thinking about physical AI, many people may envision robots manufacturing cars or other products. That’s certainly happening, with several vendors offering builder robots for sale, and with the industrial robotics market valued at $54.3 billion in 2026, growing to $94.4 billion by 2031, according to Mordor Intelligence.

One example of robots building products comes from car maker BMW, which has used a humanoid robot to weld parts together at a plant in the US.

Quality inspection and predictive maintenance

Physical AI deployed inside manufacturing environments isn’t just being used to assemble products. The technology is also being used for material handling and automated quality inspection and defect checking, with labor shortages and constrained environments driving use, notes Venkat.

Predictive maintenance is also a sweet spot for physical AI in manufacturing. AI can be used to check that the software powering equipment is working correctly, says UST’s Masood.

“Agentic pipelines now read hardware schematics and chip pinouts natively, generate the regression suites engineers once scripted by hand, and compare live equipment telemetry against digital twins to catch firmware regressions and signal-integrity faults before a production run,” he says.

Boston Dynamics’ four-legged Spot is an example of a marriage between robotics and AI, with the company saying thousands of robots have been deployed across 40 countries at companies such as Intel, Chevron, Michelin, and Cargill. Spot is used to automate industrial inspections, conduct predictive maintenance, and go on security patrols.

Boston Dynamics also sells Stretch, which automates the unloading of trailers and containers, and Atlas, a humanoid robot that can lift, sort, and assemble products.

Physical AI embedded into cameras and sensors can provide quality control inspections on factory floors, notes Parm Sandhu, group vice president for enterprise AI, edge computing, and digital innovation at IT solutions provider NTT DATA.

“They want to make sure the products built right the first time,” he says. “We use a foundation model, set up with cameras and trained in self-learning, so it very can very quickly learn standard operating procedure for one factory station.”

Autonomous vehicles and drones

The promise of self-driving cars entered the public consciousness several years ago, and the market, separate from the physical AI market, was worth more than $200 billion in 2025, according to Global Market Insights.

Autonomous taxis are also gaining momentum, with Waymo and Tesla launching robotaxi experiments in limited areas in 2025. Uber also has huge plans for robotaxis.

But the autonomous vehicle market extends far beyond cars driving down the highway. Autonomous farm equipment, including tractors, harvesters, and drones, represent a growing market, with market size estimates varying wildly. Global Market Insights estimated the market to be worth $70.9 billion in 2025, with projections for it to reach $144.7 billion by 2035.

Drones can also be operated by an AI, leading to all kinds of applications, including military uses and food and package delivery services. Amazon and other companies have experimented with drone delivery services in recent years, and DoorDash announced in late July that it would jump into the market.

One use that staddles the autonomous vehicle and manufacturing use cases involves self-driving forklifts. NTT DATA has worked with forklift manufacturer Hyster-Yale to install self-driving capabilities into the vehicles, in part a response to labor shortages, Sandhu says.

“If you think about manufacturing, pretty much everything you touch in that world was lifted by a forklift somewhere or components were lifted by a forklift somewhere,” he says. “But people don’t want to drive forklifts, and that’s a huge problem.”

Fleet and warehouse coordination

Physical AI, built into trucks and smart shelves, can track and better coordinate the movement of materials and products, from the warehouse to the end customer. Physical AI, installed in robots, can pick, sort, and transport goods. AI can use fleet telemetry to optimize routes in the shipping fleet.

AI models can now orchestrate thousands of autonomous mobile robots across fulfillment networks, what UST’s Masood calls “air traffic control for robots.”

The AI intelligence sits in the coordination layer that routes, sequences, and removes conflicts in the fleet, he adds. “It scales in ways single-robot programming never could,” notes.

Physical AI has moved beyond pilots and is operating at enterprise scale in warehouses, according to Symbotic, a warehouse physical AI vendor.

The company’s fleet of 22,000 autonomous mobile robots that traveled more than 200 million miles in 2025, with one robot traveling more than 52,000 miles, or more than twice the distance around the Earth, the company says.

Surveillance and physical security

Physical AI’s application to physical security includes roving robots like Boston Dynamics’ Spot, but it also allows organizations to connect video cameras and other security tools to provide an ever-vigilant view of the secured environment.

Companies such as Artificial Intelligence Technologies Solutions and its subsidiary Robotic Assistance Devices are connecting several devices for a sort of security mesh across a campus or building. The companies’ Speaking Autonomous Responsive Agent (SARA) is an agentic AI platform designed to coordinate cameras, fixed security devices, autonomous patrol vehicles, lights, speakers, monitoring systems, and human security personnel.

SARA can evaluate events from physical security systems, verify security events, communicate directly with people at the site, and initiate approved responses, the companies say. The automated response can save valuable time compared to human intervention, they claim.

Another example of the use of physical AI for security involves smart metal detectors with AI embedded inside. Athena Security is one company that offers AI-powered body scanners that claim a high rate of detection for all kinds of weapons, including razor blades and small knives.

Smart buildings and infrastructure

Companies can use physical AI to monitor all kinds of metrics inside buildings and across utility grids and telecom networks, notes UST’s Masood. The AI can trigger alerts, safety interventions, or environmental controls. Hospitals are now using physical AI to coordinate care, and network operators are deploying AI-powered self-healing tools.

Physical AI will create intelligent concierges at hotels, airports, and hospitals that provide directions, verify identities, and coordinate services, Kneron’s Liu says.

Over the next decade, AI will be embedded in nearly all physical spaces, including drive-thru lanes, restaurants, factories, and offices, he predicts. “People will expect a security camera that understands intent instead of simply detecting motion, a hospital room that recognizes subtle changes in a patient’s condition before an alarm sounds, a retail shelf that manages inventory autonomously, or a building that continuously optimizes energy, security, and occupancy,” he adds.

Smart cities

Outside of traditional enterprise environments, cities are now embedding AI into traffic devices to monitor vehicle flow and into cameras to monitor community service needs.

The AI-powered systems can improve traffic flow, monitor intersections, and make roadways safer without relying only on human observation. Lidar maker Ouster worked with the New Jersey Department of Transportation to install sensors at 42 intersections ahead of the World Cup tournament to assist with road and pedestrian traffic congestion, the company says.

NTT DATA is working with Brownville, Texas, to set up a citywide alert system to send workers for incidents such as when a park’s garbage containers are full and to assist police officers in filling out reports, notes Sandhu.

The next big AI wave won’t be a chatbot in your laptop, or an agent that works behind the scenes to turn meeting notes into project tickets, but AI that takes control of devices that move and interact with the environment.

Physical AI can be defined as the integration of AI into autonomous systems, allowing them to perceive the environment around them and perform complex actions in the physical world. The physical AI market, currently valued at about $92 billion, is projected by PwC to surpass $489 billion by 2030.

For many people, physical AI may conjure images of robots building widgets on a factory floor, or a self-driving car. Both examples are among the top use cases for physical AI, but physical AI is also being integrated into security cameras, traffic lights, inspection robots, medical devices, and more.

What makes a strong use case for physical AI

IT leaders thinking about how to use physical AI should think beyond the human-shaped robots that generate a lot of attention, says Adnan Masood, chief AI architect at digital transformation provider UST.

“I usually have one caution for CIOs — skip the humanoid theater,” he says. “The near-term advantage is adaptive automation in variable environments where conditions change, humans share the space, and downtime is expensive.”

The sweet spot for physical AI is when it can run safely and repeatedly and can be audited within existing safety and compliance regimes, he adds.

For physical AI to make a big impact, a handful of conditions must exist, adds Vikram Venkat, investor in physical AI systems at Cota Capital.

First, there should be a major labor component, such as existing or expected labor shortages or conditions that make the work dangerous for humans, he says. In addition, the environment should be relatively constrained, because physical AI platforms generally aren’t yet proficient at handling highly variable environments.

Finally, the task should be repeatable, often at high volumes, and have clear measurable outcomes, he adds.

In the short term, a couple of other conditions should exist, Venkat says. First, deployments should be simple, and require minimal changes to existing processes, additional infrastructure, or integrations into existing systems. Second, humans in the loop should be able to correct errors.

Top use cases for physical AI

Despite those constraints, physical AI’s potential is huge, says Albert Liu, founder and CEO of edge AI solutions vendor Kneron.

“Most people think physical AI begins with robots, which is simply the example our minds go to since it has been the most visible until now,” he notes. “But physical AI isn’t just about the typical answer — machines that move — it’s about environments that become intelligent.”

With several caveats in mind, here are seven promising uses for physical AI systems.

Manufacturing robots

When thinking about physical AI, many people may envision robots manufacturing cars or other products. That’s certainly happening, with several vendors offering builder robots for sale, and with the industrial robotics market valued at $54.3 billion in 2026, growing to $94.4 billion by 2031, according to Mordor Intelligence.

One example of robots building products comes from car maker BMW, which has used a humanoid robot to weld parts together at a plant in the US.

Quality inspection and predictive maintenance

Physical AI deployed inside manufacturing environments isn’t just being used to assemble products. The technology is also being used for material handling and automated quality inspection and defect checking, with labor shortages and constrained environments driving use, notes Venkat.

Predictive maintenance is also a sweet spot for physical AI in manufacturing. AI can be used to check that the software powering equipment is working correctly, says UST’s Masood.

“Agentic pipelines now read hardware schematics and chip pinouts natively, generate the regression suites engineers once scripted by hand, and compare live equipment telemetry against digital twins to catch firmware regressions and signal-integrity faults before a production run,” he says.

Boston Dynamics’ four-legged Spot is an example of a marriage between robotics and AI, with the company saying thousands of robots have been deployed across 40 countries at companies such as Intel, Chevron, Michelin, and Cargill. Spot is used to automate industrial inspections, conduct predictive maintenance, and go on security patrols.

Boston Dynamics also sells Stretch, which automates the unloading of trailers and containers, and Atlas, a humanoid robot that can lift, sort, and assemble products.

Physical AI embedded into cameras and sensors can provide quality control inspections on factory floors, notes Parm Sandhu, group vice president for enterprise AI, edge computing, and digital innovation at IT solutions provider NTT DATA.

“They want to make sure the products built right the first time,” he says. “We use a foundation model, set up with cameras and trained in self-learning, so it very can very quickly learn standard operating procedure for one factory station.”

Autonomous vehicles and drones

The promise of self-driving cars entered the public consciousness several years ago, and the market, separate from the physical AI market, was worth more than $200 billion in 2025, according to Global Market Insights.

Autonomous taxis are also gaining momentum, with Waymo and Tesla launching robotaxi experiments in limited areas in 2025. Uber also has huge plans for robotaxis.

But the autonomous vehicle market extends far beyond cars driving down the highway. Autonomous farm equipment, including tractors, harvesters, and drones, represent a growing market, with market size estimates varying wildly. Global Market Insights estimated the market to be worth $70.9 billion in 2025, with projections for it to reach $144.7 billion by 2035.

Drones can also be operated by an AI, leading to all kinds of applications, including military uses and food and package delivery services. Amazon and other companies have experimented with drone delivery services in recent years, and DoorDash announced in late July that it would jump into the market.

One use that staddles the autonomous vehicle and manufacturing use cases involves self-driving forklifts. NTT DATA has worked with forklift manufacturer Hyster-Yale to install self-driving capabilities into the vehicles, in part a response to labor shortages, Sandhu says.

“If you think about manufacturing, pretty much everything you touch in that world was lifted by a forklift somewhere or components were lifted by a forklift somewhere,” he says. “But people don’t want to drive forklifts, and that’s a huge problem.”

Fleet and warehouse coordination

Physical AI, built into trucks and smart shelves, can track and better coordinate the movement of materials and products, from the warehouse to the end customer. Physical AI, installed in robots, can pick, sort, and transport goods. AI can use fleet telemetry to optimize routes in the shipping fleet.

AI models can now orchestrate thousands of autonomous mobile robots across fulfillment networks, what UST’s Masood calls “air traffic control for robots.”

The AI intelligence sits in the coordination layer that routes, sequences, and removes conflicts in the fleet, he adds. “It scales in ways single-robot programming never could,” notes.

Physical AI has moved beyond pilots and is operating at enterprise scale in warehouses, according to Symbotic, a warehouse physical AI vendor.

The company’s fleet of 22,000 autonomous mobile robots that traveled more than 200 million miles in 2025, with one robot traveling more than 52,000 miles, or more than twice the distance around the Earth, the company says.

Surveillance and physical security

Physical AI’s application to physical security includes roving robots like Boston Dynamics’ Spot, but it also allows organizations to connect video cameras and other security tools to provide an ever-vigilant view of the secured environment.

Companies such as Artificial Intelligence Technologies Solutions and its subsidiary Robotic Assistance Devices are connecting several devices for a sort of security mesh across a campus or building. The companies’ Speaking Autonomous Responsive Agent (SARA) is an agentic AI platform designed to coordinate cameras, fixed security devices, autonomous patrol vehicles, lights, speakers, monitoring systems, and human security personnel.

SARA can evaluate events from physical security systems, verify security events, communicate directly with people at the site, and initiate approved responses, the companies say. The automated response can save valuable time compared to human intervention, they claim.

Another example of the use of physical AI for security involves smart metal detectors with AI embedded inside. Athena Security is one company that offers AI-powered body scanners that claim a high rate of detection for all kinds of weapons, including razor blades and small knives.

Smart buildings and infrastructure

Companies can use physical AI to monitor all kinds of metrics inside buildings and across utility grids and telecom networks, notes UST’s Masood. The AI can trigger alerts, safety interventions, or environmental controls. Hospitals are now using physical AI to coordinate care, and network operators are deploying AI-powered self-healing tools.

Physical AI will create intelligent concierges at hotels, airports, and hospitals that provide directions, verify identities, and coordinate services, Kneron’s Liu says.

Over the next decade, AI will be embedded in nearly all physical spaces, including drive-thru lanes, restaurants, factories, and offices, he predicts. “People will expect a security camera that understands intent instead of simply detecting motion, a hospital room that recognizes subtle changes in a patient’s condition before an alarm sounds, a retail shelf that manages inventory autonomously, or a building that continuously optimizes energy, security, and occupancy,” he adds.

Smart cities

Outside of traditional enterprise environments, cities are now embedding AI into traffic devices to monitor vehicle flow and into cameras to monitor community service needs.

The AI-powered systems can improve traffic flow, monitor intersections, and make roadways safer without relying only on human observation. Lidar maker Ouster worked with the New Jersey Department of Transportation to install sensors at 42 intersections ahead of the World Cup tournament to assist with road and pedestrian traffic congestion, the company says.

NTT DATA is working with Brownville, Texas, to set up a citywide alert system to send workers for incidents such as when a park’s garbage containers are full and to assist police officers in filling out reports, notes Sandhu.

Gorodenkoff – shutterstock.com

Unternehmen verlagern einen zunehmend größeren Anteil ihrer IT-Workloads auf Colocation– und Cloud-Anbieter, firmeneigene Rechenzentren haben das Nachsehen. Wie dem aktuellen „Global Data Center Survey“ des Uptime Institute (Download gegen Daten) zu entnehmen ist, machen externe Rechenzentrumseinrichtungen und -dienste in diesem Jahr 2026 einen Anteil von 46 Prozent an den IT-Workloads aus. Damit liegen sie – Dienste von Colocation-, Cloud-, Hosting- und SaaS-Anbietern eingeschlossen – knapp über den 44 Prozent, die auf unternehmenseigene Rechenzentren entfallen.

Wie die Experten betonen, ist dies das erste Mal seit Erhebung der Studie, dass externe Rechenzentrumsstandorte einen größeren Anteil an den IT-Workloads von Unternehmen halten als unternehmenseigene Einrichtungen. Im Rahmen der Studie befragte Uptime zwischen April und Mai 2026 mehr als 1.600 Teilnehmern weltweit, darunter über 800 Rechenzentrumsbetreiber und -eigentümer.

On-Premises verliert an Boden, aber nicht an Relevanz

Die Experten gehen davon aus, dass sich der Abstand bis 2028 auf 48 Prozent vergrößern wird. Dennoch werden unternehmenseigene Rechenzentren laut Uptime weiterhin ein wesentlicher Bestandteil hybrider IT-Strategien bleiben, da viele Workloads wegen Sicherheits- und regulatorischen Anforderungen weiterhin On-Premises betrieben werden müssen.

„All diese Faktoren hängen miteinander zusammen – und zwar durch KI“, erklärt Andy Lawrence, Executive Director of Research bei Uptime. „Der Druck auf die gesamte Lieferkette, die Kostenstruktur, die Stromverfügbarkeit – all das wird durch den massiven Nachfrageschub nach KI-Infrastruktur vorangetrieben.“

Kostendruck bleibt hoch – auch wegen KI

Zugleich steht das Thema Kosten für die Betreiber auch das vierte Jahr in Folge an erster Stelle: 79 Prozent der Befragten gaben an, dass ihr Management zumindest in gewissem Maße besorgt über die Kostenentwicklung sei. Der Anteil derjenigen mit einer „sehr besorgten“ Führungsebene sank jedoch von 44 Prozent im Vorjahr auf 38 Prozent.

Die Ergebnisse deuten darauf hin, dass die Betreiber Kosten inzwischen gegen ein breiteres Spektrum an KI-bezogenen Herausforderungen abwägen müssen. 76 Prozent sorgen sich hinsichtlich der Prognose künftiger Kapazitätsanforderungen. Außerdem zählten Unterbrechungen in der Lieferkette, Personalmangel, die Verfügbarkeit von Strom sowie die Bereitstellung geeigneter KI-Infrastruktur zu den Hauptsorgen des Managements.

„Die Bedenken hinsichtlich der Kosten sind zwar etwas zurückgegangen, doch das bedeutet nicht, dass diese keine Rolle mehr spielen“, erläutert Lawrence. „Es heißt lediglich, dass andere Aspekte an Bedeutung gewonnen haben.“

RZ-Betreiber werden vorsichtiger im Umgang mit KI

Während die Anwenderunternehmen mittlerweile nicht mehr nur mit generativer KI experimentieren, scheinen die Verantwortlichen in den Rechenzentren nun sorgfältiger abzuwägen, wo die Technologie sicher eingesetzt werden kann. So sank der Anteil der Befragten, die der Ansicht sind, dass KI die Effizienz von Anlagen steigert, von 58 Prozent im Vorjahr auf 52 Prozent.

Das Vertrauen darin, dass KI menschliche Fehler reduziert, sank sogar um elf Prozentpunkte auf 40 Prozent. Am größten ist das Vertrauen nach wie vor bei Anwendungen mit geringerem Risiko, etwa bei der Betriebsanalyse und der vorausschauenden Wartung (Predictive Maintenance). Nur 31 Prozent trauen dagegen der KI die Steuerung von Geräteeinstellungen zu, lediglich 16 Prozent automatisierte Konfigurationsänderungen.

„Dieser Vertrauensrückgang deutet darauf hin, dass viele Betreiber noch weit davon entfernt sind, umfangreiche Steuerungsaufgaben der KI zu überlassen“, erläutert Jacquie Davis, Research Analyst bei Uptime. „Dies unterstreicht, wie wichtig es ist, den Menschen in den Prozess einzubeziehen.“

KI treibt Rack-Dichte in die Höhe

Der Einsatz von KI beginnt zudem, das Design der Infrastruktur zu verändern: Die durchschnittliche modale Rack-Dichte überschritt erstmals die Marke von elf Kilowatt (kW), ein Meilenstein wie die Experten bemerken. Ausschlaggebend dafür sei maßgeblich die wachsende Zahl von KI-Rechenzentren mit typischen Rack-Dichten von über 30 kW, hieß es.

Ohne diese Installationen fällt der Anstieg der Rack-Dichte mit einem Zuwachs von 7,5 kW auf 7,8 kW moderater aus. Mittlerweile gibt fast ein Viertel der Betreiber an, über mindestens einige Racks mit einer Leistung von mehr als 30 kW zu verfügen. Vor einem Jahr lag dieser Anteil noch bei 19 Prozent. Daniel Bizo, Forschungsleiter bei Uptime, bezeichnet das Überschreiten der 11-kW-Marke als „psychologischen Meilenstein“ für die Branche. Der Modalwert spiegele nun den wachsenden Einfluss von KI-orientierten Implementierungen wider.

Chris Brown, Chief Technology Officer (CTO) von Uptime, erklärt, dass vor allem KI-Trainingslasten die höchsten Rack-Dichten verursachten. Bei Rechenzentren unterhalb der 30-kW-Grenze stellen weiterhin eher traditionelle Unternehmensanwendungen den Haupttreiber dar. „KI treibt die Rack-Dichte schneller in die Höhe als jeder andere Faktor“, so Brown. Gleichzeitig seien klassische Enterprise-Anwendungen weiterhin der dominierende Faktor für die meisten Rechenzentren unterhalb der extrem hohen KI-Leistungsdichten.

Ausfälle werden seltener, aber kostspieliger

Zum sechsten Mal in Folge ging die Anzahl der Betreiber zurück, die in den vergangenen drei Jahren einen folgenschweren Ausfall erlebt haben: 47 Prozent berichteten von so einem Vorfall, verglichen mit 50 Prozent im Vorjahr. Die finanziellen Folgen nehmen jedoch zu: 71 Prozent der Betreiber, die einen Ausfall erlebten, gaben an, dass ihr schwerwiegendster Vorfall Kosten von mindestens 100.000 Dollar verursachte. Im Vorjahr waren es noch 57 Prozent.

Mit 56 Prozent bleiben Stromausfälle dabei weiterhin die Hauptursache für folgenschwere Störungen Brown erwartet jedoch, dass Ausfälle im Zusammenhang mit der Kühlung an Bedeutung gewinnen, da die Leistungsdichte in den Racks KI bedingt weiter zunimmt.

Nachhaltigkeit gewinnt wieder an Dynamik

Nachdem die Entwicklung im vergangenen Jahr stagnierte, verzeichnet das Nachhaltigkeits-Reporting nun wieder einen leichten Aufwärtstrend. So erfasst inzwischen mehr als die Hälfte der Betreiber den Wasserverbrauch – ein Anstieg gegenüber 47 Prozent im Vorjahr. Laut Lawrence war die vorübergehende Verlangsamung vor allem auf den starken Fokus der Branche auf den Ausbau von KI-Kapazitäten zurückzuführen – und nicht etwa eine Abkehr von Nachhaltigkeitszielen.

Personalmangel verschärft sich

Der Fachkräftemangel in der Branche hat sich dagegen nach einigen Jahren relativer Stabilität verschärft: Mehr als die Hälfte der Befragten gab an, Schwierigkeiten bei der Suche nach qualifizierten Fachkräften zu haben – ein Anstieg gegenüber 46 Prozent im Vorjahr. Zudem berichteten 28 Prozent, Mitarbeitende an konkurrierende Rechenzentrumsbetreiber verloren zu haben.

Am schwierigsten zu besetzen sind den Befragten zufolge Stellen für Elektrofachkräfte und sowie Mitarbeitende für den operativen Betrieb auf Einstiegsniveau. Dies spiegle den wachsenden Bedarf an Fachwissen im Bereich KI-bezogener Infrastruktur wider, so die Experten. (tf)

Dieser Artikel ist im Original bei unserer Schwesterpublikation Networkworld.com erschienen.

Corporate data centers continue to lose workload share to colocation and cloud providers. Meanwhile, data center operators face mounting challenges tied to AI adoption, including increasingly difficult capacity planning, persistent staffing shortages, and more expensive outages.

Third-party data center facilities and services—including colocation, cloud, hosting, and SaaS providers—account for 46% of IT workloads in 2026, edging past the 44% in enterprise-owned corporate data centers, according to Uptime Institute’s 16th Annual Global Data Center Survey. This is the first time in Uptime’s annual survey history that third-party data center venues have held the larger share of enterprise IT workloads than corporate-owned facilities. Uptime expects the gap to widen to 48% versus 42% by 2028, although it says enterprise facilities will remain a key part of hybrid IT strategies, with security and regulatory requirements continuing to keep many workloads on premises.

Uptime’s survey, conducted between April and May, collected responses from more than 1,600 participants worldwide, including more than 800 data center owners and operators.

“All of these are linked, and they’re all linked through AI,” Andy Lawrence, Uptime’s executive director of research, said during a webinar discussing the findings. “The pressures it’s putting on the entire supply chain, the cost base, the availability of power—all of these are being driven by this massive surge in demand for AI infrastructure.”

Cost pressures ease, AI creates new challenges

Cost remains management’s top concern for the fourth straight year, with 79% of respondents saying executives are at least somewhat concerned. But the share describing management as “very concerned” fell to 38%, from 44% last year.

The results suggest operators are balancing cost concerns against a broader set of AI-related challenges. Concern about forecasting future capacity requirements climbed to 76%, while supply-chain disruptions, staffing shortages, power availability, and meeting AI infrastructure needs all ranked among management’s leading worries.

“Cost issues kind of fell slightly, but that doesn’t mean that people are no longer concerned,” Lawrence said. “It just means the other issues got serious.”

Operators grow more cautious about AI

As organizations move beyond experimentation with generative AI, operators appear to be taking a more thoughtful view of where the technology can be used safely.

The share of respondents who believe AI improves facility efficiency slipped to 52% from 58% last year, while confidence that AI reduces human error fell 11 percentage points to 40%. Trust remains strongest for lower-risk applications such as operational analytics and predictive maintenance but drops sharply for autonomous operational tasks. Just 31% trust AI to control equipment settings, and only 16% are comfortable with automated configuration changes.

“This drop in trust … suggests that a lot of operators are nowhere near the point of handing over a lot of control tasks to AI,” Jacquie Davis, Uptime research analyst, said. “It speaks to the importance of human in the loop.”

AI continues pushing rack densities higher

AI deployments are also beginning to reshape infrastructure design.

Average modal rack density surpassed 11 kW for the first time, a milestone driven largely by a growing number of AI-focused facilities with typical rack densities above 30 kW. Excluding those deployments, average rack density increased more modestly, from 7.5 kW to 7.8 kW. Nearly one-quarter of operators now report at least some racks exceeding 30 kW, up from 19% a year ago.

Daniel Bizo, Uptime’s research director, called surpassing 11 kW a “psychological milestone” for the industry because the average now reflects the growing influence of AI-oriented deployments. Chris Brown, Uptime’s chief technical officer, said AI training workloads are driving the highest rack densities, while more traditional enterprise applications remain the primary driver for facilities below the 30 kW range.

“AI is driving rack densities up faster than anywhere else,” Brown said, adding that enterprise applications remain the dominant driver for most facilities operating below the highest-density AI deployments.

Outages become less frequent but more expensive

For the sixth consecutive year, fewer operators reported experiencing an impactful outage, with 47% saying they had one during the previous three years, down from 50% in 2025.

But outages continue carrying a higher financial toll. Seventy-one percent of operators that experienced an outage said their most serious incident cost at least $100,000, up from 57% last year.

Power failures remained the leading cause of impactful outages, accounting for 56% of incidents. Brown said cooling-related failures are likely to become more significant as AI-driven rack densities continue increasing.

Sustainability efforts regain momentum

After stalling in last year’s survey, environmental reporting resumed its gradual upward trend. More than half of operators now track water consumption, while Lawrence said the temporary slowdown reflected the industry’s intense focus on expanding AI capacity rather than any retreat from sustainability goals.

More than half of operators now track water consumption, up from 47% a year ago, suggesting that even as AI infrastructure dominates investment decisions, environmental reporting remains a growing priority across the industry.

Staffing shortages worsen

The industry’s talent shortage also intensified after several years of relative stability.

More than half of respondents reported difficulty finding qualified candidates, up from 46% last year, while 28% said they had lost employees to competing data center operators. Electrical specialists and junior operations staff were the most difficult positions to fill, reflecting growing demand for AI-related infrastructure expertise.