by Gary Mintchell | Nov 3, 2025 | Commentary, Generative AI, News
The IEEE released “The Impact of Technology in 2026 and Beyond: an IEEE Global Study” surveyed 400 CIOs, CTOs, IT directors, and other technology leaders in Brazil, China, Japan, India, the U.K. and U.S. at organizations with more than 1,000 employees across multiple industry sectors including banking and financial services, consumer goods, education, electronics, engineering, energy, government, healthcare, insurance, retail, and telecommunications. The survey was conducted September 11-17, 2025.
I find surveys interesting for an understanding of current sentiment among people who may be involved in the area but seldom have time to think through the questions. As a former product development professional, I’d never use these for developing new products. You need to be a little ahead of this curve.
Still, consider these as opinions coming from a background of much media hype about AI and Agents.
In general, the survey found these opinions:
- Widespread Use of Agentic AI as a ‘Smart Assistant’ for Everyday Tasks Such as Personal Shopper, Scheduler, Data Privacy Manager and Health Monitor Expected; Agentic AI Growth Will Also Spur a Data Analyst Hiring Boom
- Annual trends study forecasts robotics, extended reality, autonomous vehicles, quantum computing and renewable energy as technology areas AI will influence the most in 2026
Be wary of adjectives and adverbs injected into survey releases. Phrases such as “lightning speed” should be sent through the BS filter of your mind. Developments have seemed fast over the past few years. They also seem to have stalled.
Agentic AI is like a smart assistant that, when given a task, can work independently, but still needs its work double-checked. Its adoption is on the rise, and a strong majority of technologists globally (96%) agree that agentic AI innovation, exploration and adoption will continue at lightning speed in 2026, as both established enterprises and start-ups deepen investments and commitments to the technology.
Following are a lot of percentages. I’d advise skimming rather than getting lost in the minutiae.
The rise of agentic AI won’t be confined to business. Survey respondents see it reaching mass or near-mass adoption by consumers in 2026 for the following uses:
- (52%) Personal assistant | scheduler | family calendar manager
- (45%) Data privacy manager
- (41%) Health monitor
- (41%) Errand and chore automator (e.g. grocery orders)
- (36%) News and information curator
In addition, 91% agree the use of agentic AI to analyze greater amounts of data will grow in 2026, spurring a data analyst hiring boom to analyze the accuracy of results, transparency and vulnerabilities.
An interesting list of anticipated top skills for employees follows. Looks as if they have little to do specifically with AI (save one).
According to the survey, the top skills technologists will seek in candidates they plan to hire for AI-related roles in 2026 are:
- (44%) AI ethical practices skills (+9% from prior year)
- (38%) Data analysis skills (+4% from prior year)
- (34%) Machine learning skills (+6% from prior year)
- (32%) Data modeling skills, including processing (no change from prior year)
- (32%) Software development skills (-8% from prior year)
An interesting list of applications. Why was extended reality cited? That seems a long way off, if ever happening. Autonomous vehicles do keep improving.
A majority (77%) of technologists agree the novelty of humanoid robots can inject fun into the workplace but over time will become like commonplace co-workers with circuits. Robotics is also a top area of technology over half (52%) of technologists think will be influenced by AI in 2026. Other areas influenced by AI in 2026 will include extended reality (XR), including augmented, virtual and mixed reality (36%); and autonomous vehicles (35%).
I don’t see manufacturing/industrial applications hitting the top list.
Meanwhile, the top industries expected to experience the greatest transformation from AI next year will be software (52%); banking and financial services (42%); healthcare (37%) and automotive and transportation (32%).
Will they use it?
- (39%) Using Regularly, But Selectively: Generative AI will continue to be a regular part of our work in selective areas, and adds value. (+20% from prior year)
- (35%) Rapidly Integrating, Expecting Bottom Line Results: AI will continue to be integrated throughout all our operations. We’ve already seen measurable bottom line results and expect these to grow.
The top uses for AI applications technology leaders expect in 2026 includes:
- (47%) Real-time cybersecurity vulnerability identification and attack prevention (-1% from prior year)
- (39%) Aiding and/or accelerating software development (+4% from prior year)
- (35%) Increasing supply chain and warehouse automation efficiencies (+2% from prior year)
- (32%) Automating customer service (+4% from prior year)
- (29%) Powering educational activities such as customizing learning, intelligent tutoring systems, university chatbots (-10% from prior year)
- (23%) Accelerating disease mapping and drug discovery (-3% from prior year)
- (22%) Automating and/or stabilizing utility power sources (-3% from prior year)
Will you be using AI—really?
More than half of those surveyed (51%) cited 26-50% of jobs across the global economy will be augmented by AI software in 2026, while less than one-third (30%) cited 51-75% of jobs, (16%) cited 1-25% of jobs, and only (4%) cited 76-100% of jobs.
I’m thinking we may be reaching peak capital investment—mostly for economic reason, not technical. But many people ride the wave.
Close to half of technologists (49%) think it will take 5-7 years to build out the global data center infrastructure required to meet growing AI development and demand. One-third think it will happen sooner, in 3-4 years, while 10% think it will not happen for 8-10 years or more.
by Gary Mintchell | Oct 22, 2025 | Generative AI
Only a month ago I wrote about how every news release included “AI” no matter how mundane or useful it was. Suddenly the new phrase is “Agentic AI”. Agents are pieces of code that build on data generated by Large Language Models (LLMs) which are the current iteration of AI. Agents consider the data and context in order to make decisions.
These can be useful. However, we do know that the data generated from LLMs are not always useful or accurate or repeatable. The technology is good, but it also needs much growth to become a reliable tool in the worker’s kit.
This news comes from the ServiceMax division of PTC. Looks like the first usable implementations will help service workers. Since I’m writing this in the customer area of the car dealer’s service center, this is top of mind today.
In brief:
- Agentic AI advancements in ServiceMax AI accelerate work order execution and enable improvements in first-time fix rates
- Agentic AI advancements in Servigistics expand AI-driven intelligence for service parts planning
From the news:
PTC announced the availability of new service lifecycle management (SLM) AI offerings in its ServiceMax field service management solution and Servigistics service supply chain optimization solution. Agentic AI advancements in ServiceMax AI strengthen multi-agent action to support field service management outcomes, including faster work order execution and smarter parts queries. Servigistics AI advancements deliver additional agentic intelligence to the service supply chain, enabling autonomous orchestration of [service] planning and execution.
The latest ServiceMax AI enhancements build on the unique ability to take advantage of AI directly from the current processes already managed by ServiceMax. This latest release enhances AI Actions with orchestrated multi-agent execution, AI-driven process automation through Service Flow Manager, and a new Knowledge API that connects to documents across enterprise systems.
Servigistics is also introducing a new AI Assistant, which supports planners by improving forecast accuracy and accelerating planning cycles, and will be generally available in October 2025.
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by Gary Mintchell | Oct 17, 2025 | Generative AI, News
Praveen Rao, Head of Manufacturing for Google Cloud, spoke at this week’s Connected Worker Conference about a survey the company conducted by National Research Corp. of manufacturing executives regarding their investments in AI. Agents were of specific interest.
We’ll skip the suspense. 78% of the executives surveyed believe they are seeing a return on their investment.
I asked Rao of agents were real or still in the vaporware phase. He assured me that there are many agentic AI use cases. A refresher—agents work with LLMs to “see, hear, think—they can design and simulate.”
There are supply chain use cases, but Rao assured me there are many other use cases. He pointed to engineering and product design workflow improvements when he pointed to 75% of surveyed executives see agents used to improve productivity. Other key areas of interest include improving customer experience (support, etc. 64%), business growth (60%), marketing (creating brochures and the like, 58%), and security (53%).
I’m interested in how these technologies can work with unstructured data. Eventually that will be a massive win.
Back to the survey from the press release:
According to the survey of more than 500 manufacturing execs, 56% reported their organizations are actively using AI agents, with 37% reporting they have launched ten or more. These agents range from gen AI-powered chatbots and single-task agents for specific functions like scheduling production jobs — all the way to sophisticated, multi-agent systems that can take actions on behalf of users, under their supervision.
Additional findings include:
AI agents are being used for core business processes such including:
54% use them for quality control
48% use them for production planning
47% use them for supply chain and logistics
Over half of manufacturing executives (55%) stated their organizations plan to allocate 50% or more of their future AI budget to AI agents,
The primary consideration for executives when evaluating LLM providers is data privacy and security (37%), followed by system integration and scalability and performance.
Google Cloud marketing includes this interesting caveat: In other words, AI success hinges on deep cross-functional collaboration, but it’s top-level support that will truly drive results, aligning AI adoption with business goals and guiding crucial decisions about its fundamental use within organizations.
Yes, technology enables and provides tools, but in the end it’s people and systems and organization that creates a win.
by Gary Mintchell | Oct 15, 2025 | Commentary, Generative AI, Podcast
I’ve published a podcast both on my podcast app (available in Apple, Overcast, or wherever you download them) and on YouTube. You can subscribe on any. Or click the links for podcast or YouTube on the right sidebar.
Why pursue AI? As a tool to help entrepreneurs add value to their companies. The appropriate roll out entails organizing small “pirate ships” empowered to experiment and implement with a budget and air cover. Many concerns about AI’s impact on employment and organization are over blown. History shows that new technology winds up creating more jobs than it destroys. This podcast is sponsored by Inductive Automation.
by Gary Mintchell | Oct 14, 2025 | Generative AI, Manufacturing IT, Operations Management, Software
I am passing this on from the Peter Diamandis newsletter. I don’t think I can link, but click the link on his name to go to his website and sign up. Diamandis sometimes climbs over-the-top optimistic. But that’s a great counter to the usual cynicism and negativity and dysfunctional thinking prevalent in today’s society.
Understanding artificial intelligence (called by Om Malik “augmented intelligence” and by others as neither artificial or intelligent) today requires a healthy dose of realistic thinking and perspective. I offer these thoughts as a counter to your usual AI hype.
Traditional companies are failing to implement AI effectively. Here are five principles to make the technology actually work for you…
1/ AI problems are rarely AI problems – they’re strategy problems disguised as technology problems. Most organizations fail at AI implementation not because they chose the wrong models or hired the wrong engineers, but because they never clearly defined what business problem they’re solving. They see competitors “using AI” and panic-buy solutions for problems they can’t articulate.
2/ Budget size is inversely correlated with AI success. The companies throwing millions at AI initiatives are systematically outperformed by teams running on shoestring budgets with clear mandates.
3/ The 10x rule is the only rule that matters for AI adoption. Anything less than a 10x improvement in speed, cost, or quality is organizational noise. Most AI projects deliver 20-30% improvements that get lost in measurement error and change management overhead.
4/ Competitive intelligence is your fastest path to AI advantage. While you’re debating whether to build or buy, your smartest competitors are already shipping AI-powered solutions.
5/ Pirates beat committees every time. The worst way to implement AI is through enterprise-wide initiatives with steering committees and governance frameworks. Instead, empower your teams from the ground up. Recent studies indicate some alarming news:
- 42% of executives say the process of adopting generative AI is tearing their company apart
- 41% of Millennial and Gen Z employees admit they’re sabotaging their company’s AI strategy
- What’s needed is to enable small teams, “pirate ships,” to move at startup speed (within enterprise contexts). Small teams are optimized to experiment and learn rather than aim for consensus. Give them a problem, a budget, and air cover, then get out of their way.
Here’s the key implementation insight: AI amplifies existing organizational capabilities (and dysfunctions).
by Gary Mintchell | Oct 10, 2025 | Generative AI, Robots, Technology
I’ve been trying to guide AI discussion toward useful applications rather than overly hyped general visions. Let the people dealing in billions of dollars promote themselves. For those who have real work to do, look into the details of AI news to discern real benefits. Perhaps this news from ABB fits that model. This news also follows the trend of larger companies investing in specialist companies in order to drive additional benefits for their products and solutions.
In this case, ABB (robotics) has invested in LandingAI in order to improve the company’s robotic applications for customers. Oh, and we get a new TLA (three-letter acronym). Note the specific examples.
In brief:
- Strategic investment secures ABB’s use of LandingAI’s vision AI capabilities, such as LandingLens, for robot AI vision applications
- Pre-trained models, smart data workflows and no-code tools reduce training time by 80% and accelerate deployment in fast-moving sectors including logistics, healthcare, and food & beverage
- First of its kind collaboration marks a major step towards ABB Robotics’ vision for Autonomous Versatile Robotics – AVR
This first of its kind collaboration will integrate LandingAI’s vision AI capabilities, like LandingLens, into ABB Robotics’ own software suite, marking another milestone in ABB’s journey towards truly autonomous and versatile robots.
“This announcement is the latest in our decade-long journey to innovate and commercialize AI, benefitting our customers by enhancing robot versatility and autonomy to expand the use of robots beyond traditional manufacturing,” said Sami Atiya, President of ABB Robotics & Discrete Automation. “The demand for AI in robotics is driven by the need for greater flexibility, faster commissioning cycles and a shortage of the specialist skills needed to program and operate robots. Our collaboration with LandingAI will mean installation and deployment time is done in hours instead of weeks, allowing more businesses to automate smarter, faster and more efficiently.”
As part of the collaboration ABB has made a venture capital investment through ABB Robotics Ventures, the strategic venture capital unit of ABB Robotics, driving collaboration and investment in innovative early-stage companies that are shaping the future of robotics and automation. Financial details of the investment were not disclosed.
LandingAI’s LandingLens is a vision AI platform that enables the rapid training of vision AI systems to recognize and respond to objects, patterns or defects with no complex programming or AI expertise required.
Through this collaboration, ABB Robotics will reduce robot vision AI training & deployment time by up to 80 percent. Once deployed, system integrators and end users can retrain the AI for new scenarios on their own, unlocking a new level of versatility. This is a critical step in scaling robot adoption in dynamic environments, beyond traditional manufacturing, especially in fast-moving sectors such as logistics, healthcare and food and beverage. ABB is already piloting LandingAI’s technology and actively working to integrate it into existing vision AI applications, including item-picking, sorting, depalletizing and quality inspection.
More information about RobotStudio with generative AI assistant:
- RobotStudio Al Assistant provides real-time, step-by-step guidance for robot programming
- More intelligent and easy-to-use generative Al interface creates faster, easier commissioning and boosts productivity
- Another step in enhancing robot accessibility and versatility beyond traditional manufacturing
Powered by a Large Language Model (LLM) that understands and interprets human language, RobotStudio AI Assistant draws from ABB’s comprehensive library of manuals and documentation to deliver high-quality, context-rich responses to questions, enabling users to set up faster and find rapid answers to questions and technical challenges.
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