Onofrio Pirrotta is a senior vice president and managing partner at Kyndryl, where he leads the technology company’s U.S. manufacturing and energy market. Opinions are the author’s own.

Artificial intelligence is no longer a futuristic concept for manufacturers; it is embedded in operations, from predictive maintenance to intelligent automation. 

According to Kyndryl’s People Readiness Report, 95% of manufacturing organizations are already using AI across various areas of their business. Yet, despite this widespread adoption, a critical gap remains: 71% of manufacturing leaders said their workforce is not ready to leverage AI effectively.

This disconnect between technological investment and workforce readiness is more than a growing pain — it’s a strategic risk. If left unaddressed, it could stall innovation, limit return on investment and widen the competitive gap between AI pacesetters and those still struggling to align people with progress. 

The readiness paradox

The manufacturing sector is undergoing a profound transformation. AI, edge computing and digital twins are reshaping the factory floor, enabling real-time decision making and operational agility.

So why are only 14% of manufacturing organizations we surveyed incorporating AI into customer-facing products or services?

The answer lies in the “readiness paradox.” Manufacturers are investing in AI tools and platforms, but not in the people who use them. As a result, employees are wary of AI’s impact on their roles and many leaders are unsure how to guide their teams through the transition. Over half of manufacturing leaders cited a lack of skilled talent to manage AI and fear of job displacement is affecting employee engagement. The result is a workforce that is technologically surrounded but practically unprepared.

What AI pacesetters are doing differently

Pacesetting companies — representing just 14% of the total business and technology leaders in eight markets surveyed — have aligned their workforce, technology and growth strategies. They are seeing measurable benefits in productivity, innovation and employee engagement by using AI with the following approaches: 

1. Strategic change management: Just over 60% are more likely to have implemented an overall AI adoption strategy and have a change management plan in place. They’re treating AI as a major, well-supported transformation rather than a quick fix.
2. Trust-building measures: Employees are more likely to embrace AI if they are involved in its implementation and the creation of ethical guidelines. It’s also important to maintain transparency around AI goals.
3. Proactive skills development: Pacesetters are investing in upskilling, mentorship and external certifications and are more likely to have tools in place to inventory current skills and identify gaps. This gives them a clearer roadmap for workforce development as well as a head start on future readiness.

Best practices

So how can manufacturers bridge the AI skills gap and join the ranks of Pacesetters to align innovation with workforce development?

Make workforce readiness a boardroom priority

AI strategy should not live solely in the IT department. It must be a cross-functional initiative that includes HR, operations and the C-suite.

Yet research shows a disconnect. CEOs are 28% more likely than chief technology officers to say their organizations are in the early stages of AI implementation and they are more likely to favor hiring external talent over upskilling current employees. This misalignment slows progress.

Manufacturers need unified leadership around a shared vision for AI and workforce transformation.

Establishing a cross-functional AI steering committee that includes frontline supervisors also ensures alignment between technology and talent strategies. Tying AI readiness to business KPIs such as productivity, quality and innovation metrics — as well as conducting regular workforce capability audits — will further elevate its importance in strategic planning and forecast future needs based on AI roadmaps.

Build a culture of trust and transparency

Fear is a powerful inhibitor. When employees worry that AI will replace them, they are less likely to engage with it. Leaders must address these concerns directly. That means communicating openly about how AI will be used, involving employees in pilot programs and demonstrating how AI can augment, not replace, their roles.

Implementing a tiered AI education program, launching employee enablement campaigns and providing access to AI-powered tools can help bring a manufacturer’s workforce along the AI journey. Hosting AI town halls where employees from supervisory roles, as well as the frontline, can ask questions or share concerns is another way to build engagement. Worker trust can also be reinforced through the development of an internal AI ethics policy and governance board. 

Evidence of uneven AI adoption in the private sector grows by the day, with executives worried about falling behind more tech-savvy competitors. But the stakes are different, and considerably higher, in government. For local leaders, AI isn’t about winning a race. It’s about unlocking new problem-solving capacity to deliver better services and meet pressing resident needs.

Even so, city governments face real barriers to further adoption, including persistent concerns about accuracy and privacy, procurement hurdles, and too little space for civil-servant experimentation. The good news? Innovative leaders are already showing how to overcome these obstacles. And, in doing so, they’re reaping insights of use to others aiming to do the same.

Designing AI tools tailored to employees’ needs.

Boston Chief Innovation Officer Santiago Garces has no doubt that his city-hall colleagues want to push their efforts forward with AI, and he’s got the data to prove it.  His team recently conducted a survey of 600 Boston city employees and found that 78 percent of them want to further integrate the technology into their work. When asked what’s holding them back, security, accuracy, and intellectual property are among civil servants’ top concerns. 

Boston’s solution: Developing AI tools with more specific use cases, such as speeding up the procurement process, and employee concerns in mind from the start. 

Following through on a project they began last year, Garces and his team recently deployed a tool called Bitbot that can answer employees’ questions about procurement. Because it was trained on dozens of procurement documents, as well as state law, local ordinances, and city best practices, Garces argues the tool is best described not as a chatbot (though it resembles one) and more like the AI version of a handbook people know they can trust. And while the city’s randomized controlled trial of the tool’s impact is still wrapping up, Garces says the city has generally seen faster task completion and higher levels of accuracy from employees using it. At the same time, the tool is set up not to send information back to the major tech companies the way most public-facing AI tools do, which helps address employee concerns around privacy and security. 

While not every city has the resources to develop products like this on its own, Garces notes that working with university partners (he works closely with Northeastern University) can be very affordable. And this sort of approach could help civil servants everywhere be more comfortable in pushing AI use forward.

“They want the city-provided tool that they know that they can trust,” Garces explains.

Rapidly prototyping to de-risk big purchases.

When not developing bespoke AI solutions, cities turn to outside vendors. And they’re increasingly doing so with great success and impact, according to Mitchell Weiss, a Harvard Business School professor and senior advisor to the Bloomberg Harvard City Leadership Initiative. Still, adoption is uneven. “Some local leaders are wary [of making a sizeable], given broader concerns in the private sector and worries about the return on investment, ” he adds. Tight city budgets make the stakes of a misstep especially, and private-sector caution only reinforces city leaders’ hesitation.  

That’s why some cities are shaking up how they buy AI tools, both to speed that procurement process up and make sure that they stay laser-focused on boosting efficiency and effectiveness, rather than pursuing new tech for its own sake. Call it “try before you buy” for cities and AI.

Take San Antonio. Emily Royall, who until this past month worked as a senior manager for the emerging technology division in the city, helped run a rapid prototyping initiative that ensures potential AI contracts address tangible, department-level needs. The city spends up to $25,000 on three-to-six-month pilots before committing to longer-term vendor deals. The goal is to gauge impact and kick the tires first. 

Longer term, Royal and her new colleagues at the Procurement Excellence Network (she joined the team in September) believe one way cities will take their AI games to the next level is by banding together and conducting joint solicitations. And unlike traditional approaches to cooperative purchasing, cities are now determined to take a more muscular role in deciding for themselves what the most valuable AI use cases look like, and then calling on industry to develop the products that bring them to life while still meeting cities’ privacy concerns.

“This is about pooling purchasing power to deliver the outcomes that governments actually want to see from their implementation of the technology,” she says.

Leading teams toward bolder experimentation.

One of the cities leading that charge when it comes to local governments shaping the AI market is San Jose, Calif., which on Wednesday announced the first winners of its AI Incentive Program, offering grants to AI startups taking on everything from food waste to maternal health. But that’s not the only way the city is standing out. San Jose is also a model when it comes to creating a workplace where employees trust that leaders will have their backs as they constantly experiment in new ways with the technology.  

“Integrating AI into city hall isn’t just a question of expense,” explains Mai-Ling Garcia, digital practice director at the Bloomberg Center for Public Innovation at Johns Hopkins University. “It also requires that you have the political capital to spend to take risks.” 

And San Jose Mayor Matt Mahan is spending that political capital to great effect.

“He tells us it’s OK if you try something and it doesn’t work—you will not be penalized so long as there’s sufficient due diligence,” explains Stephen Caines, the city’s chief innovation officer.

But it’s not just what the mayor tells civil servants. And it’s not just the training San Jose provides through its data and AI upskilling programs, which are delivered in partnership with San Jose State University and which the mayor wants to train 1,000 more civil servants next year. It’s the larger political climate he’s cultivated to encourage AI experimentation. 

For example, the mayor presented a memo to the city council two years ago calling for the city to seize the moment and help shape (and stimulate) the emerging industry, and to integrate it across city operations. When local lawmakers voted for it, it helped clarify for everyone in city hall that pushing public-sector AI use forward wasn’t just allowed, but a key part of their job.

“I am often reminding policymakers and my colleagues that we spend probably a disproportionate amount of time focused on the technology itself or the latest hot startup versus what moves the needle the most, which is the people who will use these tools,” Mayor Mahan tells Bloomberg Cities. He adds that it isn’t just him, but city leaders across the organization who encourage experimentation with the technology. 

“How you choose to react to failure matters a tremendous amount for building culture,” Mayor Mahan, who is participating in the Bloomberg Harvard City Leadership Initiative, explains.

Among San Jose’s most concrete AI successes so far is a traffic-signal initiative that has already shown the potential to reduce resident commute times by 20 percent. And if the mayor and his team have anything to say about it, that’s just the start of not just pushing AI use forward in their city but encouraging other cities to experiment, too.

“The outdated vision of government is that we are merely consumers of technology,” Caines, the local innovation officer, explains. “The thesis that we’re putting forward is that government can be not only a lab where technology can be deployed, it can also be a valuable partner in co-creation, and we can actually serve as a market indicator by highlighting use cases that make a difference for residents.”

Austin, Sept. 04, 2025 (GLOBE NEWSWIRE) — The AI in K-12 Education Market size was valued at USD 0.37 billion in 2024 and is projected to reach USD 5.24 billion by 2032, expanding at a CAGR of 39.29% over 2025-2032.

Growth is driven by the increasing adoption of AI-powered learning tools, personalized education platforms, and intelligent tutoring systems that enhance student engagement and learning outcomes. Schools are leveraging AI for real-time performance tracking, adaptive assessments, and administrative automation. Additionally, rising digital literacy, government initiatives promoting EdTech, and the integration of AI in online and hybrid learning environments are accelerating market expansion across the K-12 education segment globally.

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In the U.S., the AI in K-12 Education Market was valued at USD 0.11 billion in 2024 and is projected to reach USD 1.51 billion by 2032, growing at a CAGR of 39.05%. Growth is driven by rising EdTech investments, adoption of AI tutors, personalized learning solutions, and classroom analytics that enhance student engagement, learning outcomes, and operational efficiency in schools.

Key Players:

• IBM
• Google (Google for Education)
• Microsoft (Microsoft Education, Azure AI)
• Amazon Web Services (AWS Education AI)
• Carnegie Learning
• Pearson
• DreamBox Learning
• Knewton
• Century Tech
• Nuance Communications
• Blackboard
• Knewton Alta (by Wiley)
• Cognii
• Querium
• Squirrel AI
• Jenzabar
• Duolingo
• Khan Academy
• Altitude Learning
• Osmo (by BYJU’S)

AI in K-12 Education Market Report Scope:

If You Need Any Customization on AI in K-12 Education Market Report, Inquire Now @ https://www.snsinsider.com/enquiry/8071 

By Component, Solution Segment Leads AI in K–12 Education Market in 2024 with 71% Revenue Share

In 2024, the solution segment dominated the AI in K–12 Education Market, capturing 71% of revenue. Its leadership is fueled by extensive deployment of AI-enabled platforms, including intelligent tutoring systems, adaptive learning tools, and learning management systems (LMS). Continuous investment in digital curriculum and AI-based learning platforms ensures the solution segment remains the primary driver of market growth, supporting enhanced student engagement and personalized learning experiences.

By Deployment, Cloud Segment Leads AI in K–12 Education Market in 2024 with 69% Share

In 2024, the cloud segment dominated the AI in K–12 Education Market, capturing 69% of the share. Its leadership is driven by scalability, cost efficiency, and easy access across devices. The growing demand for centralized AI-integrated learning platforms and cloud-based learning management systems (LMS) further strengthens the cloud segment’s role in transforming digital education and enhancing personalized learning experiences.

By Technology, Machine Learning Segment Leads AI in K–12 Education Market in 2024

In 2024, the machine learning segment dominated the AI in K–12 Education Market with a significant revenue share. Its leadership is driven by extensive applications in adaptive learning, predictive analytics, and student performance tracking. The growing personalization of algorithms and automation of student progress monitoring reinforce machine learning’s central role in enhancing learning outcomes and educational efficiency.

By Application, Learning Platform & Virtual Facilitators Segment Leads AI in K–12 Education Market in 2024

In 2024, the learning platform & virtual facilitators segment dominated the AI in K–12 Education Market, capturing a major revenue share. Its growth is driven by widespread adoption of virtual assistants, learning management systems (LMS), and remote teaching platforms. The increasing use of hybrid learning environments and virtual classroom support tools further reinforces this segment’s position as the primary contributor to market expansion.

North America Leads AI in K–12 Education Market in 2024, Asia Pacific is Projected to Record the Fastest CAGR

In 2024, North America dominated the AI in K–12 Education Market, holding the highest revenue share. The region’s leadership is fueled by early adoption of EdTech solutions, strong funding support, high digital literacy, and widespread integration of AI-driven curricula across schools, establishing it as a benchmark in educational technology deployment.

The Asia Pacific region is expected to achieve the fastest growth in the AI in K–12 Education Market. Expansion is driven by government-led digital education programs, rising private sector investments, and increasing student populations. Countries including India, China, and Indonesia are adopting AI-powered, inclusive, and scalable education models, fueling rapid market adoption and digital transformation across the region.

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Exclusive Sections of the Report (The USPs) – Check Section 5

• USP 1 – AI Adoption & Readiness by Region and School Type

Helps clients identify regions and school segments with the highest potential for AI-based learning tools deployment.

• USP 2 – Curriculum Enhancement & Personalized Learning Insights

Provides guidance on how AI can tailor learning paths, adaptive assessments, and student engagement strategies.

• USP 3 – Teacher Productivity & Classroom Automation Analysis

Shows clients how AI solutions can automate grading, administrative tasks, and performance tracking to free teacher bandwidth.

• USP 4 – Student Performance & Learning Outcome Analytics

Offers insights into improving student outcomes through predictive analytics, skill-gap detection, and learning trend analysis.

• USP 5 – EdTech Integration & Platform Compatibility Assessment

Helps clients integrate AI tools with existing LMS, e-learning platforms, and digital classrooms for seamless adoption.

• USP 6 – Regulatory & Data Privacy Compliance (FERPA, GDPR, COPPA)

Ensures AI tools meet data privacy and safety regulations for children, minimizing legal and reputational risks.

• USP 7 – Future Trends & Innovation Roadmap

Prepares clients for emerging applications such as AI tutors, gamified learning, AR/VR classrooms, and real-time feedback systems.

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