Low Code/No Code

Large enterprises are scaling agentic AI faster than smaller companies, and their use is growing quickly, according to McKinsey data. The management consulting firm surveyed more than 1,700 employees across industries and company sizes for the report. 

Some enterprises that are scaling agentic AI are using coding tools to replace some of their software purchases, the report found. Nearly one-third of respondents said they decided against buying at least one software feature because they can now build it in-house. 

Even though agentic tools use more compute than previous tools, and are therefore more costly, tech leaders are finding value from these use cases, Michael Chui, co-author of the report and senior fellow at QuantumBlack, AI by McKinsey, said in the report. “Organizations are planning to invest more, even as they develop new disciplines for optimizing ROI from their AI expenditures,” Chui said.

Although some organizations deploy AI to save time or money, most are struggling to define and realize ROI. Despite a lack of clarity, AI investments continue to rise. 

Four in five respondents in the McKinsey report said AI has improved their productivity, but enterprise-level financial impact hasn’t yet followed — the proportion of employees reporting cost savings driven by AI remains unchanged from last year, according to the report. 

As organizations face changing AI operating costs, even those that tout the technology’s upside have had to become budget-conscious. 

One in five respondents said their organization limits a range of AI use because of its operating costs. Still, 28% of respondents said their organization is spending more than 10% of their total enterprisewide budget for information and communication technologies on AI, and they expect that number to grow next year. 

Although the tech industry has seen job cuts this year, the overall number of roles in the industry remains steady, with the labor market favoring AI-savvy workers. The McKinsey report found two-thirds of companies have had little or no AI-related change.

To manage IT spending optimally, businesses should be deliberate about when to buy something instead of building it, and take greater ownership over their technology and change agendas, Lieven Van der Veken, senior partner at McKinsey, said in the report. 

“They are also treating operating costs as a design constraint, not an afterthought, and making the deeper changes in workflows required for value realization,” he said. 

The rise of in-house development and the evolving role of developers to manage AI outputs instead of writing code is part of this shift, Chui said. 

“Spend on horizontal AI-enabled tools, such as chatbots, is increasingly managed as a necessary cost of doing business, similar to office productivity and communications tools,” Chui said. “AI influences other parts of the IT budget, too: Agentic coding tools are creating a more capable option for moving some software development in-house.”

The rise in price of tokens will lead AI coding to become more expensive than an average developer’s salary by 2028, according to a Gartner report. The shift will happen as consumption-based pricing overtakes subscription models among key providers.

Changing pricing models is also making AI costs a highly variable figure, keeping enterprise tech leaders from accurately forecasting and controlling spending, the report said. Vendors frequently lack transparency into how token consumption is calculated and billed. 

The cost structure is changing as enterprises still struggle to find maturity in AI projects and measure their business impact, said Nitish Tyagi, senior principal analyst at Gartner, in a statement. “Software engineering leaders are increasingly concerned as token-driven AI spend becomes harder to justify, with budgets often being depleted earlier than expected.”

Adopting AI into software workflows has become the default, leading employees to spend less time writing code and more time managing AI outputs. 

As AI use proliferates the enterprise, the cost is adding up, especially in engineering departments, Gartner found. Token overspending was linked to how software engineering leaders govern usage, with many using ungoverned, autonomous agents in their workflows. 

“AI coding costs will continue to rise as infrastructure investment and profitability challenges push model pricing higher,” Tyagi said. “At the same time, as more developers adopt AI tools, light users are expected to rapidly become mainstream users as familiarity and reliance increase, driving further growth in token consumption and overall spend.” 

Few enterprises have a clear strategy with defined goals and outcomes for their AI projects, according to Altimetrik data published in April, but they choose to forge ahead rather than miss out on the AI moment. Just over one-quarter of enterprise C-suite leaders reported having complete, real-time visibility into what their AI systems cost to operate, a KPMG report found. 

It’s likely many agents are working on tasks in the background for days, without leaders’ knowledge or audits, Rahsaan Shears, AI enterprise transformation leader at KPMG LLP, told CIO Dive in an email. 

“The CFO does not see it. The CIO may not either,” Shears said. “That is enterprise AI economics right now: costs compounding inside workflows no one has fully instrumented.”

To keep AI usage and costs from outrunning budgets, CIOs must consolidate and closely track usage, keeping watch over consumption across cloud platforms, copilots, agent frameworks, coding tools, business workflows and team-level experiments, Shears said.

Organizations should pursue a tokenomics semantic model, which connects usage to cost and cost to ownership. This approach will also map ownership of AI to business value, workload, behavior patterns and risk. 

While overspending is a costly mistake for enterprises, lack of visibility into what an organization’s AI systems operate is a bigger risk, Shears said. If a provider degrades a capability, or cost spikes, tech leaders will be in the dark. 

Shears said CIOs should investigate which agents to throttle, which harnesses are running discretionary loops on premium models and which workflows are mission-critical.

“Without that visibility, organizations are governing an agent fleet with no instrumentation and no triage plan,” Shears said.

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By Salesforce

Enterprise IT departments are facing a problem that didn’t exist a few years ago. Too many people want to build things, and not enough of them can do it safely.

Generative AI has made it possible for nearly anyone to spin up an app or an agent from a plain-language prompt. Demand has followed. According to Salesforce, 88% of IT leaders say they cannot safely meet the current surge in AI-driven development requests. Traditional build cycles, already strained by limited engineering headcount, weren’t designed for this volume.

The result is a familiar pattern with a new twist. When central IT can’t move fast enough, business units find their own tools. A decade ago that meant unsanctioned SaaS subscriptions. Now it means employees wiring together generic AI builders and third-party coding assistants outside any governance structure. Shadow IT is on the rise again, but this time with direct access to production data and business logic.

The instinct is to respond by making development faster, but speed isn't the issue anymore. The real risk is access. When business users can generate functional apps from a prompt, the boundary between exploration and production becomes dangerously blurred.

Expanding who can build without losing control

Rather than introducing more point solutions, forward-thinking organizations are narrowing their toolkit. This means bringing AI-assisted development directly into first-party platforms that already sit inside a company's security and data perimeter. Organizations don’t need a separate tool to get there.

The case for first-party building holds up across four areas.

1. Secure data stays put

Third-party tools often need a working copy of the underlying customer schema just to generate anything useful. This results in data moving somewhere the security team never approved and never audited. In a first-party environment, the schema doesn't move. And the authentication and authorization rules to govern the data are already in place.

2. No rebuilding required

Builders using third-party tools frequently end up recreating data models, integrations and business logic that already exist natively on their core platform. That’s a duplicated engineering effort with no strategic payoff. A first-party approach uses what’s already built which saves time.

3. Built-in business context

Prompting from outside your core system produces generic apps that fail in real operations. Native AI inherits existing metadata, workflows and relational data automatically, delivering tailored applications that work right out of the box.

4. Less technical debt

IT teams are already stretched thin trying to secure and account for what they've got. Every disconnected tool a department adopts adds one more thing to that list. The fix isn't another platform to manage. It's fewer platforms to begin with. Consolidating around first-party tools keeps sprawl from growing.

Building inside the enterprise boundary

Addressing this shift requires thinking beyond individual toolsets to re-evaluate underlying security architecture. Rather than relying on disparate point solutions, forward-thinking organizations are embedding AI-driven development directly within established enterprise platforms. Native tooling, like Builder Central, operationalizes this strategy by combining setup, configuration and app creation into a single governed environment, ensuring full visibility over data movement and compliance without hindering speed.

Ultimately, the goal for IT leaders is transitioning from reactive gatekeeping to enabling secure, self-service innovation. As generative AI continues to expand who can build, the, competitive advantage belongs to organizations that establish clear operational guardrails—ensuring innovation never compromises trust.

Want to balance speed and control in your own environment? Check out the on-demand webinar, Master No-Code Building and Setup on Salesforce.

Long seen as a laggard in digital adoption, the heavily regulated financial services sector has proven that an established cloud strategy can propel a company’s AI initiatives forward. 

The effort is fueled by the desire to reap the promised advantages of AI tools, including agents, throughout operations. More than half of banking IT executives expect AI agents to be fully embedded in risk, compliance and audit functions, as well as fraud detection and transaction monitoring, according to Accenture data. A separate McKinsey report expects companies to trim their costs by up to 20% as they deploy the technology.

The successful rollout of AI agents in the context of financial services requires collaboration with business clients, according to Ken Tingle, first VP and business intelligence manager at Cape & Coast Bank.

“There's a strategic component, a collaboration component,” said Tingle, speaking during the Creatio No-Code Days Florida conference. “You have to bring in not only technology but your sales leaders in order to deploy a solution that's going to benefit the organization.”

Collaboration is particularly important for the driving force behind any AI initiative: data. Leaders should enlist the entire organization in order to support governance and data quality initiatives, Tingle said, as agents can't carry out successful actions without the right data.

AI use cases in the financial services sector are focused closely on driving growth. The bank turned to Creatio's platform to deploy a referral agent, tracking the performance of AI-recommended leads alongside employee-driven referrals. 

“You want to start small and focused,” Tingle said. “Start targeted with a very small audience, and then deploy slowly to the rest of your organization.”

Adoption and data in focus

Along with audience, one tech executive recommended starting with a targeted, straightforward use case to encourage AI adoption.

“Ground zero is: have people use AI assistant-type use cases,” said Drew McMonigle, CTO at Lake City Bank, speaking during the conference. “You cannot fully automate something until you get people using the assisting capability.”

Once adoption begins to gain traction, communicate those successful, approved use cases throughout the organization to create groundswell organic adoption while supporting AI governance. "It also makes for very organic use case generation," McMonigle said.

The financial services industry shares a common challenge other industries are facing with AI: how critical data is to successful deployments. 

"If we don't have clean data, any integration you make from any other system is not going to be satisfactory," said Meeta Autrey, VP and IT manager at Mission Valley Bank. 

Cape & Coast Bank introduced an incentives program to reward employees for flagging and remediating errors in customer data. "It's a big process," Tingle said. "You have to reward them for it."

Modernization projects are a CIO headache. Costly, cumbersome, brimming with expectation and full of pitfalls, updating a central technology such as a mainframe that functions as the central nervous system of a business is an arduous undertaking. Vendors are promising AI can change that. 

They’ve rushed to deploy, for example, a menu of products that promises to update applications based on common business-oriented language. COBOL remains a widely-used but aging programming language that works across mainframe computers and legacy operating systems, supporting more than 40% of online banking systems, 80% of in-person credit card transactions and 95% of ATM transactions, according to mainframe provider IBM. 

While firmly rooted in enterprises, companies have turned to more modern programming languages such as Python and JavaScript, and the often challenging task of updating COBOL code can create bottlenecks for businesses.

In February, large language model provider Anthropic sent markets into a frenzy when it said Claude Code — its AI-enabled coding assistant — could be used for COBOL modernization. The fallout sent mainframe market leader IBM's stock into a nosedive and elicited a slew of opinions highlighting the challenges of implementing updates tied to the mainframe. But it also prompted a different kind of discussion: how AI can add to the legacy technology rather than force CIOs to leave it behind. 

Anthropic’s lofty pitch for approaching COBOL modernization with Claude Code, that it can be used for “systems of any size,” might be true, but as one of the first AI frontier model competitors in the market with such a tool, the company has a long road ahead in growing enterprise trust, said Mitch Ashley, VP and practice lead of software lifecycle engineering at The Futurum Group. 

“That’s why, when Anthropic says, ‘We will do all the modernization for COBOL, you don’t need to worry about it,’ it is, to me, a laughable statement,” Ashley said. 

The LLM provider also faces competition from established vendors such as IBM and AWS, which operate their own AI modernization tools as well as from firms developing platforms in-house for COBOL modernization, such as Morgan Stanley. 

As AI eases modernization efforts, CIOs still have to think through where applications, processes and workflows should live and whether it’s worth maintaining legacy systems. For mainframes, leaving the technology behind entirely is still often a difficult and illogical choice for CIOs, Steven Dickens, CEO of HyperFRAME Research, said in a research note. The evolution of AI tools means they might not have to decide. The technology is driving IT leaders away from rip-and-replace strategies, as they instead work to bring AI directly to mainframe data, he said. 

“What I am seeing now is a shift where the platform is not just surviving but is being architected to lead in the era of AI," Dickens wrote. "I see AI as a tailwind for the mainframe, not the opposite.”

AI is just another tool

Vendors offering AI tools for mainframe modernization often claim that AI can ease transformation as COBOL engineers retire and take their platform know-how with them. 

But the reality for CIOs is that legacy system modernization remains an unsolved business problem rather than an unsolved technical problem, said Gartner VP Analyst Matt Brasier. 

“How do we justify the money that it will cost? Is there value in going through a bunch of business change to do this and retrain people? Is it going to be worth it?” Brasier said, describing a few of the questions CIOs face when considering a large modernization project. 

Even after working through modernization strategies and roadmaps, success is anything but guaranteed. In June, Gartner issued a warning to enterprises that more than 70% of mainframe migrations started this year will fail due to tech leaders’ overestimation of generative AI’s ability to help with transforming legacy implementations. Given the high failure rate, organizations must balance mainframe exit strategies that optimize investments, limiting full platform exits to select cases, according to Gartner. 

Beyond generative AI, CIOs have been turning to tools such as machine learning as a way to help modernize legacy systems for years, Brasier said. In fact, many organizations are already using the technology to help modernize COBOL applications, finding value in the technology’s ability to document and describe what an application does and building test cases.  

“It’s just another automation tool at the end of the day,” Brasier said. 

Leading mainframe vendors, including IBM, have added AI offerings to accelerate modernization for customers.

IBM Bob, an AI coding assistant that helps engineers modernize code, became generally available in April. The company is also heavily investing in expanding AI functionality within the mainframe, making it easier for enterprises to use the technology without having to migrate to another platform for advanced capabilities. As part of that effort, IBM released a private preview of IBM Bob Premium Package for Z for enterprise mainframe applications in April. 

Previously, IBM rolled out the Spyre Accelerator chip to IBM Z mainframes in 2025 to support AI inference. The company also launched a partnership with Arm in April to build dual-architecture hardware for IBM Z mainframes, allowing enterprises to run AI and data-intensive workloads without having to rewrite code.  

Unisys, another mainframe vendor, partners with AWS to offer enterprise customers AI-augmented application modernization services. 

When relying on vendors to modernize applications, processes and workflows, enterprises need to be aware that vendor tools are usually built to help migrate companies to their tech stacks, Brasier said. 

“IBM’s tools are going to move you onto IBM’s newer Java-based stack. AWS tools are going to move you onto AWS’ cloud-native, serverless function stack,” he said. “The vendors provide the tools with an idea of, ‘This is the stack you’re going to be moving to.’ For many organizations, they have a destination in mind, and it’s probably not one of those vendor stacks.” 

Overall, AI developments don’t make it easier for CIOs to leave the mainframe, Brian Klingbeil, EVP and chief strategy officer at IT service management company Ensono, said in a blog post. 

“They actually make it easier to stay,” he said. 

Homegrown tech and the hyperscaler effect

Investment banking company Morgan Stanley is taking another route to modernizing its mainframe processes. The financial services firm has modernized more than 17 million lines of COBOL, Software AG’s Natural and PERL code into modern languages, including Java and Python, with its in-house platform DevGen.AI. 

The generative AI-powered modernization platform has saved developers in excess of 1 million hours of manual coding, Trevor Brosnan, global head of technology strategy, architecture and modernization at Morgan Stanley, told CIO Dive in an email. 

“That means more projects getting greenlit, more productive and efficient engineers and the ability to leverage emerging technologies — and deliver more to our business partners,” Brosnan said. 

While modernizing legacy COBOL is inherently complex, DevGen.AI enables developers to reverse engineer old code and translate it into a clear design so that they can migrate it into updated architectures, Brosnan said. The platform reduces coding tasks that previously required a week to half a day. 

“This capability improves the entire software development lifecycle — from analysis to testing to deployment — allowing our teams to deliver change faster while maintaining full human oversight and robust guardrails,” Brosnan said. 

The banking firm is also using coding agents to help plan and execute development workflows beyond simple code generation, Brosnan said. 

While firms like Morgan Stanley have developed platforms in-house for modernization projects, vendors are filling the gap and providing modernization tools for companies. 

The North Carolina Division of Motor Vehicles selected IT infrastructure services provider Kyndryl earlier this year to replace five legacy COBOL-based systems and implement a unified, cloud-native platform hosted on Microsoft Azure. The modernization contract, which includes platform implementation, training and data migration, totals $84.8 million. 

Meanwhile, Microsoft bolstered its Azure Migrate services with agentic AI last fall, a broad migration tool aimed not just at mainframes but legacy technology in general.

AWS Transform, which was also launched last year, uses agents for multiple points of the mainframe migration process, including the initial discovery process followed by migration waves. In one notable example, Toyota Motor North America used AWS Transform to modernize more than 40 million lines of COBOL to Java.

Going forward, successful mainframe modernization using AI hinges on both reverse engineering applications and forward engineering, according to Asa Kalavade, VP of AWS Transform. 

“It’s going to be a journey,” Kalavade told CIO Dive. “In the next little while, we see it’s going to be a combination where some workloads may remain on prem, on the mainframe, some will move to the cloud.”

Agent-led software development is fundamentally changing engineering, but some tech teams are missing out on most of the productivity AI can provide, a Bain & Company report found. The report was based on surveys of global executives and market research conducted in 2025 and 2026 by the management consulting firm.

By the fall, respondents said they expect more than half of their engineering efforts to be agent-assisted, and by spring 2027, that number rose to about 90%. While respondents reported AI is producing 63% higher outputs per engineer, most companies said they’re not yet seeing large enough increases in their overall efficiency as they figure out new roles in managing AI output.

For value to be derived, tech teams need to make adjustments to all levels of their engineering operations when introducing AI agents, not by plugging in agents into singular activities, such as code generation or testing, said Purna Doddapaneni, a partner at Bain & Company and coauthor of the report. “In an agentic world, agents are doing the work,” he said. “And as humans or organizations, we are responsible for the decisions that the agents are making.”

Agents are no longer just making developers incrementally faster, they’re rebuilding the entire delivery system around engineering, Doddapaneni said. 

Adopting AI into engineering workflows has become the default, The State of Engineering Excellence 2026 report, published by software platform Harness, found last month. The technology has changed the way engineering teams complete work and measure productivity, with more time spent on reviewing code, fixing bugs and context switching between tools. 

But teams that fail to adjust their operations around advancing tools won’t gain the benefits of them, Doddapaneni said. The report recommended humans should act as strategic advisers and incident responders through the AI-assisted workflows. Product and engineering teams will likely adopt hybrid agentic models, where developers can move from being code writers to agent orchestrators. 

More powerful AI models don't derive value without the processes put in place to direct them toward a company’s goals and objectives, Doddapaneni said.

“A model is like an engine, just having a beefier passenger engine doesn't really help me drive the car — I need to think about the steering wheel, the brakes and everything around it,” he said. “It’s like I'm building a harness around the engine, or the model, giving it context to get me from one place to the other.” 

Most engineering processes include both a product development life cycle — which defines what to build based on customer needs, market opportunity and strategic roadmap — and a software development life cycle, which is the process of building the product through coding, testing and deploying.

The sequential nature of development is disappearing in the AI era, Doddapaneni said, as humans spend less time developing products and writing code, and instead spend their time managing AI outputs. He called it “technical scaffolding,” or putting the guardrails and guidelines in place for AI to create the desired outcomes. 

“CIOs who are still running pilots without that system redesign are going to hit the ceiling,” he said. 

The size and structure of software engineering teams will likely change in the next three years, according to research from Gartner. By 2029, 60% of organizations said they will adopt smaller software engineering teams, compared with 15% this year. 

AI adoption has been changing the role and responsibilities of engineers, and as agents handle more routine technical tasks, engineers have more time to focus on problem-solving, Gartner found. This change will lead organizations to reorganize engineering departments into smaller teams, often comprised of four or five members. 

Three-quarters of software engineering leaders expect their headcounts to remain the same or grow, according to the analyst firm. The new model isn’t a cost-saving tactic, Aliyah Camacho, principal analyst at Gartner, said in an email to CIO Dive. “It’s a restructuring of teams to best take advantage of AI and human expertise,” she said. 

The software engineering profession is experiencing an overhaul as more teams adopt AI agents, responsibilities shift toward managing AI outcomes and the price of AI coding rivals the cost of human workers. 

Adopting AI into engineering workflows has become the default. Under the new operating model, engineering responsibilities have expanded to include scrutinizing code quality and security, taking accountability for downstream outcomes, and making judgment calls about when to trust Al and when to override it, a May report conducted by software platform Harness found. 

As the traditional responsibilities of software engineering roles fluctuate, organizations might find more success by breaking employees into smaller groups, Gartner’s report said. These smaller groups likely include skilled engineers such as a product manager, a user experience or agent experience designer, and at least one AI-native software engineer. 

In this structure, each team member manages a variety of responsibilities including understanding business goals, managing AI agents and product design. The size of a successful team will likely change based on the project or goals they’re working on, but it’s important they stay nimble, while being big enough to promote a diversity of ideas, Camacho said. 

“Due to AI-driven compression of roles and competencies, software engineers are becoming ‘product engineers,’ freeing product managers to focus on the product vision and feature roadmap of the future rather than feature implementation details,” Camacho said. 

She emphasized that Gartner is not predicting the restructuring should cause job losses within organizations. In fact, slowing junior-level hiring could lead to talent pitfalls for organizations over time, hindering knowledge transfer, restructuring internal talent pipelines and limiting recruitment to more expensive and competitive senior roles, Camacho said.  

“AI is reshaping software engineering — not by replacing developers, but by creating a surge in demand for intelligent applications and new engineering roles,” Camacho said. 

Roles for developers with AI skills have skyrocketed in the last five years, far outpacing demand for non-AI roles and underscoring the scale of enterprise adoption, a Randstad Digital report found. 

Based on an analysis of 35 million global job postings since 2021, AI-augmented roles for software development jumped nearly 600%. By comparison, traditional developer roles increased just 28% in the same period, the talent services firm found. 

In shaping their talent strategies, businesses are increasingly putting the spotlight on trust. Randstad Digital found AI trainers, who work to guarantee model reliability and trustworthy outputs, was the fastest-growing position globally, increasing nearly 300% in five years.

While AI-attributed layoffs pervaded headlines in recent weeks, AI adoption is leading to increased hiring in specific categories.  

Nearly one-quarter of roles for software developers in the U.S. and the U.K. now require expertise in AI as organizations grapple with lengthy hiring cycles, Randstad said. More than a half million tech job postings are open in the U.S., according to CompTIA.

Leaders aiming to support widespread adoption in their organizations should look more closely at talent strategies, said Michael Morris, global head of platform and talent at Randstad Digital.

“The technology and the process are almost never our problem, we can get through those two,” Morris told CIO Dive in May. “People transformation is almost always the one that's lagging, the one that takes the most effort and the most time.”

Amid the retool in talent strategies, two concerns are top of mind for CIOs: the implications of agentic AI and the increase in cybersecurity risk.

Agentic AI represents a key investment priority this year for half of software engineering teams, according to SoftServe data. But despite efforts to widen agentic AI use for coding, businesses report struggling with the effects of integration and the cost of compute.

Security teams are also concerned about the potential for AI-written code vulnerabilities. ProjectDiscovery, a security provider, found nearly 60% of security practitioners struggle to keep up with the volume of AI-written code.

Companies using AI to write code are creating serious security risks that not all organizations feel prepared to handle, according to a report by security testing firm ProjectDiscovery. 

Security personnel want audit trails and access limitations before they integrate AI into their processes, ProjectDiscovery found. “They are not opposed to the technology, but they need it to earn its place.”

The report highlights one of the most fraught aspects of the AI revolution in the corporate world: the tension between AI-assisted coders and the people responsible for protecting their work.

“A deluge of AI-generated code is hitting security teams, and the wave is building faster than most organizations can absorb it,” ProjectDiscovery said in its report. “Engineering teams are shipping at an unprecedented speed, and security teams are standing in the path of that rising tide.”

Only 38% of cybersecurity practitioners said they are keeping up well with the increasing volume of code they have to review because of AI, and nearly 60% said the task is getting harder, the report found. Security personnel at mid-sized companies felt this pressure more than their counterparts at large firms, perhaps reflecting the amount of resources larger companies have to devote to the work.

The report is based on a survey of 200 cybersecurity professionals at mid-size to large enterprises in North America and Western Europe. Nearly half of respondents work in security architecture, ProjectDiscovery said, and more than half play a role in “selecting or approving security products.”

Defenders are concerned about several risks stemming from the use of AI to write code, including the exposure of corporate secrets (78% of respondents cited this as a top concern), supply-chain risks from unreliable dependencies (73%) and “business logic vulnerabilities,” application design flaws that could let a hacker abuse legitimate functions (72%).

In its discussion of secrets leakage, ProjectDiscovery cited a 2025 National Cybersecurity Alliance report that found that 43% of employees admitted to entering sensitive company data into AI tools.

European respondents were more likely than their American counterparts to cite secrets leakage as a major concern (87% versus 72%), perhaps reflecting the strict privacy requirements of the European Union’s General Data Protection Regulation (GDPR).