Generative AI

Almost eight in 10 executives believe 2030 will be the year AI starts significantly contributing to enterprise revenue, more than double the number of executives — 40% — who say AI is currently boosting revenues, according to a study published Friday by the IBM Institute for Business Value. 

AI investment as a percentage of revenue will more than double over the next four years, but 68% of executives fear that integration issues will cause AI efforts to fail, according to the global study of 2,000 C-suite executives. 

Still, AI is anticipated to drive enterprise growth through 2030, the study found. “AI won’t just support businesses, it will define them,” Mohamad Ali, SVP of IBM Consulting, said in a press release. 

AI’s return on investment remains a question mark for business executives who believe in the technology’s potential, yet struggle to predict when its full value will be realized. 

While 2030 appears to be a promising year for AI’s contribution to revenues, only 24% of executives can clearly identify where that revenue will derive from, according to IBM. 

Executive views on when new technology investments will pay off vary, with some anticipating only a 27% benefit realization in the next two years, according to a Rimini Street C-suite survey published last month. 

Spending on AI is not slowing, despite ROI concerns. In 2026, global AI spend is anticipated to increase 44% year over year to $2.52 trillion, according to a Gartner report. In the enterprise, IBM found that 47% of executives’ AI spend is currently focused on driving productivity and efficiency gains, while 62% believe their AI spend will be dedicated to product and service innovation between 2026 and 2030. 

The financial services industry has been heavily investing in AI, with big banks including Bank of America, Morgan Stanley and Goldman Sachs betting on future efficiency gains. 

“There’s going to be teething pain on this stuff,” Morgan Stanley CEO Ted Pick said in the company’s Q4 2025 earnings call. However, the “technological advancement is real,” he added. 

Indeed, executives are sold on the value of AI when it comes to bolstering productivity, according to IBM. Surveyed executives expect AI will boost productivity by 42% in the next four years. Two-thirds of leaders expect to realize most AI-driven productivity gains by 2030. 

The technology will also redefine leadership roles within the enterprise by 2030, according to 74% of executives surveyed by IBM. CIOs in particular will face mounting pressure to produce ROI. 

“We’ll need more problem solvers who understand both the business and the models — people who can marry technical capability with business insight,” Umang Dharmik, SVP and head of IT at Mercedes-Benz Research Development India, said in the IBM press release. “That’s the future of every company, including ours.” 

American Express is deploying AI across the company in a variety of areas, including in sales, engineering and customer service.

The bank and card network company has “mobilized” around AI opportunities, with Amex exploring “hundreds of AI use cases” in the past few years, Chairman and CEO Steve Squeri wrote last week in the company’s annual letter to shareholders.

“Advancements in AI are creating a structural shift in the way colleagues work and how businesses operate, compete, and create value – and we are embracing it,” Squeri wrote.

Amex travel advisors in 19 countries use AI tools for “faster, high-quality travel recommendations and insights,” Squeri said in the Wednesday missive. 

In technology, about 11,000 engineers use AI tools and have trimmed coding times by more than 30%, he said. 

In sales, generative AI can “trigger real-time prospect leads, conduct pre-call research, dynamically reprioritize prospect lists based on call analysis, and automate post-call follow-ups,” Squeri said. Sales teams are also moving to a new AI-powered platform that “converts interactions and performance data into actionable insights,” he added.

Amex’s work to integrate AI tools is “a deliberate redesign of how we operate,” the CEO wrote.

Amex is poised to leverage AI gains because it has “rich data at the customer level,” Truist Securities analyst Brian Foran said Thursday in an email.

And as a card issuer and network, Amex stands to benefit from AI agents driving commerce, Foran said. “That has the potential to be even bigger” than internal AI efficiencies, he wrote.

In deploying AI tools for Amex’s customer service representatives, the company adopted a “learn as we go” approach with training and employee feedback, Amex’s head of global support, enablement & control, Anthony Devane, said in a September interview.

“There’s an element of fear factor on AI,” Devane said, adding that Amex’s adoption of AI tools isn’t aimed at “significantly reducing headcount.”

“We do see it as significantly giving us more tools to develop, and deeper relationships with our customers,” he said.

AI-fueled efficiency gains have led to widespread fears of job losses across the economy.

Last month, Block said it would cut about 4,000 employees, roughly 40% of its workforce, in an aggressive bet that AI will be able to replace many of those roles. This week, Meta Platforms notified several hundred employees their jobs would be cut as the company invests heavily to construct its AI capabilities.

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By Graphwise

AI’s lightning-fast evolution, accelerated by Large Language Models (LLMs), is promising to shake up business, knowledge and data management. To get the inside scoop, Unisphere Research surveyed 382 executives, diving into LLM and RAG adoption, applications and hurdles. Their study, “State of Play on LLM and RAG: Preparing Your Knowledge Organization for Generative AI,” reveals how companies are using these tools to boost knowledge strategies and tackle LLM limitations.

AI Everywhere: 85% of Execs Report LLM Initiatives

According to the survey, generative AI (GenAI) and LLMs have quickly become an integral part of most organizations. The majority of executives (85%) reported that their organizations are either actively testing or partially deploying LLMs.

These efforts are particularly focused on areas such as content creation and customization, customer self-service, knowledge discovery and intelligent search - areas where Graphwise is already helping enterprises transform fragmented data into actionable intelligence.

The Trust Gap: Enthusiasm Meets Security Concerns

While enthusiasm for LLMs is high, the survey indicates that AI initiatives are still in the early stages of maturity, primarily within testing and development phases. Notably, a substantial majority of respondents, 71%, perceive the increasing use of GenAI as carrying security and quality risks. A significant consensus emerged regarding the indispensable role of human oversight in mitigating AI potential pitfalls.

Data quality stands out as the foremost concern (71% of respondents), followed closely by data security and privacy considerations. Even among organizations with LLMs already in production, an overwhelming 89% agree on the importance of human involvement to some degree.

This is where traditional AI hits its limit. Without a trusted semantic backbone, LLMs LLMs often struggle with context or "hallucinate" answers. Graphwise addresses this directly by grounding AI in verified facts, ensuring that your GenAI initiatives move from risky prototypes to production-ready assets.

GraphRAG is Key to Overcoming AI Obstacles

To address these concerns, nearly one-third of LLM users are turning to Retrieval-Augmented Generation (RAG) as a crucial link between proprietary and domain specific data within corporate databases and LLMs. More precisely, RAG is a technique that enhances the performance of AI models by connecting them to external knowledge sources thus improving the accuracy and relevance of responses from LLMs.

While mentioning the growing recognition of RAG as a key technology for grounding LLM outputs in reliable and contextually relevant data, the report highlights GraphRAG as a superior variant. By leveraging the power of knowledge graphs, GraphRAG leads to improved contextual results and more actionable data, moving beyond simple information retrieval and keyword searches to understanding the context and semantic relationships.

As a result, GraphRAG offers key benefits: improved contextual awareness, more accurate answers, reduced time to insights, enhanced user trust and the ability to perform multi-hop reasoning with increased transparency in the AI’s reasoning.

Building Reliable AI with Graphwise

The survey reveals that 59% of respondents with productive LLMs already use knowledge graphs. For organizations ready to lead, the Graphwise AI Platform provides the all-in-one suite needed to make GenAI reliable and scalable.

By integrating GraphDB (a high-performance semantic database) with a low-code GraphRAG workflow engine, Graphwise allows you to:

• Automate expertise by turning disconnected silos into a cohesive knowledge hub
• Eliminate hallucinations by grounding every response in your unique domain logic.
• Scale with confidence by enabling you to move from your first use case to a fully autonomous semantic layer in weeks, not years

As the industry shifts toward a data-centric approach, Graphwise provides the infrastructure to ensure your AI doesn't just talk, it knows.

The lifespan of IT skills is growing shorter as CIOs scramble to modernize toolsets in the midst of rapid AI deployment efforts, according to a report by tech talent firm Draup.

By 2027, more than 40% of IT skills will be rendered partially obsolete, according to the firm's report. The company attributes the shift to AI adoption within IT departments, as well as the increasing consolidation of roles and skill sets.

“We’re not seeing tech jobs disappear outright, but the way work gets done is certainly changing,” Vishnu Shankar, VP of data and platform at Draup, said in a press release accompanying the report. “AI is compressing skill cycles, reshaping roles and shifting where productivity comes from faster than most organizations expect.”

Tech executives spent much of last year fine-tuning their approach to talent attraction and development. Changes in job categories amid AI adoption gave urgency to this task as companies responded to change.

The job market, too, reflected this shuffle. Demand for broader categories of tech talent remained flat while postings for AI-specific roles skyrocketed. 

As companies infuse processes with newer tools — with software development serving as an early example — human-only tasks are shrinking, while AI-led and human–machine collaboration spreads across engineering, according to Draup.

The change is not expected to shrink headcount growth, the company expects. Instead, AI adoption will help drive a net expansion of 78 million jobs by the end of the decade. 

But AI will leave its mark. A recent Forrester analysis found that AI adoption will lead to some job loss in the U.S. — the analyst firm said AI will likely trim 6% of U.S. jobs by 2030. Generative and agentic AI will jointly contribute to the elimination of 10.4 million jobs by the end of the decade, according to Forrester.

"In the next five years, the future of work will remain largely human," J.P. Gownder, VP and principal analyst at Forrester, said in a Tuesday blog post highlighting the research. "AI will take over increasing numbers of workflows and tasks, but workflows and tasks aren’t jobs. Your strategy must invest in the people who use AI to improve their productivity and employee experience."

Global banking firm HSBC named generative AI a key technology investment area. The financial services firm is scaling AI adoption across employee assistance, end-to-end process reengineering and customer experience, executives said. 

“If you ask me, ‘Where is the biggest investment going into the new technology today,’ it is definitely going into generative AI,” HSBC CEO Georges Elhedery said during the call. 

The financial services firm said 85% of its employees have access to generative AI tools to become “future-ready,” Elhedery said. The company is also assessing how the technology can help redesign 50 processes, including fraud detection and credit applications, and has rolled out tools in areas like contact centers to help improve customer experience. 

Banks tapping into AI share a strong focus on redesigning processes, supporting employees and improving customer experiences. 

Banks are planning an average of $133 million in AI investments over the next 12 months, according to a KPMG AI Quarterly Pulse Survey. More than eight in 10 survey respondents said they expect to continue investments regardless of their ability to measure immediate returns. 

Swiss banking giant UBS said it plans to leverage the technology to redesign front and back office processes and improve services, UBS Group CEO Sergio Ermotti said during the company’s Q4 2025 earnings call earlier this month.  

“We are investing in a portfolio of large-scale transformational AI programs designed to increase our operational resilience, enhance the client experience and unlock higher levels of efficiency and effectiveness across the organization,” Ermotti said during the call. 

Meanwhile, TD Bank implemented roughly 75 AI use cases in 2025 targeting loan underwriting, creating intelligent leads and deepening customer relationships, according to Raymond Chun, group president and CEO of TD Bank Group. The financial services firm is prioritizing AI investments in customer acquisition and insights, as well as risk management, he added. 

This year, TD Bank plans to focus its AI use cases on reimagining “end-to-end processes,” Chun said. Agentic AI in particular can “truly transform operations,” Ted Paris, SVP, head of analytics, intelligence and AI at TD Bank U.S., previously told CIO Dive. 

HSBC is considering generative AI tools to help redesign banking operations and to improve data processing. 

“The result of which is a more productive bank, more efficient bank and a safe bank with stronger controls,” Elhedery said. 

HSBC is also already seeing productivity gains among employees due to generative use, Elhedery said. Coding assistance tools for engineers have resulted in “five times faster patching of code, patching of vulnerabilities,” he said. 

Elhedery also anticipates “customer experience will be materially enhanced” as they deploy generative AI tools to further personalize and tailor customers’ banking journeys.

Speed is a key factor in enterprise AI adoption efforts. Some businesses rushed into generative AI, only to find a plethora of implementation barriers. Others, like Mary Kay, paused to assess the situation. 

“When ChatGPT came out, that just changed the game overnight — it was the only thing that anyone wanted to talk about,” Mary Kay CIO James Whatley told CIO Dive. “We didn’t ignore it, but we didn’t jump all in.” 

The beauty company credits its slower start in generative AI with clearing the path for adopting AI agents down the road. 

“We knew once we had that foundation, that level of understanding, when the time came with the right tool and the right use case, we were going to be able to go faster,” Whatley said. “The worst thing you can do with AI is be naive about it.”

Mary Kay’s blended technology portfolio of generative and agentic AI is helping to build the digital toolset it wants to provide for customers, beauty consultants and staff, according to Whatley. 

AI agents are already live in one market with plans for more areas and use cases in the queue. The company, like others exploring agentic AI, is leaning on trusted partners to usher in the new capabilities. 

More than 2 in 5 companies were actively deploying AI agents in Q3, a sizable jump from the mere 11% of enterprises doing so in Q1, according to a KPMG report. Business leaders have high hopes for how the technology can boost operations, but caution is critical. The majority of leaders are trying to hedge risks by accessing agentic AI only through proven providers, keeping humans in the loop and limiting access to sensitive data. 

“We have these partnerships with key vendors… and I’m leaning on them and taking advantage of their AI investment to benefit my customers,” Whatley said, pointing to Salesforce’s Agentforce offerings and Microsoft’s Copilot. 

While off-the-shelf solutions give Mary Kay adoption speed, the company is also beginning to develop customized agents for its IT service desk and other areas. 

“The agentic AI world is real and there’s true ROI, so it is going to be a part of our future,” Whatley said. “The use cases will only grow.”

Laying the foundation

Mary Kay’s early success with AI agents was only possible because it worked out the kinks before adopting generative AI. 

“We’ve been focusing on transformation,” Whatley said. “We’ve been able to close down five regional data centers over the last couple of years, moved and modernized our tech stack from custom solutions into a modern platform and deployed that around the world.”

The custom code that the company wanted to retain was also migrated to different cloud providers, helping to clean up years of technical debt during a transition to a cloud-first model. 

The projects changed workflows significantly and required a degree of upskilling among IT pros to handle the modernized tech stack. The rest of the business had to become familiar with the tools as well. 

“There were many layers of change that we had to go through, and we had to do this at every single market as we deployed around the world,” said Whatley, who transitioned to the executive team as CIO in January 2023 after initially joining Mary Kay in 1998. 

When the generative AI conversations started, Mary Kay quickly created and communicated an AI policy to communicate the dangers, especially as it relates to sensitive data. Shortly after, an AI committee was created to oversee implementation, ensure regulatory compliance and manage risks. 

“From three years ago to today, there’s a ton of tools out there,” Whatley said. “They’re not all great. We have to ensure that whatever we invest in, we get the ROI on.”

AI governance wasn’t an afterthought; it was the foundation of all the projects to come — and Mary Kay’s AI initiatives are better off for it, according to Whatley. 

“Committees and governance have a bad perception out there that it just slows things down or it's no gain, and that’s definitely not the case here,” Whatley said. “It has helped us consolidate the usage of approved tools as opposed to being scattered across groups and countries and that’s been very advantageous for us.”

The focus on governance has also helped project prioritization efforts. Before a proof-of-concept is even approved, the committee requires tools to prove their value in a sandbox environment. 

“A lot of times, it doesn’t pan out — what’s being sold is not what’s delivered,” Whatley said. “We’ve been able to be cost-conscious from that perspective and really been able to make progress without losing a lot of ground as well.”

AI use cases

Amid adoption efforts, leaders are looking closely at the intersection between AI and productivity.

Mary Kay has reviewed manual, repeatable tasks and assessed where AI could step in. Code generation, video translation and image creation are a few of the earliest use cases that have proven valuable. 

“Our goal is to get everyone understanding AI enough to help them in their specific role,” Whatley said. “In order to get there, you need more grassroots-type approaches to share the power and the risk of it.”

Mary Kay hosted a monthly series for teams to showcase potential use cases and foster discussion. One event in November drew more than 150 people, Whatley said. 

The company is also exploring customer-facing use cases, such as its AI Foundation Finder. The tool, which launched in August, uses AI to identify 151 facial feature points, analyze the user’s skin tone and match it to the corresponding Mary Kay foundation. 

“There was a tremendous amount of testing,” Whatley said, pointing to both internal teams and third-party reviews. “The model was tweaked many times until we felt like it was right.”

Companies are often more cautious about AI use cases that are customer-facing. Accuracy needs to be higher, and the tolerance for hallucinations is lower. 

The tool has been used more than 185,000 times, according to the company. Teams are working on future iterations that can simplify the shopping process, allowing customers to add the foundation to their cart and purchase after being recommended.

“We’re continuing to tweak the model, and that’s the way this works,” Whatley said. “It’s only going to get better from this point forward.”

The business world is preoccupied with gauging the return on investment for AI projects as the technology expands across operations.

The concerns stem from rising enterprise spend to support the deployment of AI tools. More than half of organizations allocate between 21% and 50% of their digital initiative budgets to AI, according to a Deloitte Insights survey. That equates to about $700 million for a company with $13 billion in annual revenue, according to the firm's estimates.

Yet before companies can measure the success of their AI efforts, they must test their solutions and scale them into production — a tricky step for most businesses

A willingness for CEOs and their boards to swing big is a prerequisite for such endeavors. Committed partners from human resources, sales, marketing and other business units are critical, too.

AI magic for HR, payment leasing

When fintech firm Prog Holdings was migrating to Workday's human resources software, leaders wondered if they could leverage AI to help employees get answers to questions on benefits and IT issues.

CTO Sridhar Nallani partnered with leaders across HR, R&D and other business functions to build Piper, a chatbot that uses a large language model to provide access to information.

The software has resolved more than 18,000 employee questions, with 58% answered correctly on the first interaction, according to Nallani. The resulting adoption spread like wildfire, Nallani told CIO Dive.

While the uptake was rapid, it wasn’t an overnight success. To gauge the chatbot’s competency, Nallani’s team started with a proof-of-concept based on ChatGPT, testing the chatbot with a small number of users before rolling it out across broader segments of the business, Nallani said.

Piper’s success emboldened Nallani to create an application that helps Prog set lease terms, pricing and eligibility to generate instant offers for customers. Nallani said the software drove a 75% reduction in decision time while improving direct-to-consumer conversions by 10%. 

Prog also built a generative AI tool that helps customers pay for products, as well as software that produces content for marketing campaigns. The company has since standardized on Microsoft Copilot for most of its chat-oriented AI services.

Although the global obsession over ROI isn’t lost on Nallani, he said that not every AI implementation needs to deliver a calculable return. Building digital services that reduce friction and time creates happier employees and loyal customers.

“You don’t need to put a number in a spreadsheet and say, ‘I’m going to get $10 million or $12 million out of this thing,’” in order for it to create value, Nallani said.

AI agents keep freezers stocked

The cost-cutting potential of AI agents quickly became apparent to Arctic Glacier Premium Ice, CIO Doug Saunders told CIO Dive.

Deploying agents to automatically field inquiries about ice shipments, the packaged ice provider shaved tens of millions of dollars by reducing customer call center and shipping operations, said Saunders, who joined the company in 2023. These operations were the result of trial and error, including a platform switch before revving to production, said Saunders.

Arctic Glacier started with an agentic AI system built on Microsoft Copilot that helped answer customer requests to order ice or track their shipment locations. With promising early returns, Saunders expanded the program.

The company eventually migrated to Salesforce’s Agentforce platform, connected to an IoT system that includes lidar sensors in the ice merchandise boxes at each customer site.

When the system senses that ice is running low, it fires off an order to AI agents, which begin routing trucks to fulfill orders. The system also takes into account historical sales trends, weather analytics and other factors to anticipate each route’s ice requirements.

Saunders said automated ice deliveries represent a competitive advantage — at least for now. The company is exploring dynamic pricing for businesses in warmer climates who may run out of ice earlier than expected.

Iteration is guiding the technology strategy. “You start with one tiny business problem, you prove it, you get a really good ROI and you see the different ways you can expand it and make that EBITDA growth even bigger,” Saunders said.

The path to value is rarely linear

Despite positive outcomes, CIOs would do well to remember that AI is still a tool, not magical fairy dust. That’s the perspective of Mihai Strusievici, who founded Axsion Digital Evolution to consult SMBs on their IT strategies.

Strusievici said the relentless pursuit for ROI happening at some companies and consultancies misses the bigger point, which is that AI can help users tackle tasks better, faster and potentially cheaper.

“Did they calculate the ROI of the word processor or spreadsheet?” Strusievici asked. “I think it’s a fantastic tool, but it’s still a tool.”

What’s clear is that AI pilots are scaling from exploration into production.

While only 25% of senior leaders said their organization moved 40% or more of their AI experiments into production to date, 54% expect to reach that level over the next several months, Deloitte said in a recent report tracking AI adoption and impact.

In this period of AI exploration, IT leaders must search for solutions that deliver genuine value to the business, said Jason James, CIO of retail tech provider Aptos Retail. That path is rarely linear.

“We’re in an R&D era,” said James, who is experimenting with AI agents. “We need to figure out what works. We’re going to spend money on stuff that we’ll find out doesn’t work and has low value.” 

Securing AI has become a top priority for CIOs, according to a Logicalis report. The report, which surveyed more than 1,000 CIOs globally, found more than a quarter see AI as a significant source of risk, placing it nearly on par with traditional threats such as malware, ransomware and phishing. 

Employee misuse of AI is compounding concerns, with 57% of CIOs saying staff are putting data security at risk. Despite the mounting risk, AI governance measures remain limited, with just 37% of organizations saying they have visibility into the AI tools in use. 

The challenges posed by the advent of AI are significant enough that nearly half of respondents in the Logicalis report said they wish AI had “not been invented.”

While traditional threats remain the dominant concern for CIOs, AI is being increasingly cited as a risk as business leaders grapple with critical issues such as shadow AI, app sprawl and lack of oversight.

Security teams, already strained, are losing ground in the face of increased blind spots, with more than one-third reporting a reduced ability to detect breaches and worsening incident response times. 

At the same time, internal misuse of AI is introducing new workforce challenges. Two-thirds of respondents say employee training on AI risk management is insufficient, while 94% of CIOs report a cybersecurity skills shortage. 

While a renewed focus on upskilling and expenditure on post-breach remediation is rising in tandem with the threat, there is a growing need for more to be done to shift response from reactive to preventative.

“AI is a powerful force in cybersecurity, but without the right skills and governance, it can create more vulnerabilities than protection,” said Bob Bailkoski, CEO of Logicalis Group, in the report. “CIOs have the challenging task of defending their organisations against AI-driven threats, but also from the risks posed by the very AI tools meant to safeguard them.” 

In response, Bailkoski suggests CIOs integrate governance and transparency into AI initiatives from their inception to ensure long-term stability.

The findings align with broader industry concerns. Recent reporting from Cloud Security Alliance and Thales similarly highlighted how unstructured data and poorly governed AI pipelines are expanding the enterprise attack surface, with 68% of companies surveyed saying a majority of their data remains unprotected.

Reliable data, AI-specific upskilling and improved governance were similarly identified as key tools in mitigating risk.

Initiatives such as Project Glasswing, Anthropic’s recently announced effort to identify and remedy software vulnerabilities, further underscore a growing push to scale governance alongside tech adoption.

The initiative was launched alongside partners including AWS, Apple, Broadcom, Cisco, Google and others who will use Anthropic’s Claude Mythos Preview model to identify and fix vulnerabilities.