Meet 10 AI trailblazers who are steering their companies into tech’s new age

The corporate rush to adopt AI is shifting into high gear.

As of July 2024, 78% of companies reported using AI for at least one business task, up from 55% since late 2023, according to a report from McKinsey & Company. From IT automation to personalized marketing, businesses are betting on AI to cut costs and drive growth.

But AI adoption isn’t without risks, including cybersecurity threats, data leaks, unreliable or biased model outputs, and a rising carbon footprint.

For Business Insider’s “AI in Action” series, we profiled 10 executives driving AI adoption at companies like JPMorgan Chase, PwC, and BMW. From chief AI officers to HR leaders, they’re helping their organizations stay competitive — and safe — in the age of AI.

At JPMorgan Chase, Teresa Heitsenrether leads the rollout of the bank’s proprietary LLM Suite. The suite has given over 220,000 employees access to AI tools that can summarize internal data. The platform integrates capabilities from multiple leading AI providers and has already saved employees several hours of work a week, Heitsenrether said.

“LLM Suite’s widespread adoption is driving a cultural transformation across the bank,” Heitsenrether told Business Insider.

This rollout isn’t just about experimentation — it’s about execution. JPMorgan tracks every AI use case from ideation to production, ensuring each AI initiative drives business value. That includes cost savings, revenue generation, and risk reduction, Heitsenrether said. Working under this framework helps the bank prioritize projects with the highest potential impact.

Additionally, employees across the bank are trained through a mix of in-person and online courses to use AI effectively and responsibly.

Joe Atkinson is embedding AI across the firm’s operations and services. He led the launch of an internal AI chatbot deployed to 270,000 employees to help them generate reports and assist in project delivery.

Under his leadership, PwC also rolled out tools like Code Intelligence, which helps organizations modernize legacy systems through generative AI-powered code conversion, and Agent OS, which was designed to streamline AI-driven workflows.

Additionally, Atkinson said he launched a global AI Academy, which has trained 90% of staff in prompt design and responsible AI use.

“We’re seeing a ton of growth and capability from our people,” Atkinson said, pointing to rising usage and more complex tasks handled by AI.

He emphasized the importance of trust, governance, and accountability, adding that virtually every function at PwC is expected to be AI-powered in the near future.

Bala Subramanian is leading UPS’s digital transformation with a focus on integrating AI across operations.

Under his leadership, UPS has implemented the Message Response Automation system, which uses large language models to automate responses to customer inquiries and reduce agent handling time.

“By alleviating the burden on our human agents, it enables them to focus on more complex and nuanced customer needs,” Subramanian told CIO.com.

Beyond customer service, Subramanian is overseeing the rollout of AI and automation technologies — like pick-and-place systems and autonomous guided vehicles — across shipment operations. Bloomberg reported that as of April, UPS is exploring a potential partnership with Figure AI, a robotics startup, to implement humanoid robots into its operations for tasks like sorting parcels.

UPS is also investing in AI infrastructure, signing a decade-long deal with NTT Data in late March to modernize its data centers — part of a broader strategy to update its logistics network and improve service delivery.

UPS didn’t respond to BI’s request for comment.

Johan Gerber is helping Mastercard use AI to strengthen fraud detection across its global payment network. His team oversees cybersecurity, digital identity, and dispute resolution in an effort to protect businesses and card users from scams.

With Gerber at the helm, Mastercard launched tools like Decision Intelligence and Safety Net, which use generative AI, machine learning, and data scanning to analyze transaction patterns, improve fraud detection rates, and flag suspicious activity.

LLMs have also been integrated into MasterCard’s Recorded Future tool, which allows analysts to quickly sift through its threat-intelligence database with queries like, “Tell me about the new malware families you found yesterday.”

Gerber told BI that combining generative and traditional AI has improved detection accuracy, but he also stressed the importance of rigorous testing and data governance before deploying models into production. He also recommended a method called “silent scoring,” which runs new AI models alongside active models before they go live; this helps assess the new model’s soundness and vulnerabilities.

Suresh Kumar is leading Walmart’s AI transformation to boost efficiency and personalize retail experiences.

He helped launch “My Assistant,” a generative AI tool that helps over 50,000 corporate employees summarize documents, draft content, and streamline onboarding. The company has also invested in developing proprietary LLMs like “Wallaby,” trained on decades of internal data, to power customer-facing assistants and personalized homepages.

In supply chain operations, Walmart uses AI for product placement, inventory management, and robotic automation. The Wall Street Journal reported that the company is testing autonomous shopping agents that can purchase items for customers.

“A standard search bar is no longer the fastest path to purchase, rather we must use technology to adapt to customers’ individual preferences and needs,” Kumar said in an October press release.

Walmart didn’t respond to BI’s request for comment.

Marco Görgmaier is leading BMW’s AI integration across production, engineering, and marketing. He said the company’s generative AI strategy centers on three pillars: adopting third-party tools, building proprietary applications, and providing in-house tools like the BMW Group AI Assistant, which lets staff create custom software without code. Görgmaier said training programs, development guidelines, and hands-on support further ensure teams are equipped to navigate the AI shift.

“While conventional AI has driven efficiencies in targeted areas like predictive maintenance and quality assurance, generative AI expands the horizon, enabling automation, creativity, and innovation across the entire organization,” Görgmaier told BI.

He said it’s important to start every AI project with a clear business need and ensure cross-functional collaboration and compliance. A key to scaling AI, he added, is a centralized, flexible data platform that supports rapid adoption of new technologies while ensuring cost efficiency and regulatory compliance.

Bhavesh Dayalji is guiding efforts to boost productivity through internal generative AI tools at S&P and Kensho, S&P’s AI and innovation research division.

Spark Assist, for example, is an internal chatbot that helps more than 40,000 employees draft reports, synthesize data, and streamline workflows. Unlike public tools like OpenAI’s ChatGPT, these are built on proprietary data and include agentic features for task automation. Another tool is Chat IQ, a chatbot where employees can query S&P’s financial datasets with questions like, “What is the price of a certain stock?”

To ensure widespread AI adoption, Dayalji introduced S&P’s workforce to mandatory training, which included online videos and workshops, designed to upskill leaders and employees.

“This is a transformation journey,” Dayalji told BI. “We want people to have hands-on experience and understand how it’s going to help them.”

Dayalji said S&P will continue placing big bets on AI: Kensho, for instance, is developing a Grounding Agent for autonomous research and analysis using the firm’s proprietary datasets.

While AI is still in its “early days,” Dayalji called the current moment an “amazing time to be involved” in adopting and advancing the technology.

Ulrika Biesèrt is helping Ingka Group equip IKEA’s global workforce — over 160,000 employees across 31 countries — with AI skills. Since launching AI literacy programs in April 2024, the retailer has trained over 4,000 employees in areas like responsible, ethical AI usage, Biesèrt said. The goal, she added, was to train 30,000 employees and 500 leaders.

“At IKEA, we’ve always believed that change should start with people,” Biesèrt told BI.

One tool employees are taught to use is Hej Copilot, an internal generative AI assistant that helps with everyday tasks like creating presentations and brainstorming ideas. To inspire their colleagues, early adopters shared use cases for the tool at workshops.

Implementing such a vast program isn’t easy. Technological change comes with uncertainty, Biesèrt said. To address this, nearly 650 senior leaders were trained, exceeding IKEA’s initial goal, to ensure a “structured, cohesive approach” to AI across the organization, Biesèrt told BI.

Arnab Chakraborty oversees Accenture’s internal responsible AI compliance program to ensure AI is developed, deployed, and scaled sensibly across Accenture and its roster of more than 9,000 global clients. According to the company, Accenture has committed $3 billion over three years to expand its Data & AI practice and double its AI talent to 80,000 professionals.

“We have to ensure that AI solutions have fairness, transparency, and accountability embedded from the start,” Chakraborty said.

He’s also co-leading a $700 million partnership with Telstra. The goal is to help the Australian telecommunications company integrate AI into its workflow processes, including identifying fiber cable defects and faster customer service, AFR reported.

Chakraborty told BI the partnership also involves advancing responsible AI capabilities like real-time model monitoring so that Telstra’s AI systems are compliant with its internal policies and community guidelines.

Building and maintaining the right data infrastructure to scale AI remains a barrier to adoption, Chakraborty added. To address this, Accenture has partnered with the Center for Advanced AI in Mountain View, California, gaining access to research labs at academic institutions like UC Berkeley and Stanford to study data privacy and develop AI training programs for companies.

Chakraborty said by year’s end, Accenture plans to launch over 100 agentic AI tools designed to detect and correct algorithmic issues, such as biases or hallucinations, before they impact business operations.

Boris Gamazaychikov leads Salesforce’s push to decarbonize the company’s AI systems.

Recognizing the significant energy demands of training large AI models, Gamazaychikov’s sustainability strategy emphasizes the importance of developing efficient, domain-specific models. Salesforce’s AI Research team has created xGen, designed for customer relationship management, orCRM, tasks like generating call summaries. These models consume less power and emit fewer emissions than general-purpose ones, according to Salesforce.

Under Gamazaychikov’s leadership, the CRM provider has introduced AI tools that help customers decarbonize, including AI agents for sustainability measurement and a nature-focused accelerator for climate-focused nonprofits supporting forest conservation, regenerative agriculture, and other efforts.

“It is important that software and AI companies are also part of the solution to reduce emissions, and provide tools that help others reduce their emissions,” Gamazaychikov said in an interview for Autonomy’s “The Decarbonists” newsletter.

Salesforce didn’t respond to BI’s request for comment.

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