Editor's note: This is AI Impact, Newsweek's weekly newsletter where each week, we will explore how business leaders are unlocking real value through artificial intelligence.

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Signal Capture

Signals from the frontlines of AI adoption

Blue Yonder CEO Says Companies Are Thinking Too Small About AI

By Adam Mills

Supply chain leaders are asking AI the wrong question, according to Duncan Angove, CEO of Blue Yonder.

“I think companies are focused on the wrong unit of transformation when they think about what AI changes,” Angove told Newsweek. “The question I hear too often is, how many planners or logistics coordinators can we replace with agents? That’s the wrong unit.”

The better question, he said, is what AI lets the supply chain do that it could not do before.

“The unit of transformation is not the user,” Angove said. “It’s the system and the outcomes it delivers.”

Blue Yonder, a supply chain software company, surveyed 678 senior supply chain professionals across North America and Europe for its latest “Supply Chain Compass” research. All respondents worked at organizations with at least $500 million in annual revenue in retail, manufacturing or logistics.

The findings show why the system matters. Respondents ranked improving efficiency and productivity as their top organizational priority, followed by faster and better decision-making, increasing profitability, managing supply chain cost and becoming more resilient to risks and challenges. Developing a more AI-driven supply chain ranked lower as a standalone priority.

That does not mean AI is secondary. It suggests that supply chain leaders are likely to judge AI by whether it improves the business outcomes they already care about: faster decisions, fewer disruptions, lower costs, better service and less waste.

Angove said AI’s value is clearest when it can coordinate decisions across a network that is usually disconnected.

“AI delivers real-time insights, decision-making, and coordination across a complex, multi-enterprise network, providing value beyond what current systems can offer,” he said.

The risk is that companies add AI to the same disconnected systems they already have.

“Historically, supply chains have been fragmented, with each node siloed from the others,” Angove said. “This design isn’t viable for an AI-powered supply chain, which requires each system to communicate with one another and cascade data through the chain to inform decision-making at each function.”

He compared the moment to factories that first adopted electricity but kept the old layouts built around steam power.

“When factories first adopted electricity, many simply replaced the steam engine with an electric motor, but kept the same belts, pulleys, layouts, and workflows,” Angove said. “They treated electricity like a slightly better steam engine. And productivity barely improved.”

He added, “The danger with AI in supply chains is the same. We simply bolt intelligence onto yesterday’s workflows instead of reimagining how supply chains should operate.”

Supply chains are not abstract software environments. They involve warehouses, factories, transportation networks, suppliers, stores, inventory levels, labor constraints and customer commitments.

Angove said companies often get AI adoption wrong by adding another tool to each function instead of connecting the functions themselves.

“Applying agents to supply chains cannot be built from a lab alone,” he said. “Supply chain operations can be very messy, even at the global level. And operational truth is often hidden inside tacit workflows and tribal knowledge.”

When AI is added to fragmented processes, he said, the result is limited.

“You end up with smarter silos, not a smarter supply chain,” Angove said.

The report points to the kinds of outcomes companies are already looking for from AI. Respondents cited better planning and predictability, better risk management, faster decision-making, increased productivity, better execution and faster responsiveness as benefits AI can bring to supply chains.

Still, he argued that supply chain AI needs to be built for the constraints of the environment where decisions carry immediate operational consequences.

“Supply chain is not a generic reasoning problem,” Angove said. “It’s a deeply operational environment with hard constraints, real-time execution, physical consequences, extreme scale, and thousands of interconnected decisions happening continuously across warehouses, transportation networks, suppliers, stores, and factories.”

Angove expects companies to match the technology to the supply chain decision in front of them.

“Companies need AI tools that leverage frontier models where broad reasoning is needed, and specialized models where operational precision, speed, and cost are concerned,” Angove said. “This hybrid approach will be the future of AI-powered supply chains.”

The long-term test is not whether a company has adopted agents. It’s whether AI helps the supply chain respond faster when demand shifts, inventory tightens or suppliers fall behind.

“The ones that get left behind will be the ones using AI to do the same old things slightly faster,” Angove said. “Agentic technology brings us closer than ever to a truly autonomous supply chain, but it requires us to elevate our thinking beyond silos and users to the systems level.”

Core Intelligence

AI Gives Financial Firms More Time. Now What?

By Adam Mills

AI is pushing financial firms past the easy productivity story and toward a more consequential question: what the extra capacity is actually for.

New tools are already helping analysts, associates and other finance workers move faster through research, analysis, presentations, communications and client work. Kevin Buehler, chief innovation officer at Rogo and senior partner emeritus at McKinsey & Company, said that impact is showing up most clearly among junior professionals.

“The power of AI tools and the rise of agentic AI is making a huge difference at the coalface,” Buehler told Newsweek.

But faster individual work is only the beginning of the argument. The real test for financial institutions is what they do with the capacity AI creates. A bank, investment firm or advisory business can use the time to reduce costs, cover more clients, analyze more markets or spend more energy on judgment-heavy work. Those choices lead to very different versions of AI adoption.

Buehler will discuss that distinction during Newsweek’s upcoming “AI Impact Forum” webinar, “AI in Finance: From Individual Adoption to Enterprise Transformation,” scheduled for Thursday, June 18, at 9:30 a.m. Eastern. Dr. Ranjit Tinaikar, host of the series, will speak with Buehler about how agentic AI could reshape financial services and what it may mean for analysts, investors, software companies and services firms.

Finance has no shortage of tasks that can be accelerated. The bigger challenge is turning scattered productivity gains into something that changes how the business runs. In a conversation with Newsweek ahead of the webinar, Buehler pointed to client onboarding, M&A transactions and lending as examples of complex workflows where AI could eventually do more than assist with individual steps.

“How do you use AI to take a process like that and really transform it?” Buehler said. “That’s what I see as the frontier today.”

Moving beyond personal productivity, Buehler described three broad stages of adoption: junior employees using AI to speed up daily work; senior leaders learning how to use AI and manage AI-assisted teams; and full workflow redesign across business processes.

Most firms have not reached that third stage. Doing so requires more than tool access. Buehler said companies need an economically driven road map, senior business leadership, stronger data foundations, talent development and change management.

“They probably need data, and that’s often the Achilles’ heel of these programs,” Buehler said. “They need talent. The talent has to be upskilled and reskilled because you’re not going to just hire new folks to do this.”

That makes AI in finance less a story about whether junior work disappears and more a question of organizational intent. If firms treat AI as a way to make the same work cheaper, the result may be cost savings. If they use it to expand coverage, improve decisions and train younger employees differently, the technology could change what financial professionals are expected to do.

The competitive difference may come from whether AI remains a personal accelerator or becomes part of how the firm actually runs. Buehler said many firms are still in “the very early innings” of that process.

You can read the full length article here: What’s Next for AI in Finance? Join Our Webinar Discussion.

Upcoming Webinars

AI in Finance: From Individual Adoption to Enterprise Transformation

Financial services firms are beginning to face a harder AI question than whether the technology can simply make professionals more productive. The real test is what they do with those gains.

In an upcoming “AI Impact Forum” session, Dr. Ranjit Tinaikar speaks with Kevin Buehler, chief innovation officer at Rogo and senior partner emeritus, McKinsey & Company, about how agentic AI could reshape financial services—and whether productivity gains become cost savings, new ways of working or a lasting competitive advantage.

Join the live discussion on Thursday, June 18, at 9:30 a.m. Eastern. Register, for free, right now.

Is India on the Right Side of the AI Trade?

India’s role in the global technology economy has long been shaped by scale: a massive IT services sector, fast-growing capital markets, digital public infrastructure and a young workforce. AI could raise the stakes even further by changing where value is created, who captures it and how countries compete.

In an upcoming “AI Impact Forum” session, Dr. Ranjit Tinaikar speaks with Shri Ashishkumar Chauhan, CEO of the National Stock Exchange of India, about whether India is positioned to benefit from the AI revolution—and what the technology could mean for the country’s economy, financial markets, market infrastructure and next stage of growth.

Join the live discussion on Thursday, July 23, at 10 a.m. Eastern. Register for free.

Prompt Injection

What’s one recent insight you’ve learned about AI?

"One of the biggest realizations I’ve had about AI is that the industry is moving through four distinct phases far faster than most organizations realize: from tool to operator to adaptive system to enterprise infrastructure.

Most companies today are still using AI as a tool: copilots and chat interfaces that help humans work faster. Useful, but incremental. The real shift begins when AI becomes an operator and systems execute workflows autonomously, orchestrate decisions and take action in real time.

From there, AI evolves into adaptive systems that continuously learn from every interaction, attack and outcome. Ultimately, AI becomes enterprise infrastructure itself, a continuously optimizing operational layer embedded across the organization.

That realization has fundamentally changed how I think about building systems. The future winners will have the strongest intelligence layers, operational autonomy, continuous learning loops and trust infrastructure on top of the best models.

In fraud and identity specifically, this means moving beyond static transaction scoring toward systems that reason continuously across identities, devices, behavior and intent in real time—including verifying not just humans, but increasingly the agents acting on their behalf."

Have your own lesson to share? Email us at: a.mills@newsweek.com

Run Log

AI use case of the week

A clinician starting a visit by apologizing for the wait is a sign that the front office problem has reached the exam room. At Graybill Medical Group, an independent multispecialty medical group in Southern California, patients were struggling to get through by phone while the organization was dealing with staffing shortages and the need to keep training new front-office workers.

Royce Cheng, chief product and technology officer at Third Way Health, a health care operations company, described the problem as bigger than call volume. Phone support, patient access and medical records all had to improve together.

Third Way Health paired AI-supported workflows with live-agent support across those areas. One of the clearest changes was quality assurance. Instead of reviewing a small sample of calls, the system evaluates every call in more detail, with operations experts using those findings to adjust the process over time.

Patient-access operations often break down in small places: a missed call, an incomplete handoff, an unprocessed records request or a patient unable to reach the right person. By applying AI to every interaction rather than spot-checking a few, Graybill could see where the process was failing and correct it faster.

According to Cheng, the work at Graybill was associated with about $3 million in savings, a 50 percent reduction in front-office costs and a 24 percent reduction in no-shows. He said Graybill also saw a 1.5 percent call abandonment rate, a 20.2-second average wait time and a 4.8 out of 5 post-call customer satisfaction score.

“AI works best when it improves the operating model around the work,” Cheng said.

At Graybill, that meant using AI where the backlog was being created: in the calls, records requests and front-office routines that determine whether patients can get through.

Have an AI use case to share with us? Email us at: a.mills@newsweek.com

Context Window

■ Rivian CEO RJ Scaringe told Newsweek the company has cut long service wait times in some key markets to two or three days ahead of the R2 launch after major investments in service capacity, diagnostics tools and AI-supported service-center operations. [Newsweek]

■ Jeff Bezos’ Prometheus raised $12 billion at a $41 billion valuation to build AI models for engineering, manufacturing and drug design, with much of the new capital aimed at compute for physical-world AI systems. [CNBC]

■ IBM and ServiceNow expanded their partnership to help enterprises modernize legacy applications, improve AI-ready data governance and automate IT operations through joint solutions planned for release in the second half of 2026. [IBM]

■ The World Economic Forum says AI skills programs may need to move beyond platforms and certifications toward human-led workshops that build employee confidence, peer learning and practical use cases across teams. [World Economic Forum]

■ The Economist examines growing pressure on the SaaS industry as AI labs, startups and enterprise-built tools challenge traditional software vendors, while agent products put pressure on seat-based pricing and recurring revenue models. [The Economist]

■ Anthropic launched Claude Corps, a $150 million fellowship that will train 1,000 early-career workers to use Claude and place them with at least 400 U.S. nonprofits, a workforce-development effort tied to the company’s broader response to AI’s impact on jobs. [Anthropic]

Transfer Protocol

Tracking executive moves across the AI landscape

Kenneth W. Bible, previously chief information security officer at the Department of Homeland Security, has joined Assail’s board of directors, bringing federal cybersecurity and modernization experience as the company expands its autonomous offensive AI platform across government and critical infrastructure environments.

Matt Rothstein, previously vice president of engineering at Goldman Sachs, has joined Akido as vice president of engineering at the California-based company, which builds AI-powered tools for doctors and delivers clinician-supervised care through its own multi-specialty provider network.

John Roa, founder of the agentic food-intelligence platform AlchemyAI, has been named chief AI officer and general manager of Raptive Intelligence, a new business unit focused on AI grounding, licensed data access, real-time intelligence products and agentic commerce infrastructure for creators, publishers and brands.

Sneha Shah, previously a senior executive at London Stock Exchange Group, Refinitiv and Thomson Reuters, has been named chief AI strategist and head of SEI Next, where she will lead enterprise AI strategy, guide decisions on whether to build, acquire or partner and help translate emerging technologies into long-term business value.

Michele Fisher, previously global strategy director at Microsoft and a retail media leader at Amazon Ads, has joined Rezolve Ai as chief marketing officer, bringing AI commerce and retail media experience as the company expands its agentic commerce infrastructure.

Billy Biggs, previously president and chief revenue officer at TechnoMile, has been appointed chief executive officer at KnowledgeLake, an AI-powered enterprise workflow automation platform focused on intelligent document processing, workflow automation and content management.

Know someone on the move in AI? Send job change info to a.mills@newsweek.com

Magic Moment

What’s the most fun or unexpected way you’ve used AI lately?

“I've started using AI as an intellectual sparring partner rather than a search engine. The most unexpected thing is how often it helps me discover better questions rather than better answers.

It shifted my view of AI from an answer engine to a thinking partner. The most valuable thing it did wasn't about giving me a solution; instead, it challenged my assumptions. As someone who spends a lot of time making decisions under uncertainty, I've found that often the quality of the question matters more than even the quality of the answer.”

Experience some AI magic? Tell us about it at a.mills@newsweek.com

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