By Adam Mills
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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 front lines of AI adoption
Is AI Delivering Results or Just Activity?
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By Adam Mills
Frontline tasks are already being handled by AI in many companies. Proving that it’s delivering real results is the next test.
“The mindset has to shift from experimentation to operationalization,” Rich Veldran, CEO of GoTo, told Newsweek. “If you can’t answer whether it’s improving revenues, improving margins or improving productivity, you’re still just experimenting.”
GoTo, formerly LogMeIn, which provides business communications and IT management software, is already applying that standard of improvement to customer operations. Veldran said most customer service calls are now handled by AI, often resolved without human involvement or routed to the right person when escalation is needed. That has allowed employees to focus on more complex issues and customer relationships.
“[AI’s impact] is very real—at least for GoTo,” he said.
The distinction, Veldran said, is not whether companies are using AI, but whether it is embedded deeply enough to change outcomes. Systems layered on top of existing processes may look impressive, but they rarely alter how tasks are completed.
“Real, effective AI is embedded in a workflow to drive value,” Veldran said.
Veldran pointed to products labeled “AI-powered” without clear links to measurable results.
“The biggest gap is between what companies call ‘AI’ and what it actually does,” he said.
Many organizations still track activity—features launched or tools deployed—rather than whether those efforts actually produce meaningful outcomes.
“Companies need to be measuring outcomes, not inputs,” Veldran said.
GoTo’s own research found that a significant share of IT leaders say their organizations are not effectively measuring the return on AI investments.
That outcome-focused approach also shapes how GoTo’s customers use AI. One example comes from auto dealerships, where incoming calls often pull staff away from customers on the floor. Through GoToConnect, AI agents now handle a large portion of those interactions—scheduling appointments, answering routine questions and routing more complex issues—allowing employees to stay focused on customers in front of them.
“It’s not a cool tech demo,” Veldran said. “It’s a real operational shift.”
As more of that routine customer activity is automated, the role of human teams is expanding rather than shrinking. Routine tasks are handled by AI, while employees spend more time on problem-solving, relationship-building and higher-value responsibilities.
“AI should be seen as a force multiplier,” Veldran said.
“They tie every AI initiative to a workflow, every workflow to an outcome, and every outcome to ROI,” he said.
“If it’s not changing how work gets done daily, it’s just theater,” Veldran said.
Core Intelligence
AI Is Breaking IT Services Pricing
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By Adam Mills
AI is making it harder for IT services firms to justify what they charge.
Clients have more visibility into how software is actually produced. As AI systems take on more of the execution, smaller teams are able to deliver work that once required far larger groups, often in less time. That is changing what clients are willing to pay for.
“Customers are now aware that the cost of inputs is dropping,” said Seth Robinson, vice president of industry research at CompTIA, an IT industry trade association. When pricing remains tied to hours or staffing levels, it raises a more fundamental question than cost: What is the price actually measuring?
“The model no longer reflects how value is created,” said Abby Kearns, CEO of ActiveState, a software supply chain security company.
Greater transparency into how work is produced, combined with pricing that has not kept pace, puts pressure on a long-standing belief: that effort is a reasonable proxy for value.
“A headcount-based model assumes that value scales with effort, but AI fundamentally challenges that premise,” said Rajesh Nambiar, president of NASSCOM, the Indian IT industry trade association.
Providers are left with a tradeoff: hold pricing steady and risk pushback, or adjust pricing and give up the gains created by higher productivity. Either path exposes the limits of a model built around billing for effort.
Clients, meanwhile, are being pushed to define what they are paying for—capacity, output or measurable business results. Output- or outcome-based pricing has long been discussed, but it requires a level of clarity many organizations still lack, including agreed-upon metrics and a shared definition of success.
“It can be very difficult to change quickly,” Kearns said, particularly when expectations are adjusting faster than the systems designed to support them.
As AI expands what smaller teams can deliver, the gap between billed effort and delivered results becomes easier to spot—and harder to ignore. What once passed as standard pricing starts to look out of step with how the work is delivered.
Over time, that misalignment becomes a competitive issue.
Firms that can clearly connect pricing to outcomes will have a stronger position with clients who understand how the work is produced. Those that cannot may find their pricing challenged—not just by competitors, but by the buyers themselves.
At some point, the question is not whether pricing changes. It’s whether clients still accept it as credible pricing.
You can read the full article here: AI Is Forcing IT Services to Rethink How They Get Paid.
Upcoming Webinars
The Trillion-Dollar Question: Who Wins and Who Loses in the AI Services Economy?
As AI accelerates across the enterprise, it is forcing leaders to rethink how technology spend is structured—and how much productivity it must deliver. In an upcoming AI Impact Forum session, Dr. Ranjit Tinaikar speaks with Noshir Kaka, senior partner at McKinsey & Company, about how this is reshaping enterprise spending—and what it means for organizations trying to keep up.
Join the live discussion on Thursday, May 28, at 10 a.m. Eastern. Register, for free, right now.
Prompt Injection
What’s one recent insight you’ve learned about AI?
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“In both personal and professional contexts, the technology adoption curve has been a studied phenomenon for decades. Historically, about 16 percent of adopters are laggards, and another 34 percent are late adopters. We’ve come to accept these patterns as human nature.
What’s different in 2026 is AI’s speed and what’s at stake. Adoption timelines are compressing as enterprise AI reshapes how quickly software and new offerings can be designed, deployed, and iterated. Companies that have embraced and adopted the technology are already seeing measurable competitive advantages. That shifts the leadership risk profile. C-suite executives who approach AI as a late-majority adopter will risk materially weakening their organization’s agility at a time when new competitors can emerge and scale almost overnight.”
Have your own lesson to share? Email us at: ai.newsletter@newsweek.com
Run Log
AI use case of the week
By Adam Mills
Changing health behavior rarely comes down to a single decision. The same person might follow a treatment plan in one moment and ignore it in another, influenced by context, timing and competing priorities. Traditional approaches to behavior change tend to rely on broad assumptions, grouping people by demographics or past behavior without adjusting as those conditions evolve.
Dr. Amy Bucher, chief behavioral officer at Lirio, focuses on how systems can respond to that variability. Rather than predicting what people should do, the goal is to learn from how they actually respond in real-world settings.
At Lirio, that means using AI to track responses over time and refine which behavioral techniques are deployed for each individual. The system draws on frameworks developed by behavioral scientists and adjusts interventions based on what proves to be effective across different situations and health needs.
In vaccination programs, using AI to refine which messages are delivered to each individual has shown how quickly those changes can influence behavior. After effective messaging was identified, individuals who had not responded to earlier outreach took action after receiving just over one additional message.
“AI is exceptional at identifying patterns across massive, noisy datasets,” Bucher said, “but only when grounded in expert, domain-specific human input.”
Behavior change can’t be reduced to a single model or message. It requires systems that learn from each interaction, with human expertise shaping how those signals are interpreted and applied.
Have an AI use case to share with us? Email us at: ai.newsletter@newsweek.com
Context Window
■ General Motors is rolling out Google’s Gemini AI assistant to roughly 4 million vehicles through over-the-air updates, bringing advanced in-car AI to older models and reinforcing the company’s push to build its own software platform. [Newsweek]
■ Google has signed a deal with the Pentagon to provide its AI models for classified use, allowing deployment for “any lawful governmental purpose” and prompting internal concern among employees over potential risks. [The Hill]
■ China has blocked Meta’s acquisition of AI startup Manus after a national security review raised concerns about technology transfer, a decision that signals tighter controls on cross-border AI deals. [Los Angeles Times]
■ The University of Wisconsin–Madison has named a founding dean for its new College of Computing and Artificial Intelligence, backed by more than $100 million in philanthropic funding and a major faculty hiring push as universities expand AI research and workforce development. [UW–Madison]
■ Amazon introduced new AI agents for hiring and supply chain operations, including tools that can conduct job interviews and handle planning tasks with minimal human involvement, signaling a deeper push into enterprise automation. [Reuters]
■ Startup Lovelace introduced a platform built on knowledge graphs to improve AI agent reliability, as enterprises look to reduce hallucinations and better ground models in accurate context. [CIO]
Transfer Protocol
Tracking executive moves across the AI landscape
Aaron Wright, previously director of application development at the Alabama Office of Information Technology, has been appointed Alabama’s first chief artificial intelligence officer, where he will coordinate AI strategy across agencies, establish governance and support adoption of AI tools across state operations.
Sandhya Gardner, previously general manager and chief medical officer of the clinical SaaS division at HealthEdge, has been named chief medical officer at Autonomize AI, where she will lead clinical strategy, AI validation and enterprise adoption of the company’s solutions to support care delivery and reduce administrative burden.
Spike Lipkin, previously co-founder and chief executive officer of Newfront, has been appointed chief AI officer at WTW, where he will guide the company’s artificial intelligence strategy and embed AI across its business, operating model and long-term growth plans.
Neill Feather, previously president and chief operating officer at Point Wild, has stepped into the role of chief executive officer at Point Wild, where he will lead its next phase of growth as the company scales its AI-driven cybersecurity platform and expands its operations.
Bill Parker, previously chief technology officer at Leap, has joined Trust & Will as chief technology officer, where he will lead technology and engineering strategy, including platform development, AI innovation and the infrastructure supporting its AI-powered planning tools.
Know someone on the move in AI? Send job change info to ai.newsletter@newsweek.com
Magic Moment
What’s the most fun or unexpected way you’ve used AI lately?
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“The most unexpected thing I've done with AI recently was watch a support agent troubleshoot a customer issue with direct access to the codebase.
We have our codebase connected alongside all our usual sources (Slack, Intercom, help center) to our agent.
Last week, someone on the support team asked: 'What happens when a blocked user tries to sign up?'—something not documented that would waste precious engineering time in the past.
Our agent Super browsed the codebase, walked through every relevant code path and returned the exact behavior. No ticket filed. No engineer interrupted. No two-day wait.
What struck me wasn't the speed; it was what it revealed about how knowledge actually works in a company. The code has always been the real source of truth—the one place that can't lie.”
Experience some AI magic? Tell us about it at ai.newsletter@newsweek.com
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