By Adam Mills

AI Editor

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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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Thanks, as always, for joining me.

This week’s “AI Impact Forum” webinar kept returning to one question: What is a company actually able to do better because of AI?

Firdaus Bhathena, executive vice president and chief technology and transformation officer at S&P Global, joined Dr. Ranjit Tinaikar for our latest conversation, “What Enterprise AI Looks Like When It Works,” and the discussion went beyond faster development and lower costs.

Bhathena talked about S&P Global’s effort to rethink products around customer workflows, the discipline required to narrow hundreds of AI projects to the few that can create meaningful value and the danger of treating adoption itself as a measure of success. One question he posed to executives really captured the point: “What are you doing for your customers today with AI that you couldn’t do two years ago?”

Trust was another major theme. Bhathena said security, compliance and resilience need to be built in from the beginning, and he described transparency as part of how S&P Global earns customer trust. His broader leadership point also stuck with me, that experience can become a weakness when it keeps executives from recognizing how much their organizations need to change.

You can read my full recap of the conversation and watch the complete webinar if you missed it live.

This emphasis on what AI actually improves carries into this week’s Signal Capture as we focus on what happens to job candidates as more of recruiting is automated—including how companies keep applicants informed and where human judgment still matters.

Read on for more.

Signal Capture

Signals from the frontlines of AI adoption

TalentBridge CEO: AI Can Making Hiring Less of a Black Box

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Long gaps between applications, interviews and hiring decisions can leave job seekers with little idea of how their application is progressing. Thomas C. Ioele, CEO of staffing and recruitment company TalentBridge, says AI can improve hiring by reducing that uncertainty.

“A fully connected human-AI recruiting team eliminates the ‘black box’ that candidates dread in the application process,” Ioele told Newsweek.

He said AI can provide status updates, handle scheduling and keep interview feedback, hiring-team decisions and candidate questions connected throughout the process. Faster screening may help employers, but candidates also need better visibility into what happens next.

TalentBridge uses an AI recruiter called Caroline for tasks that include outreach, scheduling and initial screening. Ioele said those early tasks are well suited to AI because handling them manually consumes time recruiters could instead spend on strategy and candidate relationships.

He noted that human recruiters still need to assess a candidate’s motivations, fit with an organization and longer-term potential.

“AI can handle repetitive, administrative and data-heavy work well, but the art of recruiting still depends on human judgment, empathy, trust and the ability to help people make important career decisions,” Ioele said. “When companies automate past that point, they risk removing the very interaction that creates confidence, context and trust in the hiring process.”

Automation becomes more consequential when AI contributes to whether someone advances. Ioele said candidates should know which parts of the hiring process use AI, what information it evaluates and how much weight its output carries. They should also know whether a person reviews the assessment before a decision is made.

That visibility matters as companies use AI for more than scheduling or communication. He said AI-assisted screening can consider technical capabilities, transferable experience and growth potential beyond what a keyword-based resume review may capture.

Ioele said final conclusions should remain with human recruiters. Recruiters can use information surfaced by AI to evaluate qualities such as soft skills, motivation and long-term fit rather than treating an automated assessment as the conclusion.

Hours no longer spent sorting applications and coordinating schedules can be redirected toward deeper conversations with candidates, advising hiring managers and developing longer-term talent pipelines, Ioele said.

Looking ahead, Ioele said AI should reduce unnecessary steps and repeated requests for information while leaving recruiters more time for meaningful conversations.

“AI will give candidates clarity about where they stand, and the ability to move forward with their job search without wasting time,” he said.

Upcoming Webinars

The AI Election: How Artificial Intelligence Shaped the 2026 Midterms

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What did the 2026 midterms tell us about the emerging politics of AI?

Gabriel Snyder, Newsweek’s executive editor, enterprise, will moderate a discussion with policy experts and Washington insiders examining the midterm results through the lens of artificial intelligence.

The panel will feature Nathaniel Persily, professor of law at Stanford Law School; Brent Buchanan, founder and CEO of Cygnal; Nick Penniman, founder and CEO of Issue One; and Andrew Mamo, communications advisor at The Bench.

Using Newsweek’s AI Policy Scorecard as a starting point, the panel will explore where AI policy became a campaign issue, how positions crossed traditional party lines and how debates over regulation, data centers, jobs, privacy and national security played out in individual races.

The conversation will also look at how campaigns and outside groups approached these issues, including the role of industry spending, and what the results could mean for the policy fights ahead.

Register and join the live discussion on November 10.

Enterprise AI in the Physical World: Navigating the Supply Chain’s Messy Frontier

Newsweek

Warehouses, factories and transportation networks put enterprise AI to a different kind of test: digital decisions have immediate consequences in the physical world.

Duncan Angove, chief executive officer of Blue Yonder, joins Dr. Ranjit Tinaikar, host of Newsweek’s “AI Impact Forum,” on November 19 for a conversation about bringing increasingly autonomous systems into complex supply chain operations.

The discussion will cover the role of specialized models, the expertise companies need to preserve as more decisions become automated and whether the economics of enterprise technology could move toward measurable business outcomes such as productivity, operating costs and the movement of goods.

Register to join the live discussion Thursday, November 19, at 12:30 p.m. ET.

Prompt Injection

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

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“A more powerful model doesn’t make the job any easier. It still doesn’t decide what context matters, which actions are safe, or what a good outcome looks like.

I think of LLMs as chainsaws. They make cutting faster, but they don’t do the woodworking. Someone still has to choose what to cut, shape it, and finish it with precision. A more powerful saw can make a bad cut bigger.

That changed how we build at Docusign. We design the system around the work, using the right model for each task, giving it the context it needs, and keeping a person accountable for the result.

It made me ask ‘so what?’ more often. You can pull graphs out of your data, add another skill, spin up another agent. So what? If I can’t point to time saved, dollars made or recovered, or a measurably better compliance posture, I’m not done asking.

That’s our filter for what to build. We look for where the agreement process has real friction and AI’s value is highest. If that intersection doesn’t exist, we don’t build.

I also stay hands-on, automating two or three personal tasks a month. Some work, some fail famously.”

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

Run Log

AI use case of the week

Qodo AI Speeds Code Review at monday.com

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With a roughly 500-person R&D organization working across a technology stack that handles millions of daily user interactions, monday.com, which makes software for managing work and workflows, needed its review process to keep pace. Rule-based checks could enforce established standards, but they were less effective at catching problems that depended on how a proposed change fit with the rest of the codebase.

Itamar Friedman, CEO and co-founder of Qodo, a company that develops AI tools for code review, said monday.com integrated Qodo into pull requests, where engineers review proposed changes before they are merged.

Qodo analyzes each change alongside the surrounding codebase, monday.com’s engineering practices and previous changes. The AI can flag potential problems, suggest improvements and provide feedback before the code reaches production.

Friedman said Qodo identified an average of 800 potential issues a month before they reached production and reduced review cycles by about one hour per pull request.

“As AI increased the speed and volume of code creation, teams need new approaches to verification, context and governance,” Friedman said.

Faster code creation puts more pressure on the review process. Engineering teams scaling AI-assisted output also need enough capacity to determine whether those changes are ready to ship.

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

Context Window

■ University of Michigan researchers found generative AI can use substantially more energy and often produce less accurate output in non-English languages, with Hindi and Bengali requiring 7.9 and 15.3 times the energy of equivalent English prompts. [University of Michigan]

■ AI policy emerged as a significant issue in the 2026 midterms, with competing groups spending tens of millions of dollars as candidates campaigned on data centers, worker impacts, child safety and AI regulation. [Politico]

■ Some white-collar workers are leaving established careers as AI changes the substance of their jobs and fuels concerns about long-term stability, even as broader economic data has yet to show widespread AI-driven job losses. [The Guardian]

Transfer Protocol

Tracking executive moves across the AI landscape

Sam Meckey, who led WestCX and launched its AI-native customer engagement platform, has been named CEO of FinThrive, where his priorities include applying AI and automation to modernize revenue cycle operations and improve customer outcomes.

Rob T. Lee, former chief AI officer and chief of research at SANS Institute, has moved to Dragos as its first chief AI officer, helping industrial and critical infrastructure organizations adopt and secure AI in operational technology and prepare for AI-enabled threats.

Srinivas Attipalli, after senior leadership roles at ZoomInfo, Allstate, Macy’s and Magellan Health, has joined Definitive Healthcare as senior vice president and head of AI and data, advancing its AI and data capabilities as it prepares to launch a new intelligence platform.

Tejas Totade has taken on an expanded role at Ruder Finn as global chief technology officer and chief data and AI officer, bringing technology, data and AI under one mandate while building the architecture behind the agency’s AI capabilities.

Scott Farnsworth, coming from the chief information officer role at Confluent Health, has joined Upward Health as chief information officer and will steer its technology agenda around AI, data and analytics and digital innovation.

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?

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“I’m spending a lot less time in Outlook these days. I’ve built dashboards with AI agents that monitor my email, and that’s increasingly how I interact with information throughout the day. Once you start building tools around how you actually work, you keep finding other things you want to try.

One project I’ve particularly enjoyed is a research agent I built, then introduced to my development team. We work with pharmaceutical companies and the patient support hubs that help people get onto and stay on therapy. Manufacturers typically see what’s happening through dashboards, reports and quarterly reviews. I wanted to let them ask questions directly.

For example, how many patients stopped therapy after the first month? That answer might prompt three more questions. Instead of asking someone to pull another report, you could keep the conversation going. In the prototype I built, the agent uses a dataset specific to that client and can pull in outside reference information when needed.

The fun part for me was getting back into building something myself, seeing what was possible and then bringing that idea to my development team.

I’ve built healthcare technology for a long time, and a lot of that work involves anticipating what someone will need to know. You build a report or dashboard around those questions. But there’s always a question you didn’t anticipate, and answering it becomes another request for the technical team.

What caught my attention was that someone could ask a question, get an answer and immediately ask the next question without needing another report built. For a manufacturer trying to understand where patients are being held up, the information could be much more useful in day-to-day decisions.

It also reflects what I’m seeing within my own team. There’s a lot of discussion about measuring AI’s value by how many positions you eliminate. Our team hasn’t shrunk. We’re doing more, moving faster and producing higher quality work.

That’s how I think about the value as a CTO. What can we take on now? Which ideas can we actually build? Being able to explore an idea myself and bring something tangible to my team makes that feel very real.”

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

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