ENTERPRISE

Enterprise AI’s ROI problem is its metrics

Though enterprises are eager to deploy AI, returns are still eluding many of them. 

The Wharton School of the University of Pennsylvania has published its fourth annual report on the state of AI since the launch of ChatGPT, with the findings becoming  one of the most closely watched bellwethers in the industry. Now, the report turns to the question everyone is asking: who is winning with AI, and what is their secret?

The survey, created in collaboration with market research and analytics firm GBK Collective, surveyed 1,416 US enterprise leaders and entry to mid-level employees, finding that  there is a widening gap between organizations that simply implement AI and those who are seeing more meaningful transformations. The three markers of AI success it identified are proving value, transforming workflows, and aligning the organization around AI. However, only 7% of organizations surveyed succeed across all three metrics. 

"Companies are now moving into the harder phase: integrating AI into how work gets done, learning new skills, changing processes, reorganizing departments. It’s like changing the engine while the car is running," Brian Smith, executive vice president of the AI, cloud and enterprise technology practice at GBK, told The Deep View. "That is hard work, and a lot less exciting than the early experimenting."

The report calls this year the fourth "wave" of AI, where the tech  has been embedded in the core of enterprise operations, with 62% using the tech daily, 91% using it multiple times a week, and 73% reporting significantly changed workflows as a result. 

Yet, the report identifies major flaws in adoption, including how companies are actually measuring ROI. 

• A large portion of respondents, around 40%, reported judging return on investment by hours saved. Net-new revenue was the least utilized metric for success, with 27% reporting that they track this. 

• However, the departments tracking returns by more  than time saved are doing better comparatively: 35% of departments that track at least one growth measure report strongly positive returns, against just 13% of those tracking efficiency alone. 

• Only 22% of managers look to AI to grow the business, whether through new revenue, new products, or better customer experience. Meanwhile, more than half are focused on improving productivity or cutting costs. 

One way companies can implement more meaningful deployments of AI include having a designated Chief AI Officer position: 58% of managers with a newly designated CAIO report strongly positive ROI. Additionally, as enterprises lean into AI efficiency over tokenmaxxing,  in tracking AI spend correlates with higher reported return.

Perhaps the most meaningful part of this report is seeing how the trajectory has changed over the past years. It allocates the first two "waves" of AI  as the years 2023 to 2024 as exploration, marked by trial, error and experimentation by early adopters. Then, by wave three, which was 2025, regular usage of AI became more deeply integrated. Now, in wave four, as the report states that "adoption is no longer the question." Understanding this business mindset and why so many enterprises are willing to invest so much money so quickly also helps understand why the AI labs have redirected their attention to working professionals, creating two forces that are largely driving the AI market. 

TOGETHER WITH NOOKS

Sales is harder than code. Nooks Labs is working on it.

Coding agents get feedback in seconds: the tests pass or they fail. Nooks learned that sales agents can wait weeks to find out whether a move worked. Much of what matters, like who's really making the decision, never makes it into the CRM.

Nooks' agents scored 97% on the company's own eval set, but reps still double-checked every output. So Nooks Labs, the company's applied AI team, built a harder benchmark from real deal data. The team then fixed what it exposed with calibrated confidence scores, a way to test a strategy against past deals before it runs, and 500+ examples of bad AI writing used as guardrails.

Today, 96% of agent-written emails go out without edits. The open question now: what does it take to go from 2x productivity gains to 10x?

Discover the research →

RESEARCH

Why Microsoft is chasing smarter AI scaling

Microsoft is making its way back into the frontier model race. 

On Thursday, the company's AI research lab announced it had completed pre-training on MAI-Base-2, a mixture-of-experts foundation model with 2.8 trillion parameters and 80 billion active. Additionally, the model outperforms a number of open-weight models on several evaluations, and is far more efficient than the most powerful open model it was tested against, DeepSeek-V4-Pro-Base. 

The company tested the model on "held out" evaluations, meaning it was tested on a subset of data that was excluded from the training process, in subjects such as coding, math, STEM, and question answering.

• Though it didn't test against models from OpenAI, Anthropic, Google or other frontier labs, MAI-Base-2 beat out trillion-plus parameter models from Alibaba, DeepSeek, Moonshot, and Xiaomi in these metrics. 

• As for efficiency, the company said its model is four to 12 times more "Pre-training FLOPs-efficient" than DeepSeek's model, meaning that it used far less raw computation for internal training to achieve similar performance, largely as a result of better training data and changes to model architecture. Microsoft said it directed this cost savings into a larger model. 

"Owning our model pipeline end-to-end allows Microsoft to make a distinctive offering to our customers," Microsoft said in its research blog. "Microsoft has strong data provenance over training tokens that go into our model. Our pre-training is built on organic data which does not involve distillation from other models." 

Though Microsoft's AI strategy has differed from competitors like OpenAI, Anthropic and Google, according to Business Insider, Microsoft AI Chief Mustafa Suleyman told employees in a town hall on Thursday that the company's unnamed in-house "pretrained base model" performed better than a OpenAI's GPT 5.6 Sol on private testing, and used "one-quarter of the compute" to achieve that result.

Microsoft has taken a unique strategy in the AI race compared to its frontier lab competitors. Rather than diverting tons of resources and energy to fighting for more powerful AI and more attention against major model providers, it's taken a more conservative approach, focusing on safety, security, and efficiency over brute-force scaling. It's a prudent strategy that's evident in the essays from both Suleyman and Microsoft CEO Satya Nadella, calling for caution around things like model observability and AI consciousness. It's also evident in the company's prior model releases themselves, which are focused largely on cost and resource efficiency that pass savings down to the enterprises that use them. MAI-Base-2, while certainly closer to a frontier model in size than those it's released in recent months, is another example of the company putting efficiency first, and highlights that Microsoft has its finger on the pulse of the things that are most important to its enterprise customer base: Security and cost savings. 

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ENTERPRISE

6 timeless attributes for succeeding in the AI era

AI is making it easier for employees to work beyond their specialties and that has big implications for the future organizations of all sizes. 

In this episode of The Deep View Conversations, we sit down with Bill Barton, chief operating officer at Rokt and the former leader of the Alexa team at Amazon. Rokt has gotten called out by some of the biggest tech companies in the world for its innovative use of AI inside the company. In fact, several of them have had Rokt come and talk to their teams about how they do it. So we wanted to have Bill on the show to share those insights with a larger audience.

Barton shares how Rokt is using an internal AI fleet across software development, finance, legal, security and other functions. He explains how AI is helping blur the boundaries between roles, why the company has moved from more than a dozen job ladders to four, and what it takes to turn experimentation into everyday practice.

Other topics covered:

• Why Bill returned to Rokt after trying retirement
• How AI is expanding the reach of generalists
• Enabling shared AI experimentation companywide
• Why fewer, more flexible systems can help teams move faster
• How Barton thinks about productivity gains and hiring
• What Google search reveals about frictionless AI
• Why more powerful tools can make it harder to step away from work

Bill also shares his six timeless attributes that can help anyone succeed in the AI era: relentlessness, resilience, rigorousness, inquisitiveness, optimism and humility.

For leaders rethinking their teams and professionals navigating changing roles, this conversation connects the possibilities of AI with the habits and judgment needed to make them useful.

📺 Watch on YouTube

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Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: https://tdv.transistor.fm/

LINKS

• Anthropic commits a portion of shares to Public Benefit Fund

• Disney hires more than 60 Character.ai employees, pays $180 million 

• Taiwan to criminalize export of AI servers, advanced chips to China

• China proposes export controls on AI models, chip designs

• OpenAI agent raided unowned wallet after depleting Ethereum funds

• Rogue AI agents breached live Indian systems, CERT-In says

• Super Intelligence Force to address cyber, bio, AI accountability risks

• GPT-Rosalind: OpenAI launches new Rosalind Academic Plan

• Flux 3 Image Klein: Black Forest Labs' open weights with quality of a large frontier model

• Chrome tabs: ChatGPT can now use your tabs in Chrome to make answers more useful

• GPT-Live-1.1: New voices. Better accents. And less "checking..."

• Stott and May: AI Engineer-Agentic Workflow

• Surge Group: Artificial Intelligence Engineer

• The Phoenix Group: AI Specialist

• Walden Robotics: AI Engineer

GAMES

POLL RESULTS

Do you feel comfortable using an always-on AI device or wearable?

Yes (16%)
Somewhat (20%)
No (63%)
Other (1%)