SUCCESS

Skan AI, a startup that builds what it calls a “context graph of work” by observing how employees actually perform their jobs across enterprise software, has raised $63 million in Series C funding co-led by Cathay Innovation and Dell Technologies Capital, the company announced Wednesday.Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures also participated in the round, which brings the seven-year-old company’s total funding to roughly $120 million. Alongside the raise, Skan is announcing the general availability of two new products — Skan AI Blueprint and Skan AI Agents — that, together with its existing Skan AI Intelligence offering, form a complete platform for discovering, modeling, and ultimately automating enterprise workflows.The announcement lands at a moment of deep frustration in enterprise AI. Companies have poured billions into generative AI pilots, but the results have been dismal: Gartner research cited by the company finds that only 8% of enterprises have AI agents in production, and 95% of early implementations will require a complete redesign. Those figures echo an MIT report last year, covered by Fortune, which found that roughly 95% of enterprise generative AI pilots were failing to deliver measurable returns.Avinash Misra, Skan’s co-founder and CEO, believes the industry has misdiagnosed the problem. The models are fine, he argues. What they lack is an accurate picture of the businesses they are being dropped into.”Everyone is obsessed with building a better driver,” Misra told VentureBeat in an exclusive interview ahead of the announcement. “We think the bigger opportunity is building a better navigation system.”Why enterprise AI agents keep failing when they rely on official process documentationThe standard playbook for grounding AI agents — feeding them process documentation, standard operating procedures, and system logs — is built on a fiction, Misra argues. The way work is documented and the way work actually happens inside a large enterprise are two different things, and the gap between them is precisely where agents fail.That gap is what sent Misra and co-founder Manish Garg down this path seven years ago, long before agents were a boardroom obsession. “Why is it so difficult for an organization, and a large enterprise especially, to understand how its own work actually gets done?” Misra said. “Why does it need to fly in McKinsey consultants for that?”The question has only grown more consequential as enterprises race to operationalize AI. Frontier models arrive at the company door brilliant but blind, with no knowledge of the exceptions, decisions, handoffs, and institutional habits that define how a claims department or a compliance team actually operates. Every company now stuffing agents with documentation and logs, Skan contends, is discovering the same uncomfortable truth: the source data was never the whole story. And a source data problem cannot be fixed downstream.Skan’s answer is to go to the source itself. The company deploys observation technology on employee desktops that continuously watches how work moves across applications — the spreadsheet, the CRM, the email client, the 40-year-old mainframe — and abstracts those observations into a living model of the underlying business process.”Think of it this way: if I were to share my screen here, and you were to observe my screen going from Excel sheet, CRM system, email client, in about two iterations you’d build a model of what I do,” Misra said. “Except you couldn’t do that at scale. You couldn’t do it 24/7, and for 1,500 people like me. Now replace yourself with our technology.”How screen-level observation captures the work that never shows up in system logsThat framing also explains how Skan positions itself against process mining vendors like Celonis, which reconstruct workflows from the data trails left in backend systems. System logs, Misra argues, only capture completed transactions — not the messy human work that produced them.”All backend data, by definition, is a committed state of work. Work is really what happens between those committed states,” he said. “Eighty percent of what you’re interested in, from an AI point of view, in execution of work, actually lies between those systems.”The screen, in Skan’s view, is the one place where everything converges. “It brings together human agency, it brings together the entire application landscape, and it brings together the data that matters,” Misra said. Two decades of user interface design have quietly buried enormous amounts of process knowledge in the space between a worker’s eyes and their monitor; Skan’s pitch is to bring that hidden layer back to the surface.But watching, he insists, was never the hard part — a point aimed squarely at the incumbents who might be tempted to copy the approach. “The hard problem is not screen observation,” Misra said. “The hard problem is abstraction of what you see on the screen — the intent extraction.” A human watching a colleague’s screen can instantly tell whether a jump back to step one means a new case or rework on an old one, because humans understand the signature of the work. Teaching a model to make that same judgment, statefully and at enterprise scale, is where Skan believes its seven-year head start lives.The result is a context model that AI can reason over and act on — the raw material for the agents that now sit at the top of the company’s product stack, and the foundation for everything else the platform does.Walking the line between operational telemetry and workplace surveillanceAn approach built on continuously watching employee screens invites an obvious objection, and it is not a hypothetical one. In June, Reuters reported that Meta scaled back an internal tool that tracked employee mouse clicks after workers raised concerns — a sign that even AI-forward companies are wary of the line between operational telemetry and surveillance.Misra says he heard the objection before he wrote a line of code. When he first pitched the concept to Delphine Icart, then chief transformation officer at AXA Mexico, her reaction was blunt. “Delphine’s first words to me were, ‘This sounds like a great idea, but you are dead on arrival,'” Misra recalled. “‘You are observing things that you shouldn’t be observing — the privacy of my operators, and the sovereignty of my data on those screens.'”That conversation, he says, shaped the architecture. Skan aggregates rather than individuates: the system surfaces statistical patterns across hundreds of workers performing the same process, not the behavior of any one of them. “We’re not interested in what John is doing at 10 hours and 43 seconds,” Misra said. “We are interested in what hundreds of Johns put together — what are the statistical and the semantic decisions that they are making in that business process?”Organizations control what the technology can see through an opt-in scoping model — specific applications and URLs, nothing else — and the data Skan produces never leaves the enterprise firewall. A three-tier architecture sends only anonymized metadata to the cloud. Misra points to deployments approved by European works councils, among the most privacy-protective labor bodies in the world, as evidence the model holds up under scrutiny — and credits it for clearing security review at institutions where most AI tools cannot operate.Whether aggregation fully defuses the concern is likely to remain contested. The same telemetry that reveals a broken process can, in principle, reveal an underperforming team, and Misra acknowledged that the technology has led some customers to reduce headcount in certain processes.What $500 million in claimed customer value actually measuresSkan claims more than $500 million in cumulative customer value to date, a figure worth unpacking. Pressed on whether that represents realized savings or projections, Misra was direct that it is an envelope, not a bank balance.”The number comes from the cumulative, across all our customers, of the quantified savings that we have brought to them — the savings that they have expected they would save,” he said. “Now they are on the roadmap of recouping those savings through a variety of interventions,” including process redesign, technology changes, and, increasingly, AI agents. In other words, $500 million is identified opportunity, some portion of which has been captured.The more concrete evidence comes from individual deployments. At one top U.S. bank, according to the company, Skan observed 11.2 million context switches across 1,500 finance professionals and uncovered $37 million in operational friction. Turning those observations into agent-executable context cut cost per transaction by 32%, lifted throughput by 41%, and delivered $18 million in annualized savings.Misra pointed to an anti-money-laundering operation at one bank where “60% of the cases are now being run by AI agents,” adding that the results surprised even him: “The accuracy of those agents surpasses many times over the accuracy of humans. It’s not just an argument of efficiency; it has also become an argument of quality.” Among insurers, he said, Skan typically delivers roughly 25% productivity uplift in core claims processes; one customer doubled its case volume over the past year without adding a single claims specialist.Skan’s publicly referenceable customers include Unum, the $13.8 billion employee benefits provider, and Mitie, the U.K. facilities management company, whose chief technology and digital officer, Cijo Joseph, said Skan’s technology “gives us unprecedented operational visibility that has dramatically accelerated our AI transformation.” The company declined to share revenue but said it grew more than 300% year over year — for the second consecutive year — with net dollar retention around 150%, and now counts seven of the ten largest U.S. banks and a quarter of the Fortune 50 as customers.Can AI models learn good work from imperfect employees?Skan’s thesis rests on observing how work actually gets done — which raises an uncomfortable question. Real employees make mistakes, take shortcuts, and entrench inefficiencies. What happens when the context graph faithfully encodes bad process?Misra’s answer reaches for the most famous precedent in modern AI. “Think for a moment what OpenAI did,” he said. “OpenAI took the totality of the world’s text and fed it into a transformer architecture, and semantic understanding emerged. OpenAI’s model has seen bad language and has seen good language, and yet it is able to have semantic understanding.”Skan, he argues, does the analogous thing with work: treat business process execution as a language, where process steps, screen features, and handoffs stand in for words and sentences. Fed enough end-to-end executions, the model learns the full distribution of paths — efficient ones, slow ones, compliant ones — without assuming any single path is best. “The longest path may be the best path, because it is more compliant,” Misra said. An organization then constrains the model along the axes it cares about, and the model returns the path that satisfies them.”It is not record and play — and that’s the fundamental difference between us and a lot of our competition, UiPath and so on,” he said. “It is fundamentally creating an AI model that understands work, and then constraining that model.”He offered a concrete illustration of what that unlocks: at one large bank, Skan’s telemetry continuously compares live case execution against a 600-page controls inventory, with agents that trigger alerts when cases miss required compliance steps — turning a document no human could hold in their head into a real-time enforcement layer. It is the kind of application that only becomes possible, Misra argues, once a model genuinely understands the work rather than merely replaying it.The race to own the context layer of enterprise AISkan sits at the intersection of several crowded categories, and its answer to each competitor is a variation on the same theme: scope. Process mining vendors see only what the logs record. RPA incumbents replay tasks without understanding them. And the platform giants — ServiceNow, Salesforce, Microsoft — are shipping capable agents whose vision ends at their own walls.”The context that these agents have access to is limited to ServiceNow, limited to Salesforce, whereas work spans processes across the board,” Misra said. “Creating a customer entry is a task. To receive an email and decide whether a customer entry has to be created, or something else — that is the process, and that’s what we are after.”The deeper strategic argument, and the one that seems to resonate with Skan’s regulated customer base, is about differentiation in a world where every enterprise has access to the same frontier models. “If every insurance company, every bank had access to the same models, then the outcomes will asymptotically decay to the outcome of the model,” Misra said. “Historically, you have competed and differentiated in the way you have organized work. That old word — process — now comes back as context for AI. But that context is protected by you. It’s not part of the model.”That logic explains both the company’s posture toward the model makers — “the more they are successful, the more power we have,” Misra said, disclaiming any ambition to compete with them — and the Nvidia partnership featured prominently in the announcement. Skan runs on Nvidia AI Enterprise and NIM microservices, and Misra described growing demand for private appliances that can observe work, hold the context model, and execute agents entirely inside a customer’s own infrastructure. It also fits the market’s direction: venture investors surveyed by TechCrunch at the end of last year predicted enterprises would spend more on AI in 2026 but through fewer vendors — a consolidation that favors Skan’s decision to ship discovery, intelligence, and agents as a single closed loop.Misra argues that loop matters more, not less, as automation scales, because agents demand oversight in a way humans never did. “It is an irony of sorts,” he said, “that you’ll probably need much more observation and much more understanding of work in an automated way than you would with humans.” The bet embedded in this round is that work context becomes foundational infrastructure for enterprise AI the way CRM became the system of record for customers — a comparison Cathay Innovation partner Simon Wu made explicitly, calling Skan “one of the defining platform companies of the next decade.”Misra put the stakes more simply. “You cannot retrieve context that you do not capture,” he said. “The battleground is shifting from the smartest model to knowing how your company actually works — because everyone will have access to the smartest model.”The frontier labs, in other words, can keep their arms race for the better driver. Skan just raised $63 million on the conviction that the money is in the map.

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Your day-ahead look for Aug. 12, 2026

TheStreet aims to feature only the best products and services. If you buy something via one of our links, we may earn a commission.Why we love this dealIt’s one thing to deal with the hot humidity outside during the summer, but it’s a whole other beast to deal with it indoors. You can set your own home to the perfect temperature that keeps you cool and comfortable, but what about areas where you don’t have control of the thermostat, or worse, you don’t have a thermostat or air conditioner to adjust at all? Hot, sticky, and stagnant air can get in the way of working in an office, studying at the library, cooking in the kitchen, or tackling an outdoor gardening project underneath the hot sun, but we can’t always take a break to properly cool off. That’s why small additions to our day-to-day life like the Sweetfull Personal Desktop Fan can make all the difference in how productive we are and make an overly warm environment more enjoyable for the overheater, especially when the weather outside is doing us no favors.The $20 Sweetfull Personal Desktop Fan is on sale for just $12 as part of a Walmart Flash deal, and its petite size but impressive power make it a must-have accessory for your home or on the go. Take advantage of such an incredible price and pick one up for everyone in your family, and see how easy it is to stay cool and dry when the surrounding heat gets to be a bit too much. Sweetfull Personal Desktop Fan, $12 (was $20) at Walmart

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Shop at WalmartWhy do shoppers love it?Keeping cool doesn’t just feel good. During extremely warm weather periods, it protects from heat stroke, helps your brain stay focused, and even improves your sleep at night. When you aren’t sweating as much, you’re less likely to become dehydrated, your mood is more mellow, and your heart doesn’t have to work as hard to cool your body down. When you’re outside, some of these problems are inevitable at least for the period you’re enjoying the outdoors, but inside, we wouldn’t want anyone to be plagued by heat issues, and with this desktop fan you don’t have to. Designed for travel and portability, this fan, which measures just 5.67 inches by 2.60 inches by 6.06 inches, is petite but has some pretty great fan power. With seven curved blades and a brushless motor — which makes it highly efficient and contributes to its long lifespan — this fan is powered with the help of a 4.9-foot USB cord, meaning it can run while plugged into a power bank, a laptop, a car charger, or a USB charger and electrical outlet.Known for operating at very quiet noise levels as low as 25 decibels, it has four adjustable wind speeds to suit your different needs. Opt for the lower 2.8 m/s option for a gentle breeze or crank it all the way up to 5.2 m/s for a more powerful gust of wind. Not only can it provide 55% more airflow to an area, but it makes about 30% less noise than other similar models. It has a 360-degree adjustable head for specific directional cooling to hit set areas.Related: Amazon’s portable turbo mini fan that ‘fits easily in a pocket’ is just $5 with 71% offIt is a freestanding device so it requires no mount or set up to use, and it has a detachable rear cover for easy cleaning.Details to knowDimensions: The fan measures 5.67 inches by 2.60 inches by 6.06 inches. Weight: 0.61 pounds.Colors: Two. Fan speeds: Four. Powered by: USB-C cable. You can plug it into a charging block, a power bank, a laptop, or a car charger.Shoppers really love that the USB cord gives you a variety of ways to power the device. It’s super convenient for on the go use, especially in situations where you don’t have access to a direct electrical outlet. Compact and sleek, this “small but mighty” fan is “powerful and does a good job of cooling in a small space, on a desk or a counter,” one shopper noted. “I like the size of it and it doesn’t vibrate my desk or make a lot of noise,” another shopper said. Shop more deals Woozoo 5-Speed Oscillating Globe Fan, $60 (was $79) at WalmartDreo 93-Foot Pedestal Fan, $90 (was $150) at WalmartArctic Air Freedom Touch Wearable Personal Air Cooler, $23 (was $30) at WalmartThe Sweetfull Personal Desktop Fan is the small, compact, and portable solution to staying cool wherever you go. 

Greg Abel took over as Berkshire Hathaway’s chief executive on January 1, 2026, inheriting a cash position unlike anything in the conglomerate’s six-decade history. Berkshire’s cash and short-term Treasury holdings climbed to a record $397.4 billion by the end of March, up from $373 billion at year-end 2025.Berkshire’s stock was up about 3% year-to-date entering August, compared with a roughly 13% advance for the S&P 500 over that stretch, according to market data.The mood around Berkshire reflects what analysts have called a “show me” posture, with shareholders waiting for sustained proof that Abel can deploy capital effectively, Yahoo Finance reported. What comes next with the remaining reserves, and the pace at which Abel acts, will shape how this leadership transition is judged.Abel’s early deals signal a selective deployment paceAbel’s most visible move was a $6.8 billion cash acquisition of homebuilder Taylor Morrison, announced on May 31 and completed on July 24. Berkshire paid $72.50 a share, a 24% premium, and plans to fold the builder into Clayton Properties Group, the site-built homebuilding arm of its Clayton Homes subsidiary.Berkshire also participated in a $10 billion private placement into Alphabet in June. Buffett said in a July 15 CNBC interview that he initiated the deal, though he noted Abel had final sign-off. “He is the decider,” Buffett said of Abel. The total Alphabet stake has since grown to roughly $31 billion, though Buffett noted that Burlington Northern “is certainly worth far more money.”Bill Stone, chief investment officer at Glenview Trust and a longtime Berkshire shareholder, said the Taylor Morrison deal has a clear long-term thesis. The deal reflects a bet that the housing cycle will turn and that pent-up demand will eventually be released, Stone told CNBC in June.Christopher Davis of Hudson Value Partners said Abel’s plan to unify Berkshire’s homebuilding operations marks a departure from its hands-off tradition, Fortune reported.Berkshire’s operating businesses deliver in Abel’s first quarterFirst quarter results gave Abel a solid operational foundation to build from, with several of Berkshire’s key business units outperforming the prior year. Operating earnings rose to $11.35 billion, an increase of about 18% compared with the same three months in 2025, Business Wire reported.More Berkshire Hathaway:Buffett’s successor Greg Abel doubles down on AI stockWarren Buffett’s successor Greg Abel makes another $10 billion betWarren Buffett’s Berkshire Hathaway lands major housing dealInsurance underwriting produced $1.72 billion in profit, up roughly 28% from $1.34 billion a year earlier, supported by fewer catastrophe-related losses in the period.The float that funds Berkshire’s investments, essentially premiums collected before claims are paid, reached about $176.9 billion by the end of March, the company’s first quarter earnings showed.BNSF Railway contributed $1.38 billion, up from $1.21 billion, and Berkshire Hathaway Energy added $1.11 billion, a slight improvement from the prior year. Total revenue across the company rose about 4.5% to $93.7 billion, and costs grew at a slower pace than earnings, leaving room for margins to expand.At the May 2 annual shareholder meeting, Buffett praised Abel and told the audience that “Greg is doing everything I did and then some,” Forbes reported.The praise was significant, but Berkshire’s continued lag behind the S&P 500 this year shows that full investor confidence remains out of reach.

Berkshire Hathaway’s operating businesses posted stronger first-quarter results, giving Greg Abel a solid foundation as Buffett’s successor.Cheng Xin / Getty Images

Abel pivots Berkshire toward operational improvementAt the same annual meeting, Abel outlined a vision focused on execution and productivity at the operating level rather than Buffett’s capital-allocation-first approach. He told shareholders that Berkshire intends to build technology internally rather than simply buy it, referencing predictive maintenance programs at BNSF Railway.Steven Check, president of Check Capital Management and a longtime Berkshire shareholder, told Reuters that Abel’s June deal activity showed he is moving beyond Buffett’s influence and starting to establish his own leadership identity.Everyone has been waiting for Greg to do his thing, beyond Warren Buffett’s shadow, and we’re now seeing thatStone wrote in the Forbes analysis that Abel positioned operational excellence as a distinct value-creation lever, one that grows more important as scale limits returns. Abel also shared quantitative comparisons of subsidiary performance, noting that BNSF had ranked fifth among six major railroads in profitability but rose to fourth.That degree of transparency was unusual for a company built on trust and autonomy, and it signals a new culture of operational accountability at Berkshire.What Berkshire’s leadership transition means for long-term holdersCheck told Reuters that investors had been waiting for Abel to establish his identity. The June deals suggest that process has begun, though the scale of remaining reserves means the market’s verdict on Abel’s leadership is far from final.For long-term holders, the central question is whether Abel’s deployment pace can match the opportunity cost of keeping nearly $400 billion in short-term government securities. With the stock trading at about 1.5 times book value, the margin for error on future capital decisions is narrower than in Berkshire’s earlier decades.The August 8 Q2 report offered the first sign of a shift, with Abel deploying $4.5 billion in buybacks, 19 times the Q1 pace. Whether that becomes a pattern is the question every long-term holder is now watching.Related: Berkshire Hathaway’s earnings paint misleading picture

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