Asana has launched an AI personal assistant that can track various data sources to alerts users when a work project runs into problems and recommends next actions.

It’s one of a range of product announcements made Thursday at the company’s Work Innovation Summit in London, including updates to its existing AI teammates product. These follow Asana’s recent acquisition of AI workflow automation software vendor StackAI for $75 million.

Asana Dash is described as an “AI chief of staff” that can help users stay up to date on work projects by accessing information in Asana as well as across email, calendar and team messaging apps, said Arnab Bose, Asana’s chief product officer. “Keeping people in their ‘zone of genius’ and hooking up all of these unstructured signals to the structure of Asana — that’s what Dash does best,” said Bose.

The AI assistant can access the same Asana project information as the user, and can flag when problems occur that could push a project off-track. Asana Dash can then act to address problems, such as posting messages within Asana on behalf of the user or directing an AI teammate to take action. (Asana Dash will ask the user before making any changes.)

“Asana is building on recent acquisitions, and earlier investment in a graph database focused on human connections — the Asana Work Graph — and its position within a well-integrated flow of work to deliver to each worker an executive assistant rooted in the context of their job,” said Wayne Kurtzman, IDC research vice president.    

The Asana Dash personal assistant is enabled by an expanded Asana work graph — the data model related to work carried out by teams in the application. Asana has in the past been more focused on tasks, projects, portfolios, and goals, said Bose, but the work graph now includes new sources of data, linking to employee calendars and accessing meeting transcripts, for instance, alongside other documents and databases.

There are also updates to the AI teammates feature — collaborative AI agents that multiple human coworkers can interact with — which are now more powerful, said Bose. This includes additional skills and integrations with third-party apps such as Gmail, Slack, Outlook, Figma, and Canva.

As for the StackAI acquisition, Bose said it allows Asana to extend the reach of AI agents into a variety of business apps more easily and reliably, building ] on Asana’s “system of action” function. The latter tracks work carried out across an organization, he said, and can automate the complex processes that make up many enterprise workflows. 

“If you look at StackAI’s website, the thing that they are really, really great at is building these complex, multi-step processes,” said Bose. The aim is to combine StackAI’s agent builder with integration expertise agents already available in Asana. 

“So, the idea is when an AI teammate or Dash recommends the next best action, they will be able to choose downstream actions based on the portfolio of approved workflows that you’ve built out in StackAI.”

Overall, the announcements help Asana provide a platform that combines agents and workflow automation with AI assistance that aids humans to work more effectively, said Bose.

“Our terminology for this is a ‘human-agent operating system,’ because automation, I feel, is a little reductive in the sense that there are some things that are fully automated, but a lot that you’d want a human being and an AI agent to coordinate on and align on,” he said.

Asana did not immediately respond to a request for pricing and availability details for Asana Dash.

Google has released new tools that allow developers to run agentic AI workflows locally using Gemma 4 12B, a 12-billion-parameter model from Google DeepMind.

In a blog post, the company said the model, combined with the Google AI Edge stack, can be used to build and test applications on everyday machines. The model-runtime combination supports capabilities such as autonomous data processing, visual insight generation, webpage creation, and tool use.

The release includes Google AI Edge Gallery for macOS, where developers can use Gemma 4 12B to generate and run scripts for tasks such as data analysis. Google also said its Eloquent voice dictation and editing app now runs fully on-device on macOS, with support for local transcription and voice-driven text editing.

Google has also expanded LiteRT-LM, its lightweight command-line tool for running language models locally, with a new serve command. The company said this allows the CLI to act as a local LLM server and lets developers connect Gemma 4 12B to standard tools, SDKs, and frameworks through a local endpoint.

“Your data stays on your device while maintaining reliable responsiveness, utility, and cost efficiency,” the company said in the blog post.

The announcement comes as enterprises are looking beyond large, general-purpose models for some AI workloads. Gartner predicted that by 2027, organizations will use small, task-specific AI models at least three times more than general-purpose large language models, citing demand for more contextualized and cost-effective AI systems.

Challenges to overcome

But running agents on employee devices brings a number of problems. Companies must work within the limits of endpoint hardware, which can restrict the size of models that run effectively and the number of model instances that can operate at one time.

“While the AI can now fit on a laptop, enterprise IT infrastructure is largely unprepared to manage it,” said Rishi Padhi, principal analyst at Gartner. “Even highly optimized models like the Gemma 4 12B require around 16GB of unified memory or VRAM to run alongside standard applications. Many standard-issue enterprise laptops lack the memory bandwidth and NPUs/GPUs required for fluid, multi-turn agentic execution.”

Anand Joshi, AI analyst at TechInsights, said local deployment also changes the nature of the workloads. On a PC, search may mean finding information across internal folders and files. In a data center, the same function could involve searching the internet or querying a large database such as SQL.

“The framework for local deployment of agentic AI is different from that of a data center,” Joshi said. “The models are smaller; you can run only one instance of a large model at a time. You are limited by memory, CPU, and so on.”

Security and governance are also likely to become bigger concerns as AI agents move closer to enterprise endpoints. Agentic AI is designed to take actions, creating new security risks when local models are given access to employee files or allowed to interact directly with applications and scripts.

“Sandboxing these agents without breaking their utility is still a major operational challenge,” Padhi added. “And all this while enterprises need to audit AI usage for compliance and security. When inference happens entirely offline, capturing logs, tracking model drift, and ensuring employees are using the approved, compliant ways for a model becomes incredibly difficult.”

The cost tradeoff

Running AI agents locally could reduce some cloud inference costs, but the savings may be offset in the near term by higher spending on endpoint hardware and management.

“First and foremost, it is an OpEx-to-CapEx shift, as it shifts that financial burden by forcing accelerated hardware refresh cycles for premium PCs or edge devices,” Padhi said. “It would require buying expensive, high-memory laptops for employees at a time when memflation in the hardware industry is already driving up end-user average selling prices for laptops.”

Many enterprises refreshed PCs in 2025 to support Windows 11, but at that point, most AI inference still ran in the cloud, and the case for on-device AI remained unclear, Padhi said.

Enterprises may therefore move cautiously, buying AI-capable PCs only where local inference has a clear business case.

Over time, however, on-device AI could make enterprise AI spending more predictable by reducing exposure to variable cloud inference bills. The tradeoff is that companies may face a higher baseline cost for equipping and managing employees’ devices.

Complementing cloud AI

For enterprises, local AI is unlikely to replace cloud-based AI outright. Analysts said local AI is more likely to be used for workloads that benefit from endpoint processing, especially when applications must operate offline or when privacy and response times are critical.

“For local agentic AI to proliferate, the use cases on edge will have to complement data center/cloud use cases,” Joshi said. “I don’t expect local agentic AI to replace cloud AI, but it has potential to take a slice away from the cloud, and models like Gemma are significant steps towards enabling that.”

The market, Joshi added, is still determining where local AI fits best. “I estimate that use cases that require privacy or have strict latency needs will move to local node first, with further migration of others in the next 2-3 years,” he said.

Padhi said model placement will depend on the privacy requirements of a workload, the computing power it needs, and where the relevant data resides. Tasks such as code generation or analysis of local files could increasingly run on employee devices, while enterprise-wide RAG systems and more complex AI workflows are likely to remain cloud-based.

The article originally appeared on InfoWorld.

A report released Wednesday by Boston Consulting Group (BCG) indicates that many organizations are having difficulty converting efficiency gains that are AI-driven into any sort of measurable value.

The fourth edition of the consultancy’s annual Global AI at Work Survey reveals 42% of frontline employees who use AI on a regular basis save upwards of a full day each week; however, 66% are not given guidance on what to do with time they save, and “more than half don’t redirect it to strategic work.”

The report, AI at Work: Strategy Matters More Than Tools, is based on a global survey of 11,749 employees in 14 markets, from industries ranging from financial services to the healthcare sector.

David Martin, global leader of people and organization work at BCG, and the report’s lead author, said via email that the number of employees lacking the required guidance is surprising, “but it also tracks with what we see in many AI transformations. Companies have moved quickly to give people tools, but many have not yet redesigned the work around those tools.”

Saved time, he added, does not automatically become value. If a frontline employee saves a few hours a week, but has no direction on whether to use that time for customer service, quality improvement, innovation, or faster execution, “that value can simply leak out of the organization”

The fix is for leaders to change the scoreboard, Martin said: “Don’t just measure AI adoption or hours saved. Decide where that time should go, measure whether it is being reinvested, and give managers clear guidance on how to help teams use it. This is where AI transformation becomes a management challenge, not just a technology rollout.”

In fact, said Vinciane Beauchene, a managing director and partner at BCG and one of the report’s five co-authors, “the first wave of AI focused on individual productivity. The coming wave will need to transform collective work.”

“Everyone is talking about AI replacing work,” she said, “but it is in fact really about rethinking the human value-add inside.”

A managerial revolution underway

According to Beauchene, “this is the role of leaders. Our survey reveals a true managerial revolution in the age of AI; 65% of managers and leaders now believe agents will take over at least half of their job in the next three years, and frontline workers see their jobs evolving towards more managing and directing AI.”

A BCG release stated that the survey also highlights the continued emergence and maturity of AI agents, with 30% of respondents saying that agents are already integrated into workflows, more than double the number from last year’s report (13%).

Other key findings revealed that AI adoption among frontline workers has surged, with 74% saying they now use it daily or a few times a week, which is up 23 percentage points from a year ago. In addition, six out of 10 people believe that, within the next three years, AI agents could do at least half of their jobs.

And a survey slideshow released by the company pointed out, “the AI ‘honeymoon’ won’t last unless leaders bring strategic clarity driving sustained impact AI’s novelty and cognitive stretch fuel enjoyment early on. But sustained joy comes from strategic clarity. Employees thrive when the direction is real and the message reaches them with strong CEO involvement.”

Strategic clarity is a key differentiator

The report suggests that CEOs take a holistic approach to AI transformations by focusing on business outcomes as opposed to AI usage, investing in “redesigning work end-to-end, not in more tools,” placing people at the heart of that redesign, and governing AI not as a one-off program, but as a moving target.

Overall, BCG says that strategic clarity, “more broadly emerges from the survey as the most crucial differentiator in sustaining AI’s impact over time as organizations are moving past simply implementing AI tools in use case deployment initiatives.”

Increasingly, it adds, “the focus is shifting to redesigning end-to-end workflows and processes to reimagine functions, as well as to building and innovating new business models and products to drive growth, which have nearly doubled year-over-year.”

Global leader of BCG’s tech build and design unit BCG X Sylvain Duranton, also a report co-author, added, “employees don’t push back on AI intensity; they thrive when the strategy is clear, the direction is real, and the message reaches them.”

He added, “Business value and employee enjoyment aren’t trade-offs. The organizations capturing the greatest business value are the same ones where employees enjoy work the most.”

Despite the opportunity, the report notes that only one-third of frontline employees say that leadership’s communications about AI are clear, and only 28% “see a strong connection between what leaders say and what the organization actually does.”

However, Martin said, management can’t deal with this situation on its own. “CIOs have a critical role, but this is not a problem they can solve alone, and I would not frame it as something IT created by itself,” he noted.

Many organizations, he said, “started with the natural first step of getting tools into people’s hands safely and at scale. That was necessary, but it is not sufficient.”

The next phase “has to be much more cross-functional,” he said. “CIOs should help set the technology foundation, governance, data model, and measurement systems, but they also have an important role in creating strategic clarity. Employees need to understand why the organization is using AI, where it is meant to create value, and how it should change the work.”

Martin pointed out that CIOs should also pay close attention to cognitive load, especially on technology teams, as those teams are often the heaviest AI users.

“This means they may be among the most exposed to the mental strain that can come with reviewing outputs, managing AI tools, and keeping up with constant change,” he observed. The biggest gains come when technology strategy, workforce strategy, and employee experience move together. If AI remains only an IT program, companies will “undercapture” the value.

New expectations

The abundance of AI activity is also having another effect, in that 60% of respondents say the bar for work that counts as ‘good enough’ is now higher.

That, said Martin, is because AI is changing expectations. “If a tool can produce a first draft, summarize research, generate options, or automate a routine task, then ‘good enough’ moves up the value chain,” he said. “People are being asked to spend less time producing basic output and more time exercising judgment like checking quality, improving the answer, making decisions, and applying context.”

While that can be a good thing, he said, because it can make work more interesting and more valuable, “it also explains why employees are feeling more mental strain. The work that remains is often more complex. Leaders need to recognize that AI does not just make people faster, it changes what excellence looks like. That means companies need to update training, performance expectations, and management support accordingly.”

This article originally appeared on CIO.com.

Nvidia’s RTX Spark could give PC makers a new high-end category, built around machines that run more demanding AI workloads locally rather than in the cloud.

The chipmaker and Microsoft said RTX Spark Windows PCs will be built for personal AI agents and heavier local AI workloads, from AI development to engineering and content creation.

Nvidia said RTX Spark will offer up to 1 petaflop of AI performance and up to 128GB of unified memory, allowing systems to run 120-billion-parameter large language models locally.

Nvidia has lined up several major PC makers for the launch. The company said RTX Spark laptops and compact desktops will be available this fall from Asus, Dell, HP, Lenovo, Microsoft Surface, and MSI, with models from Acer and Gigabyte to follow. Dell is bringing the platform to its XPS 16 Creator Edition, while HP said upcoming OmniBooks powered by Nvidia will target agentic developers. Microsoft is positioning its Surface Laptop Ultra for creators, developers, and engineers.

Microsoft is also introducing the Surface RTX Spark Dev Box, a compact Windows AI developer PC designed to let developers build and refine models locally before turning to the cloud for larger workloads.

That could create a premium tier above mainstream AI PCs based on Intel, AMD and Qualcomm chips, helping lift average selling prices in a PC market where growth has been uneven. It could also raise questions about whether current AI PCs have enough local computing power for the more ambitious AI workloads that software makers and chip companies are now promoting.

But broad enterprise adoption is not assured. According to Futurum Research, the AI PC market could grow at a compound annual growth rate of about 38% between 2025 and 2030, but adoption is likely to slow in 2026 after a Windows 10 end-of-support-driven refresh cycle and normalize through 2027.

Futurum expects another wave of upgrades around 2028, as systems with higher levels of on-device AI compute become capable of running agentic AI workloads locally, suggesting RTX Spark’s early impact may be felt more in premium and specialist systems than in broad corporate fleets.

Adoption potential

Analysts say RTX Spark’s first test will be whether enterprises treat local AI compute as a workstation requirement rather than a standard laptop feature.

“In the near term, RTX Spark is more likely to be a high-end enterprise AI workstation category than a mass-market PC category,” said Pareekh Jain, CEO of Pareekh Consulting. “Most enterprise users do not need the level of local AI compute that RTX Spark offers.”

Jain said the platform could establish a premium tier between traditional workstations and AI servers, similar to how gaming GPUs created a premium PC segment. Its longer-term significance, he said, may lie less in unit volumes than in whether it becomes a reference architecture for AI-native workstations that can run large models on-device with strong security and low latency.

Prabhu Ram, VP of the industry research group at Cybermedia Research, said RTX Spark adoption would start in niche segments but could expand over the next two to three years if the software vision materializes. Its prospects will depend on post-launch performance, real-world pricing, and early enterprise pilot results, he said.

Ram added that OEM uptake would be the clearest early signal of whether RTX Spark is becoming a real enterprise category rather than a niche workstation product.

Cost and competition

The clearest near-term effect may be at the high end of the PC market, where RTX Spark could give vendors a more powerful class of AI system to sell above mainstream business laptops.

Jain said RTX Spark systems, which he expects to cost more than $2,000, are designed for heavier local AI workloads, including large language models and advanced content creation. By contrast, he said mainstream AI PCs based on Intel, AMD and Qualcomm chips are typically priced below $1,500 and are aimed more at Copilot+, summarization and other office productivity tasks.

That split could raise enterprise PC spending for power users, while making mainstream AI PCs look more like productivity devices, Jain said. Over time, it could increase pressure on Intel, AMD and Qualcomm to add more AI capabilities at lower price points.

But the immediate impact may not be on demand for mainstream PCs based on Qualcomm, Intel or AMD chips, according to Neil Shah, vice president for research and partner at Counterpoint Research. He said the more likely scenario is that RTX Spark may create a new segment that competes more directly with gaming PCs, Apple’s Mac Mini, and higher-end Macs used for on-device AI applications.

Who needs RTX Spark?

Analysts said RTX Spark-class systems are likely to be justified only where running AI locally has clear business value.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the test for enterprises is not whether a workload uses AI, but whether the organization gains by running that AI closer to the user, data, device or operating environment.

“If the work is meeting summaries, drafting, email triage, transcription, translation, search and ordinary assistance, Spark is unnecessary and a mainstream AI PC will do,” Gogia said. “Issuing Spark to every employee for that would be sending a Formula One car to fetch the milk.”

Gogia said likely early users include software developers, AI engineers, data scientists and security teams working with sensitive code, larger models, forensic data or local retrieval pipelines that companies may not want to move into external systems.

The security question could also shape adoption. Nvidia said the platform will rely on new Windows security tools and its OpenShell runtime, allowing companies to set policies for agents while keeping some queries on local models and masking personal data before selected queries are sent to cloud services.

“Nvidia is not only selling endpoint hardware,” Gogia said. “It is installing itself into the endpoint’s runtime, its policy layer and its agent orchestration. The endpoint conversation has quietly expanded from endpoint hardware to endpoint agency, and that is a CISO question long before it is a procurement one.”

Manish Rawat, analyst at TechInsights, said local AI compute could support faster development cycles, stronger privacy and lower cloud inference costs, while enabling workloads such as 12K video editing, simulations, digital twins and edge AI applications.

“CIOs should buy Spark where the workload justifies it, where the governance model supports it, and where the economics hold, and nowhere else,” Gogia added.

Cloud computing has reached a crossroads. The high cost and data sensitivity of AI workloads are raising the appeal of private clouds, even as neoclouds and sovereign clouds shake up the cloud provider landscape. New cyberthreats, shifting compute requirements, and management complexity are adding to cloud complications.

Download the June 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World, and learn how to navigate the latest cloud strategy developments.

Microsoft has developed a new AI agent that can run autonomously around the clock to complete tasks across Microsoft 365 applications.

Microsoft Scout, unveiled at the company’s Build event Tuesday, is a new type of always-on agent based on the OpenClaw agent framework that Microsoft calls “autopilots.”

These act on a user’s behalf with their own governed Entra identity, Omar Shahine, corporate vice president at Microsoft, said in a blog post.

“Autopilots stay active in the background, understand how work gets done across your apps and systems, and take action without needing to be prompted each time,” said Shahine, a Microsoft veteran who recently announced he is leading a new team to bring OpenClaw-based personal assistants to Microsoft 365 apps.

Microsoft Scout connects to apps such as Teams, Outlook, OneDrive, and SharePoint, and accesses data from chat, email, calendar, and contacts. Accessed via Teams, it can also interact with a user’s browser and with external apps via model context protocol (MCP). The tool functions across cloud, desktop, and the web.

Shahine said Scout can reduce mundane tasks that office workers face, such as coordinating and scheduling meeting times with colleagues, or blocking times in a user’s calendar based on upcoming work commitments. “It can also spot risks, like stalled decisions, so you can address them before they become blockers,” he said.

It’s available as an “experimental release” to customers of the company’s Frontier program, Microsoft said, and will require Intune policy configuration and “opt-in attestation.”

Scout is the latest in a range of agentic tools available in Microsoft 365 apps, including Agent Mode, where users can interact with Microsoft 365 Copilot inside apps such as Word and Excel to create content, and Copilot Cowork — Microsoft’s version of Anthropic’s Claude Cowork agent that can complete tasks independently.

Despite the company’s big AI push, Microsoft has struggled to convince businesses that Microsoft 365 Copilot is worth the additional cost; it’s advertised at $30 per user each month for large businesses. Around 3% of Microsoft 365 customers pay for the add-on subscription, the company said in January, with 15 million paid users. (Microsoft announced last month that that figure has now risen to 20 million.)

It’s not clear whether Scout will be included in Microsoft 365 Copilot subscriptions or charged separately. Microsoft did not immediately provide additional details about pricing.

The launch follows Google’s recent announcement of Spark, an autonomous agent that runs within the Google Workspace application suite. Spark can also be considered a response to the launch of OpenClaw last year, initially under the name “Clawdbot.”

OpenClaw has drawn scrutiny due to apparent security flaws, but Microsoft promises Scout is built with “enterprise-grade security and controls, so it can be trusted in your organization from day one.”

For organizations that have already deployed Microsoft 365 Copilot, Scout doesn’t introduce entirely new data risks, said Jeff Pollard, vice president and principal analyst at Forrester. But it “amplifies whatever data governance problems already exist. The difference this time: instead of surfacing sensitive data to users, it can potentially act on it.

“That makes it an active risk in terms of day to day operations,” Pollard said.

Potential security concerns echo those for AI agents and are exacerbated with personal agents such as Scout: amplified data exposure (since agents can interact with data and use tools autonomously); agent manipulation or prompt injection; unexpected actions, such as using tools or acting in ways that aren’t supposed to be allowed; and observability gaps related to understanding user and agent intent and the explainability of actions.

“However, these tools exist because they make AI far more useful for individuals, so security leaders can’t draw a line in the sand and say “no.” They have to adapt and figure out how to secure them,” said Pollard.

As with most new workplace technologies, Pollard expects adoption to start with “power users” who design and develop the use cases for the agent that can then expand more widely across users.

He warned that the accuracy of tools such as Microsoft Scout can fall short of user expectations. “LLM agents still struggle with goal alignment, multi-step reasoning drifts, and tool misuse,” he said. “Users aren’t always great at explaining what they want and LLM agents aren’t always great at providing what was requested. That’s a continuing problem.”