Google’s AI features just got more confusing


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The AI Sandbox demo area at Google I/O

Radhika Rajkumar/ZDNET

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ZDNET’s key takeaways

  • Google’s many new AI features span multiple interfaces.
  • Discreet features took the place of overall upgrades to Gemini.
  • Multiple single-function touchpoints could get confusing.

At this point in the AI race, most AI labs have figured out that the real money lies in enterprise use cases: big, agentic features that significantly impact the way major companies move and work. Google is one of those labs, which could explain why many of the company’s more consumer-forward AI features unveiled at I/O on Tuesday feel somewhat … underwhelming.

Also: Google’s new Omni AI tool will let you video clone yourself – I’m intrigued (and concerned)

Not just that, but they’re oddly disparate across the already multilayered Gemini landscape. This I/O in particular was a chance for Google to make everyday AI appeal to users, especially those skeptical of how hard the company is pushing it. While these new features do offer some conveniences, Google has packaged them in ways that could undermine their relevance and usability.

Many separate Lives

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Radhika Rajkumar/ZDNET

Google Workspace got a few new AI capabilities at I/O, the main two of which are Docs Live and Gmail Live. And yes, they’re effectively the same functionality.

Like Gemini Live, which lets you use voice to engage with Gemini, Docs Live and Gmail Live let you query each application via voice. 

In a demo, a Google employee used the former to generate a Google doc on her phone, cobbled together from disparate notes, presentations, and other contextual information she rattled off on the fly. To demo the latter, she asked Gmail questions like, “What’s happening at school this week?” Gmail Live scanned her inbox and responded that there was a field trip to prep for.

Also: I compared how Gemini, ChatGPT, and Claude can analyze videos – this model wins

Both features handled more complex follow-up questions about scheduling conflicts and changed topics seemingly without confusing related terms, like “field trip” and “trip to Detroit.” But these are effectively Gemini Live functionality, applied to new apps. Why give them distinct names and silo them to specific places? Why not simply expand Gemini Live integrations?

None of the Google reps I asked on-site could really answer this. 

It’s an odd approach to making Workspace more accessible with AI when, up until now, Google has pretty much been putting Gemini in everything. Regardless of whether you like it or not, it’s a clean, authority-building approach that gives users a one-stop shop for these subtle but convenient AI perks (that is, when they work). Besides, Gemini in Workspace already outfits Gmail, Docs, and Sheets with various capabilities — why not put these under this umbrella, at least?

Also: Your Android phone is getting agentic powers with Gemini Intelligence – here’s how and when

By splicing out near-identical functions into separate features, Google risks confusing or overwhelming the person it needs to convince: the long-term Google customer who’s hesitant about AI or unsure whether it’s relevant to them. That’s much easier to do by saying Gemini sources information for you from several apps via voice than by naming a laundry list of independent product titles.

Google isn’t alone in this: Microsoft has tacked Copilot onto most of its existing products, leaving users to navigate the differences between Copilot Chat, Microsoft 365 Copilot, and other overlapping terms. OpenAI risked similar confusion when it launched ChatGPT Apps, which are just integrations for ChatGPT with other apps — not a new product or app creation platform like the name could suggest. 

What’s more, Docs Live and Gmail Live are rolling out to AI Pro and Ultra subscribers — who pay $20 and at least $100 per month, respectively — and will be in preview for Workspace business users this summer. It’s unclear whether they’ll eventually trickle down to all users or roll into Gemini itself. 

These are quite niche use cases for standalone launches, let alone ones users will need to pay for. Both appear to be available only on mobile, at least for now, focused on making “on the go” tasks more seamless.

A very brief Daily Brief

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Demo of Daily Brief feature at Google I/O, May 19. 

Radhika Rajkumar/ZDNET

Adapted from an experiment called CC, announced late last year, the new Daily Brief feature sources information from your email, calendar, and other connected apps to give you a rundown of what your day looks like. For those worried about hallucinated meetings, it also includes links to where in your ecosystem it pulled each agenda item from.

I say feature, but Google called Daily Brief “a new agent.” Not to bury us in semantics, but unless I’m missing something, this feature doesn’t strike me as living up to the standard of agent that OpenClaw set this year. 

Also: I tested ChatGPT Plus vs. Gemini Pro to see which is better – and if it’s worth switching

To be fair, Daily Brief does demonstrate some level of deeper reasoning: We haven’t been able to test it ourselves yet, but in two separate demos, it went beyond the present day to flag upcoming charges for later in the week. It also determined that the test user was about to renovate their kitchen, suggesting they set up a temporary cooking station.

But that’s where the “agent” stopped. When I asked whether the feature could take action from any of those items, a Google employee told me a user should just start a chat with Gemini or move into Spark, Google’s new “personal agent” that lives (again, confusingly) inside Gemini.

Not to sound like a brat, but … that’s it?

Also: How Google just revamped Gemini Enterprise for the agentic era – here’s what’s new

A daily briefing is a product of an agent’s summarization work. But is the brief itself an agent? By Google’s definition, I’m not convinced (though hands-on testing may reveal more agentic skills). While reviewing data from your connected apps requires agentic capabilities, Daily Brief is a pretty narrow application of those abilities. 

In the grand scheme of AI expectations, Google is also somewhat late.

“One of the things that OpenClaw is known for is its ability to give you a daily briefing. People also use Claude Cowork for it,” ZDNET contributor David Gewirtz said. “It is certainly not a new capability in the world of AI. People have been talking about daily briefings ever since agents started to become usable.”

Given that context, I have the same question I had about the many Lives: Why not just roll Daily Brief into Gemini as a new (perhaps long-overdue) feature?

AI tools need clarity

Gemini’s various levels of capability are already split across interfaces, separated only by slightly different names. Gemini Intelligence, an Android-specific arm of the agent, launched earlier this month with agentic, multitask capabilities. That’s different from Gemini Personal Intelligence, by the way, which customizes your query responses based on the data you give it access to. 

Then there’s Gemini in Search and the standalone Gemini app.

Also: This powerful Gemini setting made my AI results way more personal and accurate

This setup risks making what Gemini can do so illegible that users simply tune out. Technically speaking, Google might separate each of these Gemini arms based on how they perform across different surfaces: To a developer or product manager, it might make sense to distinguish the Android version of a Gemini product. 

But from a marketing perspective, maybe that doesn’t need to be so visible to the consumer.

Even if AI labs are now following Anthropic’s success and focusing on enterprise, the field of consumer AI tools is still relevant — and crowded. Making it harder for users to keep your products straight probably doesn’t help.





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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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