Users in the United States can now ask Google Search to make real-world phone calls for them, gathering service and pricing information from local businesses without speaking to anyone themselves.

AI Used in Local Enquiries

Google has rolled out a new AI-powered calling feature within Search that allows users to collect information from businesses such as pet groomers, garages, and dental clinics. Instead of having to make a phone call personally, users can now instruct Google’s AI to handle the enquiry on their behalf.

UK Soon?

The feature is currently available to all Search users in the United States. It’s worth noting here that although Google has not provided a confirmed timeline for the UK launch of this feature, based on the company’s typical rollout strategy for Search and Gemini features, a wider international release often follows within several months of a successful US launch.

How To Use It

Using the new AI-powered agentic calling feature, when someone searches for a service like “dog groomers near me”, a new option appears offering to “Have AI check pricing”. Users are then asked a few follow-up questions, such as what type of pet they have, what service they need, and when they would prefer an appointment. From there, Google’s AI makes the call, gathers information, and returns a summary by email or text.

According to Google, every call begins with a clear announcement that it is an automated system from Google acting on behalf of a user. This is intended to prevent confusion and maintain transparency, especially after earlier versions of the technology were criticised for sounding too human and failing to identify themselves clearly.

How the Technology Works

In terms of the tech behind it, the feature uses a combination of Google’s Gemini model and its existing Duplex technology, which has been used for AI voice calls since 2018. Duplex originally drew attention for its ability to make bookings or ask for opening hours using natural-sounding speech, but was temporarily scaled back due to concerns about transparency and practical usage limits.

However, this new version is more focused and practical, targeting specific types of local businesses and providing structured information directly back to the user. The use of Gemini helps the system handle follow-up questions and summarise results more clearly, while Duplex provides the voice interface that handles the actual phone call.

Google has stated that business owners retain control and can opt out of receiving these calls via their Google Business Profile settings.

Access, Availability, and Cost

The AI calling feature is free to use and is currently being made available to all Search users in the US. However, those subscribed to Google’s AI Pro and AI Ultra plans will benefit from higher usage limits, allowing them to make more AI-driven requests each day.

Google AI Pro is priced at 19.99 US dollars per month. Subscribers gain access not only to enhanced call limits but also to a broader set of advanced AI features across other Google products, including Docs, Gmail, and Search.

There is no confirmed launch date for international availability, but Google has indicated that it plans to expand access globally over time.

Convenient

This feature may appeal most to people who prefer not to make calls themselves. For example, younger users in particular have shown in surveys that they are more likely to avoid phone conversations where possible. For many, the ability to compare availability and pricing from several providers without needing to speak to anyone may be seen as a welcome convenience.

For example, someone looking for car servicing could quickly receive quotes from three nearby garages with minimal effort. The AI not only makes the call but ensures the response is presented clearly and directly.

Mixed Impact For Businesses

For businesses, however, the impact may be more mixed. For example, while the system could generate new leads, it also adds a layer of automation that some business owners may find disruptive or unfamiliar. Staff answering the phone must be prepared to speak with an automated caller and provide information in a way that can be understood and relayed accurately.

Part of a Bigger Transformation in Search

Google’s introduction of AI calling is really part of a wider evolution of Search towards more agentic, action-oriented tools. For example, at the same time as launching this calling feature, the company also announced the rollout of two other significant updates for users on its AI Pro and AI Ultra subscription tiers, i.e. Gemini 2.5 Pro in AI Mode, and a new Deep Search capability designed for complex research tasks.

Gemini 2.5 Pro Comes to AI Mode

AI Mode is Google’s conversational interface in Search that allows users to pose complex or multi-part questions and receive structured answers with helpful links. Until now, it used a version of Gemini based on the 1.5 model. However, with the new rollout, paying subscribers can now switch to Gemini 2.5 Pro, a more advanced model that performs better in coding, mathematics, and advanced reasoning.

Users can select Gemini 2.5 Pro from a drop-down menu within AI Mode. The new model offers clearer logic, better problem-solving abilities, and more precise answers. Google says it is especially helpful for users tackling more technical tasks, such as software development or quantitative research.

Deep Search Adds Multi-Step Research Capabilities

Also new is Deep Search, a feature designed to save users hours of research by allowing the AI to run hundreds of background searches and reason across different sources. The result is a fully cited and structured report that addresses a query in depth.

Google says Deep Search is useful for work-related research, hobbies, academic study, or life decisions such as evaluating mortgages or comparing investment options. Rather than manually visiting multiple websites and comparing answers, users receive a compiled response that includes context, sources, and suggestions.

This feature is currently available to AI Pro and AI Ultra subscribers in the United States who have opted into Google’s AI Mode experiments in Labs. It builds on the trend of shifting from traditional search queries towards more autonomous AI assistance.

Impressive Tools with Practical Considerations

The new agentic features represent a major change in how people interact with information online. Instead of simply retrieving answers, Google’s AI now takes action on the user’s behalf, whether by conducting research or placing real-world phone calls.

However, the effectiveness of these tools will depend on adoption and reliability. If local businesses do not respond well to AI calls, or if the information returned is inconsistent, the user experience could suffer. Similarly, the shift towards subscription-based access raises concerns about accessibility, especially if more functionality becomes limited to paying users.

Even so, the direction is clear. Google is continuing to reshape Search into a more proactive and intelligent assistant, with features that aim to remove friction from both digital and real-world tasks. As the company put it in its announcement, “We’re bringing some of our most cutting-edge AI features to Google AI Pro and AI Ultra subscribers first, and we look forward to continuing to bring advanced capabilities in Search to all our users globally.”

What Does This Mean For Your Business?

Google is clearly moving Search from a place to find answers to a tool that completes tasks. Features like AI-powered calling change how users interact with businesses, removing the need for phone conversations altogether in some cases. If rolled out in the UK, this could directly affect how service providers handle enquiries, especially in sectors like grooming, repairs, and healthcare. Businesses that respond promptly and provide accurate, up-to-date information through their Google listings will be better placed to benefit. Those that fail to do so may find themselves left out of automated selection entirely.

For subscribers, the introduction of Gemini 2.5 Pro and Deep Search adds a new layer of functionality to Search. These tools are designed to deliver more complete, structured answers and reduce the time spent piecing together information manually. That is likely to appeal to professionals, researchers, and anyone dealing with complex decisions. However, the decision to reserve the most powerful features for paying users raises questions about who gets access to high-quality AI support and who does not. It may also increase pressure on non-paying users to upgrade, particularly if the standard tools begin to feel limited by comparison.

As these capabilities continue to expand, they are likely to influence how people expect digital services to behave. For UK businesses, the priority will be staying visible and responsive within this new model. For users, the benefits will depend on how well the tools perform across a range of everyday tasks, and how widely they are made available.

Co-op’s chief executive has confirmed that Hackers stole the personal data of all 6.5 million Co-op members in an April cyberattack.

The breach exposed names, addresses, and contact details, but no financial data. Co-op says it shut down its systems just in time to block a ransomware attack, though the incident still caused widespread disruption.

CEO Shirine Khoury-Haq called the attack “devastating” and praised IT staff for acting swiftly. The group behind the attack is believed to be ‘Scattered Spider’, a known cybercrime gang that uses social engineering to access internal systems.

Four suspects aged 17 to 20 were arrested and bailed earlier this month in connection with the attacks on Co-op and other UK retailers.

In response, Co-op has partnered with The Hacking Games to help guide young cyber talent into ethical careers, starting with a pilot across its academy schools.

To reduce risk, businesses should train staff to recognise impersonation tactics, restrict internal access, and ensure systems can be swiftly isolated in the event of an attack.

As electric vehicle usage expands, the race is on to recycle their batteries and recover scarce materials like lithium, nickel and cobalt, thereby cutting emissions and easing dependence on mining.

A New Phase In the EV Revolution

With global sales of electric vehicles (EVs) topping 14 million in 2023, a 35 per cent increase on the previous year, attention is now turning to what happens when those vehicles reach the end of their life. The answer lies within a growing industry focused on recycling EV batteries and recovering the valuable metals they contain.

According to the International Energy Agency (IEA), more than 40 million EVs were on the roads worldwide by the end of 2023. Yet while these vehicles eliminate tailpipe emissions, their batteries pose new sustainability challenges. The raw materials used to make them, especially lithium, cobalt, nickel and graphite, are limited, unevenly distributed around the world, and often extracted in environmentally and socially problematic conditions.

This is where EV battery recycling comes in: a critical step not just for sustainability but for securing supply chains, lowering costs, and reducing dependence on virgin mineral extraction.

Why Recycle EV Batteries?

Each lithium-ion EV battery contains a tightly packed structure of anodes and cathodes, typically made from graphite and a mix of lithium, cobalt and nickel. Over time, these batteries degrade and are eventually removed from vehicles. If not properly handled, they risk leaking toxic materials into the environment or catching fire during disposal.

More importantly, without recycling, the valuable critical minerals they contain would be lost. These metals are expensive to mine and refine, and demand is expected to grow rapidly. For example, the World Bank projects that by 2050, global demand for lithium could increase by nearly 500 per cent, with cobalt and nickel not far behind.

Recycling essentially offers a more sustainable and secure alternative. By 2040, up to 50 per cent of the UK’s EV battery material demand could be met through recycling, according to estimates shared by Altilium Clean Technology, a UK-based battery recycling firm. As Dr Christian Marston, Altilium’s COO, puts it: “If we do battery recycling at scale, we can produce materials at around 20% lower cost than commercial imports—and with significantly lower emissions.”

A Circular Model Made in Britain

Altilium is an example of a company at the forefront of this movement. Based in Tavistock, Devon, with new large-scale facilities under development near Plymouth, the company has created a fully integrated recycling process that turns old EV batteries into battery-ready materials.

The heart of its process is EcoCathode™, which is a hydrometallurgical method that uses water-based chemistry instead of high-emission smelting. Here’s a brief summary of how it works:

Step 1 – Shredding

Spent EV batteries are mechanically shredded into a fine, dark powder known as “black mass”. This material contains a mix of critical metals, plastics and other by-products.

Step 2 – Acid Leaching

The black mass is soaked in a sulphuric acid solution. This dissolves the key metals (lithium, nickel, and cobalt) into a liquid form, separating them from inert or less valuable materials.

Step 3 – Graphite Recovery

Before further processing, the graphite from the anode is extracted and purified. Altilium reports a 99 per cent recovery rate for graphite, which is then reused in new anodes.

Step 4 – Metal Separation

Using a series of chemical tweaks, unwanted elements like aluminium and copper are filtered out. The remaining solution contains the valuable metals needed for new batteries.

Step 5 – Solvent Extraction

The lithium, nickel and cobalt are separated out one by one using an advanced chemical process involving kerosene and selective reagents. This allows for high-purity recovery of each element.

Step 6 – Reprocessing for Reuse

Finally, the extracted metals are refined into cathode active materials (CAM) and precursor materials, which can be fed directly back into battery production. This closes the loop by turning waste back into high-value battery inputs. The company claims this method produces 74 per cent less carbon emissions for CAM and 77 per cent less for anode materials compared to traditional sourcing. Their recycled components are already being tested at scale by the UK Battery Industrialisation Centre, with a major car manufacturer due to validate performance later this year.

“Closed-Loop” Supply Chain

Altilium’s aim is to create a “closed-loop” supply chain within the UK, keeping resources onshore, reducing dependence on foreign imports, and supporting national energy security. “We see batteries which are in this country as a strategic asset in the UK,” says Marston. “If you do the processing in the UK, you add the value in the UK.”

Who Else Is in the Race?

Although Altilium is leading in the UK, it’s certainly not alone globally. Several firms are now building out battery recycling ecosystems, each with different models and geographic strengths. These include, for example:

– Redwood Materials, founded by Tesla co-creator JB Straubel, is a major US player with sites in Nevada and South Carolina. The company focuses on recovering and refining materials like lithium, cobalt and nickel, and has established partnerships with Toyota, VW, and BMW. Redwood’s strategy is to build a full circular supply chain, reducing US dependence on imported minerals.

– Li-Cycle, based in Canada but operating facilities across North America and Germany, also uses hydrometallurgical recycling. The company reports recovery rates of up to 95% for key materials, and is working closely with US policymakers through funding support from the 2022 Inflation Reduction Act.

– Ecobat, the world’s largest battery recycler, which is pivoting from its traditional lead-acid battery work towards lithium-ion recycling. With a strong global logistics and collection network, Ecobat has been expanding its lithium battery services across Europe and the US. According to its website, the company is focused on achieving “closed-loop recycling rates” for lithium comparable to those already achieved for lead.

How Most EV Battery Recycling Technology Works

Most battery recycling processes follow similar core steps, i.e., collection, dismantling, shredding into black mass, and then separation and refinement of metals.
Older approaches like pyrometallurgy, which uses high temperatures to melt down batteries, are effective but extremely energy intensive and carbon heavy. They also tend to destroy some of the more delicate materials, such as graphite.

Newer techniques like hydrometallurgy, used by Altilium, Li-Cycle, and Redwood, rely on water-based chemical treatments. These enable much more precise separation of metals at lower temperatures, resulting in higher recovery rates and far lower emissions.

Altilium’s process, for example, uses sulphuric acid to soak the black mass, selectively precipitating out low-value metals like iron and copper before extracting more valuable cobalt, nickel and lithium. The graphite is recovered earlier in the process and reprocessed for reuse in new battery anodes. The resulting materials are refined to battery-grade purity and can be used to manufacture brand new battery cells.

Environmental and Economic Benefits

The sustainability case for battery recycling is pretty compelling. For example, according to a 2024 IEA report, recycling critical minerals could cut the need for new mining by up to 40 per cent by 2050. For the automotive industry, that means fewer emissions from extraction, processing, and transport, and less exposure to volatile global commodity markets.

Cost savings are also significant. Altilium estimates that its recycled CAM could be 20 per cent cheaper than virgin equivalents by 2035. This could reduce the overall cost of manufacturing a new EV by 5 per cent, which is quite a meaningful margin in a competitive industry where affordability remains a barrier to adoption.

Also, the environmental gains go far beyond carbon. For example, avoiding new mining means less disruption to ecosystems, fewer human rights violations, and a reduced geopolitical dependency on regions like the Democratic Republic of Congo (which currently supplies two-thirds of the world’s cobalt) or Indonesia (the top source of nickel).

Not Without Its Challenges

Despite the promise, the battery recycling industry is obviously still in its early industrial phase. As Dr Xiaochu Wei of Imperial College London points out, many firms have only recently begun scaling beyond pilot stages. Altilium’s own journey, from a modest lab in 2022 to a full-scale facility in 2025, illustrates both the speed and complexity involved.
Battery designs themselves are also a barrier. With so many chemistries in use, from lithium-iron-phosphate (LFP) to nickel-manganese-cobalt (NMC), recycling methods must be flexible enough to handle mixed inputs. Standardising designs or redesigning batteries to be easier to dismantle could help but will require coordination across manufacturers.

Greenwashing Risk

It should be noted here that there’s also the risk of greenwashing. For example, as competition intensifies, some firms may make sustainability claims that outpace their actual recovery rates or environmental impact. Regulation can help: the EU’s new Battery Regulation, due to phase in from 2025, will require specific thresholds for material recovery and recycled content in new batteries.

What Does This Mean For Your Organisation?

Recycling EV batteries at scale is starting to look not just possible but inevitable. With regulations tightening, supply chains under pressure, and emissions targets looming, the case for a circular battery economy is becoming hard to ignore. Companies like Altilium are showing that high recovery rates and lower-carbon processes can be achieved using homegrown innovation. If they succeed in scaling up production and maintaining performance, the UK could have a credible, strategic alternative to importing expensive critical minerals from volatile markets.

For UK businesses, particularly in the automotive and clean tech sectors, this opens the door to a more resilient supply chain with greater control over cost and compliance. Manufacturers looking to meet EU recycled content rules from 2025 onwards will need trusted partners, and local recyclers could offer both regulatory support and operational savings. There are also commercial advantages to be gained from marketing genuinely low-carbon products built with verified recycled inputs, which will only become more valuable as sustainability reporting requirements evolve.

The wider benefits stretch further still. Reducing the UK’s reliance on overseas mining reduces exposure to supply disruptions, ethical concerns and carbon-heavy logistics. It also supports the domestic energy transition with onshore capabilities that align with national goals on net zero and industrial growth. For stakeholders across the board, from EV manufacturers and policymakers to investors and consumers, the expansion of battery recycling signals a maturing ecosystem with real potential to deliver on sustainability promises, rather than just headline targets.

None of this, though, will be straightforward. The sector still faces infrastructure gaps, chemistry complexity and the challenge of building scale fast enough to match the rise in end-of-life EVs. However, if early leaders can maintain momentum, the next few years may see battery recycling move from pilot to pillar, a new industrial sector supporting cleaner transport, better economics and lower environmental impact all at once.

If you’d like to know how quickly your website downloads and how it renders for people and other technical information which can help your SEO, this video explains how to access a handy tool which is completely free to use.

[Note – To Watch This Video without glitches/interruptions, It may be best to download it first]

Want to trust what you see? WhatsApp now lets you check the origin of an image by searching it on Google without ever leaving the app.

How to:

– Tap the image in chat.
– Tap the ⋯ menu and select ‘Search on web’ (or ‘Search image with Google’).
– The image is sent (with consent) to Google and results open in your browser.

What it’s for:

Helps prevent sharing misleading or manipulated images — perfect for vetting news, verifying content, and avoiding misinformation in work or group discussions.

Pro‑Tip: Use this on images sent in group chats or forwards before trusting or sharing them further.

New research has found that 70 per cent of AI agents struggle to complete standard office tasks successfully, while Gartner warns that over 40 per cent of current agentic AI projects will be scrapped by the end of 2027.

What Are ‘AI Agents’ And Why Are They Struggling?

AI agents are software systems that use large language models (LLMs), like ChatGPT or Claude, in combination with tools and applications to carry out goal-driven tasks without constant human input. Unlike chatbots or virtual assistants that only provide responses, agentic AI is designed to take actions, such as navigating software, interacting with web content, or managing emails, based on natural language instructions.

Examples include agents that can generate reports, schedule meetings, or execute multi-step operations such as processing CRM queries or managing code deployments. The idea behind them is that AI can behave like a semi-autonomous digital worker, thereby improving speed and efficiency while reducing costs. However, recent evidence suggests the reality falls far short of the promise.

For example, in a landmark study by researchers at Carnegie Mellon University (CMU), most of today’s leading AI agents were only able to complete around 30–35 per cent of assigned office tasks. That means they failed nearly 70 per cent of the time.

Testing Real-World Tasks

To evaluate how AI agents perform in realistic workplace scenarios, the CMU team created TheAgentCompany, a simulated IT company environment designed to mimic tasks that real employees might encounter. These included browsing the web, writing and editing code, interpreting spreadsheets, drafting performance reviews, and messaging colleagues on internal comms tools like RocketChat.

Results Not Good

Researchers tested agents based on how many tasks they could complete fully and accurately. Top-scoring models included Gemini 2.5 Pro, which managed a 30.3 per cent success rate, and Claude 3.7 Sonnet, which achieved 26.3 per cent. Other well-known models fared worse. GPT-4o completed just 8.6 per cent of tasks, while some large-scale models like Amazon-Nova-Pro and Qwen-2 scored under 2 per cent.

Variation and Serious Slip-Ups

“We find in experiments that the best-performing model…was able to autonomously perform 30.3 per cent of the provided tests to completion,” the CMU team noted. Even with extra credit for partial progress, most agents still fell short of reliable performance.

Also, it looks as though the failures weren’t just minor slip-ups. For example, in some cases, agents forgot to message colleagues, froze while interacting with pop-ups, or even faked task completion, such as renaming users to make it seem like they’d contacted the correct person.

Salesforce’s Findings Echo the Concerns

A separate study by Salesforce offered similarly sobering results. In their CRM-focused benchmark CRMArena-Pro, LLM agents completed about 58 per cent of simple, single-turn customer service tasks. However, in multi-step scenarios where context had to be maintained, success rates dropped sharply to around 35 per cent. None of the evaluated agents demonstrated any meaningful understanding of confidentiality—an essential requirement for deployment in enterprise settings.

The researchers concluded: “LLM agents are generally not well-equipped with many of the skills essential for complex work tasks.”

Over 40 Per Cent of Projects Will Be Cancelled by 2027 …

Industry analysts at Gartner believe this isn’t just a technical hiccup, but could be an indicator of wider strategic risk. For example, the firm predicts that more than 40 per cent of all agentic AI projects will actually be cancelled by the end of 2027. Their assessment is based on the three key drivers of spiralling costs, unclear business value, and inadequate risk controls.

“Most agentic AI projects right now are early-stage experiments or proofs of concept that are mostly driven by hype and are often misapplied,” said Anushree Verma, Senior Director Analyst at Gartner. “This can blind organisations to the real cost and complexity of deploying AI agents at scale.”

A January 2025 Gartner poll of more than 3,400 business respondents revealed that while 19 per cent had already made significant investments in agentic AI, another 42 per cent were only dipping a toe in. Around a third were still waiting to see how the technology matures before committing.

What’s Going Wrong?

A key issue appears to be the fact that many supposed “AI agents” aren’t really agentic at all. For example, Gartner has criticised the growing trend of ‘agent washing’, where vendors rebrand chatbots, rule-based automation tools, or even basic assistants as ‘agents’ to ride the hype wave. Of the thousands of companies claiming to offer agentic AI products, Gartner estimates that only around 130 genuinely qualify.

Even for the legitimate players, it seems that technical challenges abound. For example, CMU’s team highlighted the following major limitations:

– Common-sense reasoning failures. AI agents often misinterpret basic instructions or misunderstand context. This limits their ability to carry out even straightforward workplace tasks.

– Poor tool integration. Many agents struggle to operate reliably within software interfaces. They may freeze, click the wrong buttons, or fail to retrieve the right data.

– Fabricated outputs. Hallucination remains a major problem. Agents sometimes invent plausible-sounding but incorrect responses. Among developers, 75 per cent report experiencing hallucinated functions or APIs.

– High cost and inefficiency. Despite being pitched as labour-saving, one study estimated that a typical AI agent task involved around 30 steps and cost over $6, which is often more than it would take a person to do manually.

– Security and privacy risks. Because agents need wide-ranging system permissions, there’s a serious risk they could accidentally expose sensitive data, or act unpredictably in ways that breach confidentiality.

Complexity and Context

While some agent frameworks are improving, it seems that the wider problem is that many office tasks require not just automation, but judgement. For example, Graham Neubig, a co-author of the CMU paper, explained that while coding agents can be sandboxed to limit risk, office agents must interact with live systems, sensitive messages, and human colleagues.

“It’s very easy to sandbox code…whereas, if an agent is processing emails on your company email server…it could send the email to the wrong people,” Neubig warned.

There’s also the issue of persistence. Multi-step tasks require agents to keep track of state, adapt based on outcomes, and respond to dynamic inputs. Even advanced models struggle to maintain context and consistency across more than a handful of steps, particularly when unexpected events, e.g. like a pop-up, error message, or missing file, intervene.

Buyers, and the Enterprise

For AI companies, the research findings appear to cast doubt on the maturity of the agentic AI market. Those selling genuine solutions will need to demonstrate clear, auditable performance, while others may face a credibility backlash if their products are exposed as agent-washed rebrands.

For enterprise buyers, the message is to proceed with caution. Agentic AI holds promise, but only for very specific use cases where outputs can be clearly defined, risks are manageable, and success is measurable. Without that, projects risk becoming costly distractions that never reach production.

Gartner suggests that businesses focus on agentic AI investments only where it can deliver proven ROI, e.g. by automating decisions, not just tasks, or by redesigning workflows to be agent-friendly from the ground up. “It’s about driving business value through cost, quality, speed and scale,” Verma explained.

Even so, Gartner remains optimistic that the agentic AI landscape will improve. By 2028, they predict that 15 per cent of all daily work decisions will be made autonomously by AI agents, up from none in 2024. They also expect 33 per cent of enterprise software applications to include agentic AI functionality by that time, suggesting that while short-term challenges are real, the long-term potential may still emerge.

What Does This Mean For Your Business?

The current hype around AI agents may be loud, but the reality behind the scenes appears to be proving far messier. Recent research shows that these systems still struggle with many of the core qualities needed for effective office automation, e.g., context awareness, reliability, consistency, and trust. While some agents show promise in structured environments like coding or CRM workflows, real-world office tasks often involve ambiguity, judgement, and unexpected challenges that most agents today simply can’t handle. This mismatch between marketing and capability is already fuelling disillusionment across the enterprise tech landscape.

For UK businesses, this could mean adopting a much more measured approach. For example, rather than rushing into large-scale AI rollouts, organisations may want to carefully assess whether agentic tools truly solve the problem at hand, and whether those benefits outweigh the risks and complexity. In industries where security, compliance, or client confidentiality are vital, agents that behave unpredictably or hallucinate outputs could introduce significant operational or reputational risk. Decision-makers will need to ask hard questions about vendor claims, demand transparency around performance, and avoid falling for superficial rebrands.

Also, for AI developers and solution providers, the pressure is now mounting to deliver genuine value and technical maturity. As Gartner’s forecast suggests, many agentic AI projects may be scrapped before they ever reach deployment. Rising costs, patchy results, and lack of clarity about return on investment are already stalling momentum. Yet amid this shakeout, there remains opportunity. Businesses still want tools that save time, reduce admin overhead, and support hybrid teams. If AI agents can evolve into reliable, well-integrated assistants that are grounded in workflows that make sense for users, they may yet become part of the fabric of enterprise software.

Until then, the safest path forward appears to be to treat agents as experimental copilots, not replacements. Hybrid approaches that combine AI capabilities with human oversight are likely to produce the most stable and trustworthy results. For now, it seems that the goal shouldn’t be full autonomy, but augmentation that helps people work smarter, and doesn’t automate them out of the loop.