As I’m sitting in my office writing this review, delicious, cheesy, garlicky scents are wafting up the stairs. I can hear whizzing and whirring, and the occasional clunk, as a robot chef in my kitchen is making macaroni and cheese. Its app tells me there are three minutes left in the process, and based on the snapshot it’s showing, the dish looks like a creamy pile of cheesy goodness.
Artificial Intelligence
This $1,500 robot cooks dinner while I work
I’ll be heading out the door shortly to pick up my daughter from the school bus, and when we’re back, the robot-cooked mac and cheese will be waiting for her to dive into, staying fresh thanks to a “copilot” mode that keeps it warm and stirs it occasionally until we’re ready.
Meet Posha, my latest foray into the fascinating world of smart kitchen gadgets. Posha is a $1,500 countertop cooking appliance with a $15 monthly subscription that uses AI computer vision, a robotic stirring arm, and automated food and spice dispensers to autonomously cook a meal from start to finish.
It’s an absurd luxury, too dependent on the internet, and feels like a first-gen device in many ways. But it’s also a really good cook, saved me hours of standing over a hot stove, and is a glimpse into the future of home robots in the kitchen.
It took me less than five minutes to load the mac and cheese ingredients into Posha, and the robot handled the rest: sauteing some garlic, pouring in the milk, flinging in the pasta, filling it up with water to cook the pasta, then adding the cheese and stirring it all into a thick, gooey mass.
The result was that, even during my 10-hour workday, I could still offer my daughter a tasty home-cooked meal at 4:30PM, when she got back from school. The alternative in a similar time frame would be a hastily microwaved box of processed mac and cheese. The Posha meal tasted much better.
This is the whole idea behind Posha: to help working families put freshly cooked meals on the table every day without spending a lot of time doing it. As any working parent will tell you, eating well and having enough time to eat well can be a real challenge.
Posha founder Raghav Gupta grew up in India, where he says he saw love expressed through food and witnessed friends and family struggle to choose between careers and providing home-cooked meals to their families. That struggle is global, and entire appliance categories and businesses have been developed to solve it.
To children of the 1980s, the microwave meal was one solution. For today’s kids, the meal kit delivery service may loom large in their memories of home-cooked meals. In the ’80s, microwaves were expensive; meal kits can cost thousands of dollars a year. With that context, a $1,500 robot chef doesn’t seem quite so absurd.
Posha is designed to mimic the way a human chef cooks. It can cook almost anything that fits in a single pot, and the company has devised methods for making dishes you might not associate with one-pot cooking — such as chicken wings and roasted veggies — within the constraints of its form factor.
It’s a similar concept to other modern cooking robots, the Thermomix and the Instant Pot. The main difference is that Posha is totally autonomous once you’ve added the ingredients, whereas many dishes in the Thermomix and Instant Pot require ongoing intervention.
About the size of a large countertop microwave, the Posha is at heart a 1,800-watt induction cooktop with a robotic arm and a camera. It autonomously stirs, heats, and times each step of cooking the dish in a proprietary pan while the camera watches the food and analyzes its color, texture, and consistency, adapting the cooking as you might if you sense the sauce is too dry or the onions are not translucent enough.
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You have to prep the ingredients, including chopping, weighing, and placing them into one of the four plastic containers, which slot into designated holders from where they will be flung into the pot at the appropriate time. A motorized spice tray rotates special pods to release the right amount of seasoning at the right time, and oil and water dispensers do the same for those liquids. The robotic arm features three swappable spatulas intended for different tasks.
It took complicated and time-consuming dishes and turned out restaurant-quality cuisine with minimal effort on my part
You choose a recipe from the embedded touchscreen, which also includes all the device’s controls, load the ingredients (all at once or prep as you go), and come back 30 minutes to an hour later to a fully cooked dish.
Watching it work is fairly mesmerizing; it’s methodical, and the process is similar to how a human cooks. It generally starts with the seasonings, then adds the protein, followed by the other ingredients and a sauce if required. Its skills at sauteing chicken were impressive.
A dish does tend to take a bit longer than I would if I were making it myself; my guess is because it’s constantly analyzing the food and adapting as it goes. But as I don’t have to stand there and watch it (you can keep an eye on it through a companion mobile app if you want), that didn’t bother me.
The company sold out of its first batch of production units, which it started shipping in January 2025. It’s currently working through a preorder waitlist that costs $25 to join. The full retail price is $1,750, but preorders get it for $1,500.
I know what you’re thinking. The prepping and the cleanup are often the worst bits. The actual cooking is the fun part. I agree, and was deeply skeptical about the device when I first saw it demoed at the Smart Kitchen Summit in 2024. But after three months of using it, I’m a convert.
I cook most evenings, and even though I enjoy it, seven nights a week wears thin. I regularly revert to takeout or a Trader Joe’s freezer meal special when I’m just too tired. But since we’ve had the Posha, my takeout bill has dropped, and my time spent with my husband and hanging with my kids has increased.
With Posha, I can prepare a meal in five to 20 minutes, and spend the hour or so I would have spent cooking doing something else. It’s been a huge time-saver. While a slow cooker or Instant Pot operates similarly, Posha’s stovetop method produces more varied results. My husband is also a fan, lauding every dish we’ve tried as “excellent and flavorful.” When I went away on a business trip for a week this fall, he whipped up a few meals himself and was impressed by how easy it was to use.
It’s an absurd luxury. But it’s also a really good cook and a glimpse into the future of home kitchen robots.
Posha has also helped me avoid cooking disasters, like burning the garlic while multitasking, and expanded my weeknight repertoire. Like most families, we get in a rut, and I generally make the same few dishes every week. Doing something new requires a fair amount of effort, but Posha has made it easier. Browsing its recipe library in its companion app and adding a few new ingredients to my shopping list is simple, and consequently, we’ve tried a lot of new dishes, in particular curries.
One key to Posha’s success is that it is infinitely more patient than I am. I’ve tried making curry before, but it rarely turned out great. Posha knows how to layer flavors properly, adding the right amount of spices at the right time, cooking the proteins perfectly, and the sauces with precision — skills that can take years to learn.
Almost every dish I’ve cooked in it has been delicious and full of flavor. From butter chicken and paneer curry to chicken risotto and shakshuka, it took what can be complicated and time-consuming dishes and turned out restaurant-quality cuisine with minimal effort on my part.
This is not to say it’s perfect. First, there’s a fair amount of cleaning with the Posha — you have to wash each container, the pot, and the spatula after every cook, though everything except the arm can go in the dishwasher. The device itself can be wiped down, but there are many nooks and crannies.
While there are 1,000 recipes, diversity is slim. The founders are Indian, and the Posha’s selection of Indian cuisine is vast and delicious. There’s also a healthy selection of Italian, but far fewer options under the American, Chinese, and Thai categories.
While I have loved much of what I’ve tried, I’m not quite ready to shift my family’s menu to predominantly Indian, and there is only so much pasta we want to eat. My husband likes red meat, and there aren’t many options there, mostly ground beef dishes like tacos and chili. There’s no option to have it sear a steak or a slab of salmon; everything has to be diced before going into the containers.
Because it’s limited to one-pot meals, I wouldn’t use it for every dinner. Sometimes, you want a meat and two veg, or a good salad. But I’ve found myself using the Posha at least three or four times a week — that’s a lot for a cooking gadget. It can also scramble eggs and make a mean frittata, so I’ve found it handy for weekend breakfasts. When I’m making a bigger meal, I’ve used it for side dishes like bok choy stir fry and green bean casserole.
It is also huge. While it fits under a standard cabinet, it takes up a lot of countertop space. By comparison, the Thermomix is a similarly expensive cooking robot that can do much of what the Posha does, and more (such as steaming, blending, and chopping), in a smaller footprint.
In a head-to-head, I would favor the Thermomix for its versatility and less obnoxious size. But the Posha is easier to use, being almost entirely hands-off during cooking, and does a better job with more complex recipes.
The software is also an issue. While the app and interface are good, I don’t like kitchen gadgets controlled solely by a touchscreen; I want the option of physical knobs. I had a couple of times when I was all prepped and couldn’t start a cooking session — once because of a Wi-Fi/software update issue, and once because the touchscreen was just unresponsive.
I am also not a fan of being asked to rate every dish after it’s completed. And if you select fewer than four stars, you have to type an explanation. Then you’ll get an email with suggestions on what you could do better next time. No thanks.
Worse, the Posha is dependent on Wi-Fi. You can’t start cooking a recipe without an internet connection. Gupta says this is because the recipes and cooking process rely on a cloud-based AI model, though if the internet drops while it’s cooking, Posha falls back to a smaller, local model that is robust enough to finish the recipe. And he says that should the company ever go out of business, it will push a software update to unlock the hardware. “We will never let a Posha become a paperweight.”
The Posha is dependent on Wi-Fi. You can’t start cooking a recipe without an internet connection.
Then there’s that $15-a-month subscription. This helps fund the team of chefs who create the recipes and respond to custom recipe requests through the app. While not unusual for the connected cooking world (Thermomix charges $65 a year for access to its 100,000 guided cooking recipes), it’s still steep. Without the sub, you can use 50 free recipes and the copilot mode (which lets you manually set the arm speed and cooktop heat), but it’s not the same experience.
Posha is undoubtedly a niche device, but for families or households that struggle to find time to prepare fresh meals (and have some disposable income), Posha delivers on its promise of autonomous cooking. I enjoyed having it in my kitchen — not because it’s flashy, but because it reliably gave me my time back while producing excellent meals. I wish it had physical controls and a lower price tag, and wasn’t dependent on a subscription, a cloud connection, or the long-term stability of a startup. But as a glimpse into the future of having our kitchens cook for us, it’s impressive. It’s also a fairly good sign that the future may be closer than I thought.
Photos and video by Jennifer Pattison Tuohy / The Verge
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Artificial Intelligence
Ronnie Sheth, CEO, SENEN Group: Why now is the time for enterprise AI to ‘get practical'
Before you set sail on your AI journey, always check the state of your data – because if there is one thing likely to sink your ship, it is data quality.
Gartner estimates that poor data quality costs organisations an average of $12.9 million each year in wasted resources and lost opportunities. That’s the bad news. The good news is that organisations are increasingly understanding the importance of their data quality – and less likely to fall into this trap.
That’s the view of Ronnie Sheth, CEO of AI strategy, execution and governance firm SENEN Group. The company focuses on data and AI advisory, operationalisation and literacy, and Sheth notes she has been in the data and AI space ‘ever since [she] was a corporate baby’, so there is plenty of real-world experience behind the viewpoint. There is also plenty of success; Sheth notes that her company has a 99.99% client repeat rate.
“If I were to be very practical, the one thing I’ve noticed is companies jump into adopting AI before they’re ready,” says Sheth. Companies, she notes, will have an executive direction insisting they adopt AI, but without a blueprint or roadmap to accompany it. The result may be impressive user numbers, but with no measurable outcome to back anything up.
Even as recently as 2024, Sheth saw many organisations struggling because their data was ‘nowhere where it needed to be.’ “Not even close,” she adds. Now, the conversation has turned more practical and strategic. Companies are realising this, and coming to SENEN Group initially to get help with their data, rather than wanting to adopt AI immediately.
“When companies like that come to us, the first course of order is really fixing their data,” says Sheth. “The next course of order is getting to their AI model. They are building a strong foundation for any AI initiative that comes after that.
“Once they fix their data, they can build as many AI models as they want, and they can have as many AI solutions as they want, and they will get accurate outputs because now they have a strong foundation,” Sheth adds.
With breadth and depth in expertise, SENEN Group allows organisations to right their course. Sheth notes the example of one customer who came to them wanting a data governance initiative. Ultimately, it was the data strategy which was needed – the why and how, the outcomes of what they were trying to do with their data – before adding in governance and providing a roadmap for an operating model. “They’ve moved from raw data to descriptive analytics, moving into predictive analytics, and now we’re actually setting up an AI strategy for them,” says Sheth.
It is this attitude and requirement for practical initiatives which will be the cornerstone of Sheth’s discussion at AI & Big Data Expo Global in London this week. “Now would be the time to get practical with AI, especially enterprise AI adoption, and not think about ‘look, we’re going to innovate, we’re going to do pilots, we’re going to experiment,’” says Sheth. “Now is not the time to do that. Now is the time to get practical, to get AI to value. This is the year to do that in the enterprise.”
Watch the full video conversation with Ronnie Sheth below:
Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour.
The “build it and they will come” model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide deployments because initial financial modelling ignored the realities of production scaling.
“When we integrate FinOps capabilities with automation, we’re looking at a change from being very reactive on cost management to being very proactive around value engineering,” says Holmes.
This shifts the assessment criteria for technical leaders. Rather than waiting “months or years to assess whether things are getting value,” engineering teams can track resource consumption – such as cost per transaction or API call – “straight from the beginning.”
The unit economics of scaling intelligent automation
Innovation projects face a high mortality rate. Holmes notes that around 80 percent of new innovation projects fail, often because financial opacity during the pilot phase masks future liabilities.
“If a pilot demonstrates that automating a process saves, say, 100 hours a month, leadership thinks that’s really successful,” says Holmes. “But what it fails to track is that the pilot sometimes is running on over-provisioned infrastructure, so it looks like it performs really well. But you wouldn’t over-provision to that degree during a real production rollout.”
Moving that workload to production changes the calculus. The requirements for compute, storage, and data transfer increase. “API calls can multiply, exceptions and edge cases appear at volume that might have been out of scope for the pilot phase, and then support overheads just grow as well,” he adds.
To prevent this, organisations must track the marginal cost at scale. This involves monitoring unit economics, such as the cost per customer served or cost per transaction. If the cost per customer increases as the customer base grows, the business model is flawed.
Conversely, effective scaling should see these unit costs decrease. Holmes cites a case study from Liberty Mutual where the insurer was able to find around $2.5 million of savings by bringing in consumption metrics and “not just looking at labour hours that they were saving.”
However, financial accountability cannot sit solely with the finance department. Holmes advocates for putting governance “back in the hands of the developers into their development tools and workloads.”
Integration with infrastructure-as-code tools like HashiCorp Terraform and GitHub allows organisations to enforce policies during deployment. Teams can spin up resources programmatically with immediate cost estimates.
“Rather than deploying things and then fixing them up, which gets into the whole whack-a-mole kind of problem,” Holmes explains, companies can verify they are “deploying the right things at the right time.”
When scaling intelligent automation, tension often simmers between the CFO, who focuses on return on investment, and the Head of Automation, who tracks operational metrics like hours saved.
“This translation challenge is precisely what TBM (Technology Business Management) and Apptio are designed to solve,” says Holmes. “It’s having a common language between technology and finance and with the business.”
The TBM taxonomy provides a standardised framework to reconcile these views. It maps technical resources (such as compute, storage, and labour) into IT towers and further up to business capabilities. This structure translates technical inputs into business outputs.
“I don’t necessarily know what goes into all the IT layers underneath it,” Holmes says, describing the business user’s perspective. “But because we’ve got this taxonomy, I can get a detailed bill that tells me about my service consumption and precisely which costs are driving it to be more expensive as I consume more.”
Addressing legacy debt and budgeting for the long-term
Organisations burdened by legacy ERP systems face a binary choice: automation as a patch, or as a bridge to modernisation. Holmes warns that if a company is “just trying to mask inefficient processes and not redesign them,” they are merely “building up more technical debt.”
A total cost of ownership (TCO) approach helps determine the correct strategy. The Commonwealth Bank of Australia utilised a TCO model across 2,000 different applications – of various maturity stages – to assess their full lifecycle costs. This analysis included hidden costs such as infrastructure, labour, and the engineering time required to keep automation running.
“Just because of something’s legacy doesn’t mean you have to retire it,” says Holmes. “Some of those legacy systems are worth maintaining just because the value is so good.”
In other cases, calculating the cost of the automation wrappers required to keep an old system functional reveals a different reality. “Sometimes when you add up the TCO approach, and you’re including all these automation layers around it, you suddenly realise, the real cost of keeping that old system alive is not just the old system, it’s those extra layers,” Holmes argues.
Avoiding sticker shock requires a budgeting strategy that balances variable costs with long-term commitments. While variable costs (OPEX) offer flexibility, they can fluctuate wildly based on demand and engineering efficiency.
Holmes advises that longer-term visibility enables better investment decisions. Committing to specific technologies or platforms over a multi-year horizon allows organisations to negotiate economies of scale and standardise architecture.
“Because you’ve made those longer term commitments and you’ve standardised on different platforms and things like that, it makes it easier to build the right thing out for the long term,” Holmes says.
Combining tight management of variable costs with strategic commitments supports enterprises in scaling intelligent automation without the volatility that often derails transformation.
IBM is a key sponsor of this year’s Intelligent Automation Conference Global in London on 4-5 February 2026. Greg Holmes and other experts will be sharing their insights during the event. Be sure to check out the day one panel session, Scaling Intelligent Automation Successfully: Frameworks, Risks, and Real-World Lessons, to hear more from Holmes and swing by IBM’s booth at stand #362.
See also: Klarna backs Google UCP to power AI agent payments
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
FedEx is using AI to change how package tracking and returns work for large enterprise shippers. For companies moving high volumes of goods, tracking no longer ends when a package leaves the warehouse. Customers expect real-time updates, flexible delivery options, and returns that do not turn into support tickets or delays.
That pressure is pushing logistics firms to rethink how tracking and returns operate at scale, especially across complex supply chains.
This is where artificial intelligence is starting to move from pilot projects into daily operations.
FedEx plans to roll out AI-powered tracking and returns tools designed for enterprise shippers, according to a report by PYMNTS. The tools are aimed at automating routine customer service tasks, improving visibility into shipments, and reducing friction when packages need to be rerouted or sent back.
Rather than focusing on consumer-facing chatbots, the effort centres on operational workflows that sit behind the scenes. These are the systems enterprise customers rely on to manage exceptions, returns, and delivery changes without manual intervention.
How FedEx is applying AI to package tracking
Traditional tracking systems tell customers where a package is and when it might arrive. AI-powered tracking takes a step further by utilising historical delivery data, traffic patterns, weather conditions, and network constraints to flag potential delays before they happen.
According to the PYMNTS report, FedEx’s AI tools are designed to help enterprise shippers anticipate issues earlier in the delivery process. Instead of reacting to missed delivery windows, shippers may be able to reroute packages or notify customers ahead of time.
For businesses that ship thousands of parcels per day, that shift matters. Small improvements in prediction accuracy can reduce support calls, lower refund rates, and improve customer trust, particularly in retail, healthcare, and manufacturing supply chains.
This approach also reflects a broader trend in enterprise software, in which AI is being embedded into existing systems rather than introduced as standalone tools. The goal is not to replace logistics teams, but to minimise the number of manual decisions they need to make.
Returns as an operational problem, not a customer issue
Returns are one of the most expensive parts of logistics. For enterprise shippers, particularly those in e-commerce, returns affect warehouse capacity, inventory planning, and transportation costs.
According to PYMNTS, FedEx’s AI-enabled returns tools aim to automate parts of the returns process, including label generation, routing decisions, and status updates. Companies that use AI to determine the most efficient return path may be able to reduce delays and avoid returning things to the wrong facility.
This is less about convenience and more about operational discipline. Returns that sit idle or move through the wrong channel create cost and uncertainty across the supply chain. AI systems trained on past return patterns can help standardise decisions that were previously handled case by case.
For enterprise customers, this type of automation supports scale. As return volumes fluctuate, especially during peak seasons, systems that adjust automatically reduce the need for temporary staffing or manual overrides.
What FedEx’s AI tracking approach says about enterprise adoption
What stands out in FedEx’s approach is how narrowly focused the AI use case is. There are no broad claims about transformation or reinvention. The emphasis is on reducing friction in processes that already exist.
This mirrors how other large organisations are adopting AI internally. In a separate context, Microsoft described a similar pattern in its article. The company outlined how AI tools were rolled out gradually, with clear limits, governance rules, and feedback loops.
While Microsoft’s case focused on knowledge work and FedEx’s on logistics operations, the underlying lesson is the same. AI adoption tends to work best when applied to specific activities with measurable results rather than broad promises of efficiency.
For logistics firms, those advantages include fewer delivery exceptions, lower return handling costs, and better coordination between shipping partners and enterprise clients.
What this signals for enterprise customers
For end-user companies, FedEx’s move signals that logistics providers are investing in AI as a way to support more complex shipping demands. As supply chains become more distributed, visibility and predictability become harder to maintain without automation.
AI-driven tracking and returns could also change how businesses measure logistics performance. Companies may focus less on delivery speed and more on how quickly issues are recognised and resolved.
That shift could influence procurement decisions, contract structures, and service-level agreements. Enterprise customers may start asking not just where a shipment is, but how well a provider anticipates problems.
FedEx’s plans reflect a quieter phase of enterprise AI adoption. The focus is less on experimentation and more on integration. These systems are not designed to draw attention but to reduce noise in operations that customers only notice when something goes wrong.
(Photo by Liam Kevan)
See also: PepsiCo is using AI to rethink how factories are designed and updated
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
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