I can’t grow anything. Multiple attempts to create a cottage garden, first in Idaho and now in South Carolina, have brought disappointment. Both are challenging climates, but where others have succeeded, I’ve been left with little more than a pile of cherry tomatoes for my vast efforts (those things are bulletproof).
Artificial Intelligence
This smart garden turned my black thumb green
I’d all but given up on the idea of ever successfully growing my own food — I can’t even keep those pots of herbs you buy at the grocery store alive for more than a week — until I met the Gardyn Studio 2.
A smart indoor garden, the Gardyn Studio 2 is an automated growing platform that deploys AI to do what I failed to do: watch over and properly take care of my plants.
It works through a combination of hydroponic watering, automated lighting, a camera that captures plant images, and algorithms that analyze the plants’ growth stages and adapt. An AI assistant called Kelby, which requires a subscription, manages the light and water cycles, sends alerts when I need to intervene, and processes all the data from the Gardyn’s sensors and camera to help your plants grow.
It’s an impressive, albeit very expensive, system. In the three months I’ve had it in my home, I’ve grown a sunflower, a full kohlrabi, lots of basil, lettuce, green beans, chard, and some other tasty exotic veggies I’d never heard of. I am currently working on strawberries, cherry tomatoes, lavender, jalapeño peppers, and buttercrunch lettuce — in December, in my dining room. This is why I love technology.
Everyone loves the idea of growing their own food, but few of us have the space, time, or expertise to dedicate to the task. It’s a bit like baking; it’s a lot harder than it looks.
Gardyn Studio 2 is designed to address these problems. It costs $549, with an optional $25 monthly subscription (or $19 a month with a two-year plan). That gets you Kelby’s assistance, new seed pods every month to keep your Gardyn growing, and discounts on plant food, among other perks.
My Gardyn started producing within 6 weeks, and I was able to cut my family of four’s weekly produce buying in half, easily covering the monthly subscription. Plus, I really enjoy that every time I go to get something from the Gardyn, it’s fresh and ready, not wilted in my fridge. I’ve also found I generate far less food waste.
1/4
The Studio 2 is Gardyn’s newest model and is a smaller version of its flagship product, the $900 Gardyn Home. The Studio has space for 16 plants (compared to 30 on the Home) and is a redesign of the first Studio, which launched in 2024. The Studio concept addresses customer feedback that the Gardyn Home produced too much produce for smaller households to keep up with, and its footprint was too large, founder and CEO FX Rouxel told me.
- Price: $549 (or $23/month for two years)
- What’s in the box: Gardyn Studio, 16-plant starter kit, grow guide
- Membership: Starting at $19 a month paid annually or $25 paid monthly (includes monthly plant refills, Kelby AI assistant in app, additional features like Vacation Mode).
- Size: 17”W x 12”D x 54”H, 1.4 sq ft footprint
- Sensors: water level, humidity, and interior temperature
- Camera: 8MP ultra-wide camera (photos only, can be disabled)
- Lights: 60W full spectrum LED lights
- Connectivity: Wi-Fi, 2.4GHz band
- Warranty: 2 years
The Studio 2 uses new columns that require less maintenance, an upgraded camera with higher resolution, and a wider field of view to better monitor its charges. There is also a new sunrise/sunset lighting mode for the large 60-LED lamp that helps the garden grow.
The columns are what make Gardyn unique among indoor plant systems (none of which I’ve tried). It’s a patented vertical growing, hydroponic system that Rouxel calls “the most compact way to grow food at home.”
Proprietary yCube seed pods that use rockwool as a growing medium fit into the columns, which sit on a large water tank with a pump inside and an attractive wooden lid. The strip of LED lights with a camera built in sits in front of the plants, facing the wall (so it’s not going to be snapping pictures of you, only the plants, every 30 minutes).
Gardyn comes with straps to anchor the system to the wall, as fully grown plants could cause it to topple. A button at the bottom lets you turn the light on or off quickly, and you can also schedule it in the Gardyn app.
Assembling Gardyn was straightforward and took about 20 minutes. It came with a starter kit of 16 seed pods called the Chef’s Favorites, including herbs such as thyme, basil, and lemon balm, as well as celery, collard greens, green tatsoi, spinach, salanova, and yellow chard. There was also a sunflower.
1/5
I inserted the pods using the app to determine the best position for each and sat back and let Gardyn do the rest. Other than topping up the water based on Kelby’s prompts in the app and adding some plant food, I didn’t have to do anything else until four weeks in, when I was told to clean the tank and check the roots.
I am growing strawberries, cherry tomatoes, and lavender — in December, in my dining room. This is why I love technology
One challenge of Gardyn is finding a suitable spot for it. At 1.4 square feet wide and about 5 feet tall, it takes up quite a lot of space. While the kitchen would be the logical place, the light is too bright for my open plan space. You can adjust the timing of the light, but Gardyn recommends 14-16 hours of full light per day. I ended up putting it in the dining room just off the kitchen.
Once the plants started to bud, Kelby told me to trim a few to encourage stronger plants, and within a couple of weeks, I had my first harvest of young sprouts, which made for a delicious, if unusual, salad.
Harvesting got more labor-intensive as things started to really grow; the green tatsoi was going like gangbusters and started blocking the light to some of the herbs. I did a bit of strategic reconfiguring of the pods, along with lots of harvesting — it was great as a bok choy substitute.
I can see why people wanted a smaller Gardyn, although I think if I had more plants I used regularly, it would be a bit easier.
I did have two pods that never sprouted, and my bull’s blood beets didn’t seem to be doing much, so I swapped them out for three new pods, including green beans, cherry tomatoes, and strawberries. (These needed to be started outside of the Gardyn as they can’t take plant food until they sprout). Those will obviously take longer to proffer their goods, although I got at least one serving of green beans within a month.
It will take some fine-tuning to determine which plant lineup works best for my needs. At this point, I’m leaning towards a combination of herbs, salad greens, cherry tomatoes, and chard. I can see that once you have a solid rotation that fits with your culinary habits, Gardyn could replace most of your fresh produce purchases.
1/4
I’m also experimenting with transplanting the mature plants into my garden. However, out there, they will be at my mercy and away from the watchful eye of Kelby. I was surprised to find Gardyn’s AI assistant was very useful. It basically did what I have failed to do in my time as a master plant killer — keep an eye on and take care of the plants, roping me in to help every now and then to add water, plant food, thin the sprouts, harvest, prune roots, and other actions to encourage growth.
Now, if you are more attentive and attuned to plants than I am, you could do much of this yourself, but if you are a beginner or a failure like me, Kelby feels fairly essential.
Rouxel explained that the AI models crunch data from temperature, humidity, and water sensors, along with images of the plants, to compute how fast the plants are growing and to optimize variables to promote healthy growth. “It’s like having a personal master gardener on your side 24/7, always monitoring what’s going on, taking action where it can, and when it cannot, it lets you know what to do,” he says.
If you opt out of Kelby after the free month’s trial, you can continue to grow plants and automate the watering and lighting on a schedule that you can customize, as well as buy new yCubes directly from the Gardyn app starting at $5 a plant.
I’ve enjoyed using the Gardyn, and everything I’ve grown has tasted delicious. It does require some maintenance, but far less than growing anything from scratch by myself. It remains to be seen if my plan to transplant veggies into my garden will be successful. If it is, I would be even more inclined to invest in this. But for anyone who likes the idea of having freshly grown vegetables, fruits, flowers, and herbs in their home, Garden really is a foolproof system.
Photos by Jennifer Pattison Tuohy / The Verge
Follow topics and authors from this story to see more like this in your personalized homepage feed and to receive email updates.
Continue Reading
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.
-
Fintech6 months agoRace to Instant Onboarding Accelerates as FDIC OKs Pre‑filled Forms | PYMNTS.com
-
Cyber Security7 months agoHackers Use GitHub Repositories to Host Amadey Malware and Data Stealers, Bypassing Filters
-
Fintech6 months ago
DAT to Acquire Convoy Platform to Expand Freight-Matching Network’s Capabilities | PYMNTS.com
-
Fintech5 months agoID.me Raises $340 Million to Expand Digital Identity Solutions | PYMNTS.com
-
Artificial Intelligence7 months agoNothing Phone 3 review: flagship-ish
-
Artificial Intelligence7 months agoThe best Android phones
-
Fintech4 months agoTracking the Convergence of Payments and Digital Identity | PYMNTS.com
-
Fintech7 months agoIntuit Adds Agentic AI to Its Enterprise Suite | PYMNTS.com