News
- The number of companies using AI is up, but the number of tools per company hasn't grown much
- ONS data also confirms that AI doesn't seem to be having a major impact on headcount
- SMBs could be lacking a clear business use case for AI
New ONS data has claimed that while AI adoption has expanded rapidly across UK businesses, the actual depth of adoption is still limited, with most firms using just a small number of tools to improve existing processes rather than totally overhauling their businesses.
Among the businesses surveyed that had adopted AI, the number of tools they used only increased marginally from 1.4 to 1.6 in the three years leading up to 2026.
The ONS also revealed that, while 60% of larger businesses have used AI to improve business operations, no negative impacts on headcount have been observed.
AI adoption might be growing, but that's not the full storyThe data mostly implies experimentation rather than widespread transformation, with just 17% of SMBs (0-9 employees) reporting extensive AI use, however the report warns that many small businesses are merely trying general-purpose AI tools rather than making substantial investments into tech that can truly unlock new levels of productivity.
What's less clear is why SMBs are falling behind their enterprise counterparts, because while 7-14% of all business sizes cited cost as a barrier, 41% of SMBs with 10+ employees said they didn't have any major barrier concerns.
Rather than being held back by costs, infrastructure or capability, the ONS data actually implies that SMBs might actually be lacking compelling business cases to use AI altogether.
All in all, the most recent data confirms that while SMBs haven't exactly ignored AI, they're still stuck in the experimentation stage. The readiness to use automation tech is very much there, but ability to identify a genuine use case is potentially holding back deeper deployment.
Data center design has been shaped by a familiar set of priorities for years: keep systems available, resilient and predictable in any condition. Just like the electrical grid that powers these sites, they have been engineered to provide a highly consistent service regardless of what happens, even when individual components fail.
This has meant operators build layers of redundancy into power, cooling and network infrastructure.
However, artificial intelligence has changed the story. While organizations are racing to roll out AI tools at scale, the data center industry is discovering that not all workloads have the same infrastructure requirements.
For instance, training a large language model, running real-time inference, supporting enterprise applications and processing business-critical transactions each place very different demands on the underlying infrastructure.
Today, one data center doesn’t need to serve every purpose equally and we’re increasingly seeing that facilities can be both flexible and tailored to specific workload requirements.
The end of the traditional modelHistorically, 99.999% uptime was non-negotiable. Data centers have traditionally powered systems like banks, emergency networks and customer-facing digital services, requiring continuous availability.
In these types of environments, where outages could have an extreme impact (from high financial losses to putting real lives at risk) this approach makes sense. Since operators couldn’t always predict which applications would be truly mission-critical, many facilities were built to the highest resilience standards by default.
But AI has changed this. “One-size-fits-all” redundancy isn’t necessary anymore. Different models, training and inference processes each require totally different service levels. For example, facilities for AI training workloads are being designed without backup generators, complex redundancy systems or high-tier architecture.
The good news is that there is a growing understanding of the distinction between environments needed for AI training and AI inference. Training facilities are increasingly being located wherever power is available.
The primary constraints are energy supply, cooling capacity and speed of deployment. In many cases, maximizing compute density and accelerating delivery timelines are more important than achieving the highest possible redundancy levels.
Inference infrastructure presents a different set of priorities. These workloads are often deployed closer to users and support services that people interact with daily. In these scenarios, latency, availability and customer experience become notably more important, creating a stronger case for resilient infrastructure and geographically distributed architectures.
Precision resilience to support an industry under pressureIt’s clear, therefore, that reliability still matters. However, infrastructure requirements vary significantly depending on the service being supported. In today’s age of AI, ‘precision resilience’ should be the focus, e.g., redundancy matching how workloads actually behave, rather than relying on legacy design assumptions.
The key challenge here for operators is determining where resilience delivers genuine business value and where it simply adds cost and complexity.
In a time when developers are facing a huge amount of pressure amid labor shortages, with demand outpacing supply, defaulting to ultra-resilient, high-tier designs for every AI deployment only intensifies challenges.
The industry is also expected to deliver capacity faster than ever before, while battling an ongoing power gap, meaning large-scale developments are increasingly difficult to execute. In this landscape, overengineering infrastructure can have unintended consequences.
Every additional layer of redundancy consumes capital and increases complexity. This is triggering an increased focus on efficiency, not just in terms of energy consumption, but in how capital is allocated throughout a project. Operators are looking to design infrastructure that maximizes the value generated by every watt of available power.
The role of upgradabilityAs operators move away from this one-size-fits-all redundancy to optimize their bottom line, it’s crucial that their facilities can adapt as workload requirements change.
While inference is expected to account for a growing share of AI demand, the landscape continues to evolve and it’s difficult to predict which workloads, densities and cooling requirements will dominate in the future. Infrastructure that can accommodate changes in compute technologies will be better positioned to support the next generation of AI applications.
Flexibility and fungibility are therefore the new non-negotiables in data center design. How is this made possible? Increasingly, developers are using ‘building blocks’ constructed off-site in factory environments, and then later assembling them on site to create an adaptable facility that can forever evolve, grow and shift.
This approach reduces the need to make every resilience decision upfront and builds with tomorrow’s changes in mind. In the coming years, we will see a shift towards multiple types of facilities, each developed for a different purpose.
These will range from energy-optimized training campuses built close to power sources, to distributed inference sites where uptime and latency directly affect user experience, alongside hybrid environments supporting both AI and traditional workloads. Yet they should all be built with flexibility front of mind to ensure they can evolve as requirements change.
We've featured the best web hosting service.
This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit
The Lenovo Yoga Slim 7x Gen 11 is an ultra-thin laptop with a strong spec that makes it more than capable for workday use.
It looks smart, thanks to its minimalist form. All sides are flat, and the Cosmic Blue colorway is subtle yet helps to distinguish it in this largely monochromatic sector.
The build quality is excellent. The body is as premium as it comes, feeling very solid but also light and smooth to the touch. In fact, it could probably lay claim to having one of the best laptop constructions around right now. Allied to that lightness is its thinness, which is quite remarkable, although the thick rear foot underneath somewhat negates this aspect, which is a shame. What’s more, these truncated dimensions mean that you get three ports, all of which are USB-C.
(Image credit: Future)With the new Snapdragon X2 Elite chip equipped, the Yoga Slim 7x Gen 11 is a capable all-round performer. It handles multiple browser tabs and 4K streaming with ease, as well as basic productivity. While it's not necessarily aimed at gamers and lacks a dedicated GPU, it's even capable of some light gaming; it managed to run Cyberpunk 2077 in a playable state, although don't expect top-tier performance. Heat and fan noise are generated when such workloads are conducted, but neither is too disruptive.
Another advantage of the X2 Elite chip is the improved efficiency. This is evident in the battery life of the Yoga Slim 7x Gen 11, which is one of its most impressive aspects. It lasted close to a full day when I ran a movie on a continuous loop, which ranks among the best in class. It’s also quick to charge, taking about two hours to do so.
The 2.8K display in my review unit was sharp, bright, and vivid, although it was prone to showing reflections at times. For the most part, though, these were only apparent in the most unfavorable of lighting conditions.
The keyboard is fairly easy to use, thanks to the ergonomic keycaps and their spacing. However, presses are a little heavier than you might expect, so this can take some getting used to if you’ve come from a board that requires a light touch. The touchpad is large and smooth, which makes for easy navigation, while taps and clicks are responsive and provide good feedback.
The Yoga Slim 7x Gen 11 commands a reasonably high price tag, but considering its spec and quality, it makes for good value compared to some of its less premium-feeling rivals. It might lack the ports and graphical performance of other laptops, but it’s a fine choice if portability and build quality are among your top priorities.
Lenovo Yoga Slim 7x Gen 11 review: Price & availability(Image credit: Future)- Starts from $999.99 / £945 / AU$1,999
- Available now in one colorway
- Reasonably expensive for the spec
The Yoga Slim 7x Gen 11 starts from $999.99 / £945 / AU$1,999 and is available now in one color: Cosmic Blue. This base spec features a Snapdragon X2 Plus X2P-42-100, 16GB of RAM, 512GB of storage, and WUXGA display. This isn't exactly cheap, and costs escalate quickly as you move up the model range. The top-spec unit, which features 32GB of RAM, 1TB of storage, and a 2.8K display, costs $2,299.99 (about £1,700 / AU$3,300).
If you’re looking for another thin and light laptop, then the HP OmniBook 7 is a very strong alternative. I was very impressed with its performance when I reviewed it, and its only missteps are the port placements and slightly harsh keyboard. It's base model is slightly cheaper than the Yoga Slim 7x Gen 11's in the US, and significantly so in the UK.
There’s also the Asus Zenbook A14, which is incredibly thin and light, and has one of the best battery lifespans around. However, it lacks the build quality and display brightness of the Yoga Slim 7x Gen 11, yet it’s significantly more expensive when comparing like-for-like models.
- Value score: 4 / 5
Base spec
Max spec
Price
$999.99 / £945 / AU$1,999
$2,299.99 (about £1,700 / AU$3,300)
CPU
Snapdragon X2 Plus X2P-42-100 Processor (4.04 GHz)
Snapdragon X2 Elite X2E-80-100 Processor (4.70 GHz)
Graphics
Integrated
Integrated
RAM
16GB LPDDR5X
32GB LPDDR5X
Display
14-inch WUXGA (1920 x 1200), OLED, Glare, HDR 500 True Black, 100% DCI-P3, 400 nits, 60Hz
14-inch 2.8K WQXGA+ (2880 x 1800), OLED, Glare, HDR 1000 True Black, 100% DCI-P3, 500 nits, PureSight Pro, 120Hz
Storage
512GB SSD M.2 PCIe Gen4
1TB SSD M.2 PCIe Gen4
Ports and Connectivity
3x USB-C (USB4 40Gbps); Wi-Fi 7, Bluetooth 5.4
3x USB-C (USB4 40Gbps); Wi-Fi 7, Bluetooth 5.4
Battery
70Wh
70Wh
Weight
2.6lbs (1.2kg)
2.6lbs (1.2kg)
Dimensions
12.3 x 8.7 x 0.6 inches / 312 x 221 x 14mm
12.3 x 8.7 x 0.6 inches / 312 x 221 x 14mm
Lenovo Yoga Slim 7x Gen 11 review: Design(Image credit: Future)- Extremely thin
- Premium materials
- Lacks ports
The minimalist design of the Yoga Slim 7x Gen 11 is appealing, while the dark Cosmic Blue colorway rescues the unit from looking dull. The rounded sides and corners help to soften its appearance. Most sides are flat, with no extraneous bulges, save for the central camera bezel that extends beyond the rest of the lid. However, the overhang this creates actually helps you to open the lid, which is a thoughtful touch.
The body is one of the best I’ve seen in a laptop. It looks and feels premium, rivaling what the best MacBooks have to offer. It’s incredibly smooth and seems very durable. There’s some flex to the base and the lid, but they’re remarkably solid when you consider just how light the whole unit is. The lid is also very stable when open, and adjustments are smooth and easy to make.
(Image credit: Future)Just as impressive is the thinness of the Yoga Slim 7x Gen 11. It’s certainly one of the thinnest 14-inch laptops I’ve seen, although it’s a shame the bulky bar underneath, which acts as the rear foot, somewhat undermines this aspect. This is no doubt to improve airflow from the underside vent, which is more exposed than many others.
To achieve this thinness, port selection has been compromised. There are only three ports, all of which are USB-C, which means you’ll need plenty of adapters (or a hub) if you’re going to be connecting lots of peripherals. The saving grace is that all ports have 40Gbps transfer rates, support the Power Delivery standard, and can be used to charge the laptop itself. They’re also split across both sides of the unit, which improves practicality.
On the right you’ll also find the power button and a button for disabling the webcam, which might disappoint those hoping for a physical privacy shutter.
- Design score: 4 / 5
- Capable everyday performance
- Light gaming possible
- Keys are a little heavy
3DMark: Night Raid: 40,637; Fire Strike: 7,911; Steel Nomad: 1,051; Solar Bay: 21,754; Solar Bay Unlimited: 22,518; Solar Bay Extreme: 2,491; Solar Bay Extreme Unlimited: 2,526
Geekbench 6.5: Multicore: 20,362; Single-core: 3,815
Cinebench R23: Multi Core: 13,071; Cinebench R24: Single Core: 90; Multi Core: 723
Crossmark: Overall: 1,896; Productivity: 1,703; Creativity: 2,171; Responsiveness: 1,742
Passmark Overall: 7,821.2; CPU: 30,341.4; 2D Graphics: 594.2; 3D Graphics: 6,632.9; Memory: 3,429.5; Disk: 51,620.7
BlackMagicDisk: Read: 5,214MB/s; Write: 4,947MB/s
HandBrake 4K to 1080p: 63.68fps
Total War: Warhammer III: 1080p, Medium: 43fps
Total War: Warhammer III: 1800p, Ultra: 11fps
Battery Life (TechRadar movie test): 23 hours and 30 minutes
For everyday use, the Yoga Slim 7x Gen 11 is certainly fast enough. It handled many of the tasks I threw at it with aplomb, from basic productivity to 4K streaming. With the 32GB of RAM in my unit, it also handled multi-tab web browsing with ease.
No doubt a lot of this performance comes from the new Snapdragon X2 Elite chip inside the Yoga Slim 7x Gen 11. Qualcomm claims this chip is a stronger performer than Intel equivalents, and based on our testing this appears to be true. For instance, it beat the Geekbench Single Core benchmark score of the HP OmniBook 7, with its Intel Core 5 220H, by over a thousand points, and doubled its Multi Core score.
When it comes to gaming, the Yoga Slim 7x Gen 11 was less competent, but that's hardly a surprise — it's not really aimed at wooing gamers, given it doesn’t have a dedicated GPU. However, I still managed to run Cyberpunk 2077 in a playable state, albeit with ray tracing disabled and resolution scaling set to Balanced. It wasn’t exactly a smooth experience, so it's not likely to be for you if you're in the market for a dedicated gaming laptop, but if you want a productivity powerhouse with just a little casual gaming on the side, it should suffice.
As expected, the fans can be heard whirring away in such cases, but the noise isn’t too disruptive. Heat is also generated, and temperatures can get quite high in places, although thankfully these hotspots are confined to the top and underside of the base.
(Image credit: Future)The display in my review unit was crisp, thanks to its 2880 x 1800 resolution, and the high levels of brightness ensure content is viewable in most conditions. There were times when reflections showed on screen, but only when the lighting in my environment was particularly unsuitable.
The keys in the Yoga Slim 7x Gen 11 adopt Lenovo’s trademark shape, which are very ergonomic. The generous spacing between them also makes for a forgiving typing experience. However, they’re a little heavier and travel further than you might expect, which might throw off those who prefer a light and snappy response. The touchpad is suitably large and incredibly smooth, making it conducive to cursor navigation. Taps are responsive without being overly sensitive, and clicks are easy to perform and provide plenty of feedback.
- Performance score: 4 / 5
The battery life of the Yoga Slim 7x Gen 11 is very impressive. No doubt that aforementioned new Snapdragon chip plays a major part here, due to its improved efficiency. It managed to last just under 24 hours in our movie playback test, putting it ahead of many other laptops in this sector. This may be partly due to that aforementioned chipset, which Snapdragon claims brings improved efficiency. Charging is a quick affair, too, as it took about two hours to fully replenish.
However, there are those that can match and even outlast it, such as the HP OmniBook 7, which managed 26 hours in the same test. And then there’s the Asus Zenbook A14, which managed a staggering 28 hours and 25 minutes.
- Battery life score: 4.5 / 5
Attributes
Notes
Rating
Value
It seems expensive, but when you consider its design and spec, it’s reasonable.
4 / 5
Design
I couldn’t help but marvel at its thin and premium design. The port selection is disappointing, though.
4 / 5
Performance
Great for everyday tasks, and it can even handle light gaming. The display is vivid if a little reflective, and it can get hot under load.
4 / 5
Battery life
Among the best in class, it really can last a full day. It’s quick to charge, too.
4.5 / 5
Total Score
The Yoga Slim 7x Gen 11 is great if you need a thin yet powerful Windows machine that lasts all day unplugged. A few areas are less impressive, but it’s still a good value proposition.
4 / 5
Buy it if…You want one of the thinnest laptops around
That bulky foot underneath might spoil things a little, but the Yoga Slim 7x Gen 11 is still incredibly thin.
You want a premium design
It looks premium, and the body feels exquisite and hardwearing. It’s also quite light.
You want lots of ports
There are only three ports on board, and all are USB-C, so you'll need a hub if you have lots of connections to make.
You want light keys
Although they’re comfortable to use, the keys on the Yoga Slim 7x Gen 11 are a little heavier than on many other laptops, which might take some getting used to.
HP OmniBook 7 14-inch (2025)
Like the Yoga Slim 7x Gen 11, the OmniBook 7 is an incredibly thin and light laptop. Not only that, I was very impressed with how well it handled all kinds of workloads, and it has a superb battery life to boot. Read our full HP OmniBook 7 14-inch (2025) review.
Asus Zenbook A14
Another portable hero, the A14 is even lighter than the Yoga Slim 7x Gen 11. However, its battery life is even better; in fact, it’s the most enduring laptop I’ve ever tested, lasting a staggering 28 hours in our movie playback test. Its build isn’t as premium as the Yoga Slim 7x Gen 11’s, and its display isn’t as bright, but it’s still a capable machine. Read our full Asus Zenbook A14 review.
- Tested for several days
- Used for a variety of tasks
- Plentiful laptop experience
I tested the Yoga Slim 7x Gen 11 for several days, during which time I used it for a variety of tasks, from productivity and browsing to streaming and gaming.
I also ran our series of benchmark tests, designed to assess every aspect of a laptop's performance. I also ran a movie on a continuous loop to test the battery life.
I've been using laptops for decades, and have reviewed a large number of them, from small budget models to large gaming machines.
- First reviewed: July 2026
- Read more about how we test
If you’ve done any shopping for marketing automation tools these days, you’ve probably noticed they all claim to be “powered by AI.” Apologies for splitting hairs, but that’s just not true.
A significant portion still rely primarily on rule-based automation, work identically to the platforms they replaced, triggering if/then workflows created by human engineers years ago. Give their systems an edge case to parse and you’ll soon see them send an inappropriate email, crash, or output some stale nonsense that wouldn’t matter to any living person.
Same label. Same old architecture. The problem? A rule bottleneck.
Traditional marketing automation relies on knowing rules ahead of time. A lead hits a score? Send an email. A prospect completed behaviors A, B, and C? Trigger sequence Y. Wait Z days, send the follow-up email.
A rules engine can execute these commands flawlessly – but it can only execute what it knows. When a situation arises that doesn’t fit the rules, what do you do? You update the rules.
Why this mattersMarketing is messy. Prospects take unpredictable journeys, trends come and go overnight, and audiences who loved your message last week don’t care about it this week. But rule-based systems can only improve when given new rules to fire. Engineers can’t possibly keep writing rules faster than the world changes.
The industry has been papering over this problem with AI buzzwords. Sprinkle some Neural Network magic on a rule engine, and suddenly you’ve got yourself an “AI platform.” Engineers who look past the updated sales brochures still find the same good old-fashioned if/then statements, patched up with trendy new nomenclature for the latest round of funding.
The difference between legacy automation and true agentic AI is that true agentic AI won’t just patch up the last generation of marketing automation tools – it will replace them. Agentic AI isn’t defined by capabilities so much as by the way decisions are made.
Rules engines ask, “what rule should fire next, given this input?” Agents ask, “what action should I take to get closer to my goal?” This is subtle but critical. Agent theory holds that the system knows its goal, its current context, and a list of available actions it can take.
Based on those three pieces of information, it can reason as to which action will bring it closer to accomplishing its overall objective. This extends far beyond executing canned responses - it’s deciding what to do.
Agentic systems maintain goals, reason over available actions, invoke tools, evaluate intermediate results, and adapt their plans as new information becomes available. The architecture is fundamentally iterative rather than purely reactive.
You know where this is going.
An agent can adapt if a campaign stops performing. It can coordinate with other agents who manage different subsets of that workflow. And it can do so without a human engineer going back into the system to rewrite the rules every time the world changes. The system manages its goals.
Why specialization mattersOne important architectural decision that separates good agent implementations from the rest is specialization. Should you build one big generalist AI system to handle everything or many specialized agents, each performing their own task?
Specialization comes up often in discussions around AI, from medical doctors to Renaissance men. There is broad utility in generalization, but singular accuracy in specialization. The family doctor can handle any symptoms you throw at them. But when you need to be absolutely certain about your diagnosis, you see a specialist.
That’s because specialists aren’t smarter than the generalist – they’re just trained on narrower data. Likewise, generalist AI models aren’t going to produce great results for highly specific use cases. OpenAI’s models can write you a marketing strategy. They can craft creative assets. But they can’t produce marketing assets that:
- Fit the pixel ratio requirements of a given publisher
- Match your brand’s color palette
- Align with your target audience’s emotional affinity profile
- Incorporate mentions of trending topics from the previous day
They can’t do all of those at once, either. And you shouldn’t expect them to. For hard problems with specific solutions, you should build specialized agents (sometimes called “agent crews”) that own a narrow subset of your workflow.
One crew might specialize in strategy generation, while another focuses on creative writing. One might select publishers while another analyzes performance. Separately, these crews create atomic workflows that a generalist system would struggle to manage.
How not hosting your models affects data privacyThere’s another argument for specialized, privately hosted models that isn’t made enough: data privacy.
Whenever you use a public large language model (LLM) to write marketing copy, your data is being uploaded to someone else’s infrastructure. “We don’t use customer data for training” is easy to say but barely offers any assurance. Inputs are still being ingested, processed, stored, and handled according to what that provider’s internal policies dictate.
And those policies can change… most corporate lawyers have never looked at the data use section of public AI providers Terms of Service, let alone dissected it line-by-line.
But what about controls your organization can enforce? Do your developers scrub data for PII before generating content with an LLM? That only works if everyone in your company memorizes your data policies and uses tools responsibly. One rogue employee attaching a spreadsheet full of internal pricing to a prompt breaks your compliance.
But if the model itself is hosted privately, that’s one major source of exposure that goes away. Your data never leaves your infrastructure. There’s no ingestion point to transmit it to a third-party, no training feedback loop that will process it, and no agreement to parse about how that company will handle your data “moving forward.”
Governance is a system propertyBecause AI in the enterprise has reached a maturity level where governance is a legitimate concern, many teams treat it as a bulk edit at the end of AI-generated content. Have humans review and approve. That’s fine, and many teams require this today. But governance should be built into the system at a fundamental level.
Well-built agents have guardrails at every stage of the decision-making process. That means models that make predictions within set bounds. That means observability that can trace every word generated back to its origin.
That means third-party benchmarking to prove your models perform well against industry standards, not just internal testing. Governance shouldn’t just be applied to outputs – it should be inherent in the architecture.
What enterprise buyers should actually be asking aboutBuying criteria for agentic AI will vary by company, but as requests for proposal accelerate to keep pace with innovation in the industry, here are a few considerations every enterprise buyer should ask about:
- Goals vs. rules - Is this system actually agentic? Or is it just automating workflows with AI tools bolted on? The first step is asking vendors point blank what their system does when it encounters data it doesn’t know how to parse. Rules engines will point to specific fallback rules that execute. Agents will talk about reassessing their goal and weighing their available actions until they decide on the next best step.
- Models and hosting - Where are the models hosted? Are they specialized and trained on domain-specific data? This answers two questions at once – vendor capability, as well as data privacy concerns.
- Long-term memory and context - Enterprise agents become dramatically more useful when they retain organizational context over time. Rather than treating every interaction as a new conversation, they can accumulate institutional knowledge, remember previous decisions, and personalize future actions while remaining within governance boundaries. Persistent memory allows agentic systems to improve continuously without requiring engineers to encode new rules after every edge case.
- Hallucination - No current LLM is immune to hallucinations. The important architectural question is how the system detects, bounds, and mitigates them before they affect downstream business processes. Specialists hallucinate less in their domain of expertise. Prediction window guardrails limit how far an AI system can go “outside the data.” Human approval gates before sending anything live catch anything that slips through.
- Governance / auditability - Can the system provide traceability for every output it generates? Is the system’s accuracy benchmarked against a third-party, or just internally verified?
There's an additional argument that often gets overlooked in discussions focused on capability: cost structure.
Token-based pricing from large model providers creates a fundamentally unpredictable cost model for enterprise deployments. Every question, every generation, every iteration costs tokens — and iterating toward an acceptable output for a complex campaign task can consume a significant volume of them.
Enterprise subscriptions impose usage caps that create their own operational friction. The more AI-dependent your workflows become, the more acute this pressure grows.
Organizations that own and host their own specialized models are not subject to this dynamic. There is no token meter running. The economic relationship is closer to infrastructure than to a metered service - you bear the cost of building and maintaining the system, and in return you have predictable marginal cost. For organizations at scale, that math changes substantially.
Don’t fall victim to marketing speakAI marketing platforms will continue to flood the market with AI-sounding languages attached to rules engines. But for enterprises who truly want to deploy agentic AI, there’s a far better solution. Domain specific, privately hosted agents that don’t leave your organization exposing itself to risk.
As agentic systems mature, the organizations that differentiate between genuine autonomous architectures and AI-enhanced workflow engines will be better positioned to capture sustainable competitive advantage.
We've featured the best business intelligence platform.
This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit