With the evolution of the cloud, cloud services are becoming broadly accessible and are a more viable option to deliver virtual desktops from the cloud. However, many organizations also struggle with the transition to the cloud due to the new skills required and a different approach to infrastructure and operations. Nerdio changes the game by simplifying and enabling efficient and effective operational tooling through automated deployment and configuration features, streamlined setup, and cost-effective running of cloud environments.

Greater visibility of cloud costs

Managing cloud costs effectively is a significant challenge for many organizations. As they transition to cloud-based solutions, they often find their budgets spiraling out of control due to a lack of visibility and understanding of their cloud spend. Without the correct building blocks in place, these companies exceed their cloud budgets and fail to optimize their resources properly, leading to inefficiencies and unnecessary expenses. Nerdio Manager for Enterprise addresses these pain points by providing comprehensive control, confidence, and visibility into cloud spending:

• Sophisticated reporting and real-time analytics: Nerdio’s intuitive dashboard provides insights into overall cloud expenditures and expert guidance on how to achieve additional savings in real time.
• Advanced Auto-scaling: The platform fine-tunes resource allocation, adjusting up or down as needed to ensure that you only pay for the resources you’re actually using. This dynamic scaling capability not only optimizes costs but also ensures that the necessary resources are available when needed.

Based on what we’ve seen across our existing client base, Nerdio Manager’s Auto-scaling can easily save you up to 75% on Azure compute and storage costs.

Simplify management and optimize productivity

Managing and scaling Microsoft Azure Virtual Desktop (AVD) can be a complex and time-consuming task, particularly given the limited native management tools available in Azure. Organizations often struggle with scaling and require a robust solution to deploy at scale with the IT resources you have in-house.

Additionally, companies migrating from Citrix or VMware expect comprehensive management capabilities, which are often lacking in the native Azure platform. This gap necessitates highly skilled technical resources to architect, build, deploy, and manage their AVD environments.

Nerdio Manager for Enterprise simplifies the entire process under one comprehensive management tool. With Nerdio, even IT generalists can build, deploy, and manage Azure Virtual Desktop environments efficiently thanks to these product features:

• Image management: Nerdio automates, manages, and schedules critical but mundane tasks, freeing up IT teams to focus on other essential duties. This automation not only saves time but also reduces the risk of human error.
• Rapid deployment: You can deploy Azure Virtual Desktop environments within a few hours or connect to an existing environment in as little as 10 minutes with minimal manual intervention. Nerdio’s intuitive interface streamlines the provisioning of virtual desktops, making the process quick and efficient.

The automation built into Nerdio Manager for Enterprise ensures you can scale and manage your virtual desktop environments effectively in 20% of the time it would normally require.

Secure and protect

Ensuring the security and protection of your Azure Virtual Desktop environment is crucial for business continuity. Downtime can have significant financial repercussions, making robust disaster recovery and security features essential. Nerdio Manager for Enterprise enhances the security and resilience of your Azure Virtual Desktop deployment, providing advanced tools to safeguard your virtual desktop infrastructure:

• Disaster recovery (DR): The dynamic Auto-scaling previously mentioned can also be leveraged for DR. This functionality ensures that your resources can scale up or down in response to demand fluctuations, maintaining operational efficiency and minimizing downtime. By dynamically adjusting resources, Nerdio helps organizations avoid the substantial financial losses associated with prolonged outages.
• Role-based access control (RBAC): The RBAC feature ensures that permissions are tightly managed inside Azure, giving users access only to the resources necessary for their roles, thereby enhancing overall security.
• Secure deployment model: Nerdio Manager for Enterprise is deployed as an Azure application directly in your own subscription. This ensures that sensitive management operations are conducted within the customer’s own controlled and secure Azure infrastructure, further enhancing the security of the AVD environment.

Nerdio offers comprehensive security and protection features, ensuring that your AVD deployment is both resilient and secure while minimizing risks and potential financial losses.

Whether it’s troubleshooting technical issues, optimizing performance, or navigating licensing requirements, Nerdio’s comprehensive suite of tools and features helps IT teams of all sizes maximize their AVD investment while driving operational excellence and fostering a more productive workforce.

Explore Nerdio Manager for Enterprise on your own in our self-guided demo.

Legacy virtual desktop infrastructure (VDI) solutions that rely on centralized servers and complex infrastructure are not well-suited for the dynamic and diverse needs of modern organizations. Instead, many are looking for a cloud-based alternative that reduces complexity and total cost of ownership.

A few years ago, legacy VDI solutions were vital to organizations seeking to bolster application delivery management, unified endpoint management, and security capabilities. But they are often expensive, difficult to manage, and prone to performance issues.

Desktop as a service (DaaS) solutions leverage the cloud to deliver virtual desktops on demand and provide many benefits that on-premises systems lack, such as improved security, scalability, flexibility, and cost-efficiency. Users can access virtual desktops on demand from internet-connected devices while the organization can simplify and automate application management with less costly, scalable solutions.

Microsoft Azure is renowned for its robust security features and compliance certifications, and Microsoft Azure Virtual Desktop (AVD) leverages those capabilities to provide a secure virtual desktop environment. From data encryption and access controls to threat detection and identity management, Microsoft AVD helps organizations mitigate security risks and ensure compliance with industry regulations and standards. 

Still, not all IT organizations want to handle the extensive customization and configuration options that can optimize AVD for specific organizational requirements. In some cases, they don’t have the time it takes for IT staff to acquire training and familiarization with the Azure platform. As in many areas of IT, a managed platform option can supplement overtaxed internal resources.

Avoiding tedious migration efforts

Radius Recycling, an operator of large recycling facilities and its own steel mills, used a legacy VDI provider and was reliant on old servers running on Windows 2008 to host custom applications developed in-house. It was breaking so often that Radius felt it was completely unstable and untenable. The company’s development team began the process of migrating to Azure the main SQL server, which was supporting 50 Pick-n-Pull lots across the US and Canada.

With one IT staffer tasked with rebuilding apps from scratch and deploying them on Windows 10, the process was inefficient and tedious. Then that staffer learned about Microsoft AVD with Nerdio Manager for Enterprise. Microsoft AVD and Nerdio support more than 500 monthly active users at Radius Recycling, easing the burden on the help desk and providing employees with improved performance of third-party apps.

Onboarding was relatively simple without the need for an elaborate new vendor procurement process because Nerdio Manager is installed directly from the Azure Marketplace into the customer’s tenant.

Microsoft AVD with Nerdio allows organizations to leverage Microsoft’s significant investments in AVD, Intune, and Windows 365 services. Nerdio Manager for Enterprise enhances Microsoft’s native components instead of replacing them with proprietary functions. There’s no lock-in, so organizations can remove Nerdio Manager without impacting users or infrastructure.

With the advantage of Nerdio’s modern, unified platform, IT professionals can deliver and maintain a wide range of virtual Windows endpoints and Windows applications across hybrid workforces with ease and utilize powerful monitoring and analytics for maximum effectiveness.

Organizations can fully manage the entire lifecycle of desktop images without the need for PowerShell scripting. The platform offers Scripted Actions that enable IT Admins to create images in less than 30 minutes. Meanwhile, companies can reduce Azure compute and storage costs by up to 80% using AVD with Nerdio’s Auto-Scaling capabilities.

Organizations need greater flexibility to adapt to changing business needs. To learn how Microsoft AVD with Nerdio can help modernize IT infrastructure and enhance user productivity, visit us here.

The advantages of cloud computing come with notable challenges that frustrate enterprises, particularly managing costs and optimizing resources. Desktop as a service (DaaS) is one notable cloud service that can be complex and requires careful planning and monitoring to avoid overspending and underutilization, leading many to turn to managed platforms that can ease those burdens.

Microsoft Azure Virtual Desktop (AVD) stands out as a leading enterprise VDI solution, offering unparalleled scalability, security, and flexibility for organizations of all sizes. The virtual desktop operating system is used to securely host desktops and applications in the cloud. It is highly scalable and flexible, renowned for robust security features, and it offers a cost-effective, consumption-based pricing model.

Many organizations, though, face a steep learning curve in implementing and managing AVD, especially if the IT team lacks experience with cloud-based virtual desktop environments. Configuring AVD’s extensive customization options and optimizing it for specific organizational requirements takes planning and expertise. That’s led some to augment AVD with Nerdio Manager for efficient applications management, streamlining support for hybrid workforces, and providing a single pane of glass for overseeing AVD, Windows 365, physical devices, and Windows applications.

AVD with Nerdio enables organizations to modernize their IT infrastructure and enhance user productivity. Nerdio significantly reduces the learning curve associated with AVD by offering intuitive management interfaces and step-by-step guidance tailored for IT administrators.

Avoiding cloud utilization headaches

Unoptimized cloud computing costs can create major headaches for IT and finance teams. But Nerdio’s patented Auto-Scaling feature can reduce Azure compute and storage costs by as much as 80%. Nerdio Manager for Enterprise automatically swaps out OS disks from SSD to HHD when VMs are not running, and it automatically expands and reduces the Azure Files IOPS and sizes to meet storage needs based on usage.

Nerdio’s Auto-Scaling and automation enabled the IT organization at Canada’s Equitable Bank to turn virtual machine schedules on and off as AVD usage and demand fluctuated. This resulted in 74% savings on compute per month compared to the previous environment. The bank now manages approximately 1,900 end users with Nerdio, seamlessly connecting employees across Canada with a cloud-based solution.

With Nerdio’s storage optimization of Azure Files and Azure NetApp Files, Log Analytics, OS disk swapping, Advanced Predictive Drain Mode, and multiple scaling triggers, organizations can closely match the Azure infrastructure to user demand in real time. This ensures maximum efficiency in utilizing AVD’s consumption-based pricing.

Nerdio can fully manage desktop images, utilizing Scripted Actions to easily enable the creation of images in less than 30 minutes and install the software that is needed without having to log onto the VM. This avoids manual image creation or utilizing DevOps, Packer, or some other complicated method.

An automated approach to provisioning AVD with Nerdio ensures consistency and reliability across deployments, mitigating the risk of errors and ensuring a seamless user experience.

In 2021, the University of North Florida (UNF) undertook a major strategic initiative to digitally transform and migrate to the cloud to better serve the student body, faculty, and staff. When beginning the transition to Azure Virtual Desktop, they found that it was more agile and improved performance—but the native capabilities were limited, making it challenging to manage and automate, so it turned to Nerdio Manager for Enterprise to improve the AVD environment.

“Unlike with our previous virtualization solution, I no longer have to sit around to see if machines are being over-utilized,” said Michael Holmes, Assistant Director of Endpoint Management at UNF. “I don’t need to move users around or re-image my hosts in the middle of the day. Nerdio handles it all gracefully, ensuring a seamless user experience.”

Microsoft Azure Virtual Desktop ensures a uniform and efficient user experience for employees and contractors alike, whether they’re working remotely, on the move, or within offices. But if you don’t have the resources or the desire to master how to optimize AVD performance and utilization, learn more about how Nerdio can provide the expertise and support services needed to ensure a seamless experience for end users and maximize the value of your investment.

What do the Boll Weevil, Cavendish bananas, and the recent Windows/CrowdStrike fiasco all have in common? They’re all economic disasters that occurred because far too many people put their trust in a monoculture.

I’m serious.

Indeed, I warned you years ago about Windows when I first mentioned Mr. Boll Weevil. After the Civil War, the US South became more dependent on cotton production than ever before to make money in the region in the late 19th century. 

Then, in the mid-1890s, Boll Weevils arrived and almost destroyed the cotton crop and the South’s economy. With only one cash crop, the South was vulnerable to this one bug as it destroyed crops — and hundreds of thousands of people’s livelihoods.

Today, the banana you get for breakfast every morning is in danger. Almost half of the bananas in your grocery store are Cavendish bananas, which are being devastated by the Fusarium wilt. This fungal disease might well drive Cavendishs into extinction; then what will you do for your banana split??

And now, we come to Windows and the disaster that unfolded on Friday. (This time ,Microsoft’s poor security wasn’t to blame for the problem for once.) The proximate “credit” for the ongoing mess goes to CrowdStrike, which released a truly awful security update to its Falcon Sensor program, which scans Windows computers for intrusions and signs of hacking. 

All it took was a single faulty content update — not really even code — to fry Windows computers from Afghanistan to Zimbabwe. Just as bad, undoing the problem requires manual fixes to every computer, PC by PC. IT staffers will be up to all hours over the weekend and beyond deleting the fouled-up data file and the system reference that called it. (Those overworked IT folks will be happy learn the CEO has apologized.)

Why was the update so awful? Why did it cause hundreds of millions — perhaps billions — of PCs around the world to crash and get locked into endless reboot loops? Because just like cotton and Cavendish bananas, we depend all too much on a single product: Windows.

I told you so; let me reiterate: “Windows bad, Linux good.

I wasn’t surprised to learn that, according to the folks at QR Code Generator, “Analysis of Google search data has revealed that online searches for “Microsoft alternative,” “MacOS,” “Debian,” “Ubuntu,“ and “Linux” soared by up to 290% worldwide during Microsoft’s global IT outage.”

Once more, and with feeling, I suggest you seriously consider switching your computers from Windows to Linux and contemplate moving from PCs to Macs. 

Leaving that smart-aleck attitude aside, we really do depend too much on Windows, period. If we were all using Macs or Linux, we might have encountered the same problem, but it’s less likely. Linux is more secure by design, but it’s had its security breaches, as well. It just doesn’t have them nearly as often as Windows does. 

To a lesser degree, it’s the same story with CrowdStrike. You’re unlikely to use Falcon Sensor on your home PC. Still, according to the business data analysis company  6sense.com, CrowdStrike is the No. 1 business endpoint security company with more than 3,500 customers. 

If you’re playing the “What Happened to Whom Game at home,” that’s about one in four companies that use endpoint security. These tend to be big companies. For example, my friends stuck in airports on Friday kept telling me it included most of the major airlines and all the airport flight scheduling screens. It was not a good day to fly. 

Or, to buy groceries, or get paid, or… you get the idea. I’m sure you have your own story. 

Me? Yes, I was fine with all my Linux desktops and servers…, as long as I stayed in my home/office. 

That’s the problem, you see. In this interconnected world of ours, even open-source fans like me are affected when Windows goes down. We all are.

Windows has become a single point of failure for the world’s IT infrastructure. We really must move on, not to a world where everyone uses Macs or desktop Linux, but one where we use a multitude of different operating systems.

Yes, this will be a pain. But at least this way, we won’t have days like Friday when all too much of the day-to-day technology we depend on goes down. 

CrowdStrike CEO has apologized to the company’s customers and partners for crashing their Windows systems, and the company has described the error that caused the disaster.

“I want to sincerely apologize directly to all of you for today’s outage. All of CrowdStrike understands the gravity and impact of the situation,” CrowdStrike founder and CEO George Kurtz wrote in a blog post on the company’s website titled “Our Statement on Today’s Outage.”

He reiterated the company’s earlier message that the incident, which brought down computers around the world on Friday, July 19, was not the result of a cyberattack.

From the editors of Computerworld, this enterprise buyer’s guide helps IT staff understand what the various desk-booking software options can do for their organizations and how to choose the right solution.

CrowdStrike has admitted to pushing out a bad software update, causing many Windows machines running the affected software to crash. The problem, apparently affecting its Falcon platform, brought down servers at airlines, locked up computers at banks, and hurt healthcare services.

“CrowdStrike is actively working with customers impacted by a defect found in a single content update for Windows hosts,” the company said Friday in a post to its blog titled “Statement on Windows Sensor Update.”

Mac and Linux versions of the software are unaffected, and the incident was not the result of a cyberattack, it said.

Apple has confirmed recent claims that it might have used subtitle data from YouTube videos to train one of its artificial intelligence (AI) tools, but says the tool is not used in Apple Intelligence.

Apple confirmed a report from Proofnews that it had used this YouTube data to train one of its models. The company explained that it did so to train the open-source OpenELM models released earlier this year. The information was included within a larger collection maintained by the EleutherAI non-profit company that supports AI research.

Apple used YouTube once, but not now

However, Apple told 9to5Mac that models trained using that information don’t power any of its own AI or machine learning tools, including Apple Intelligence. This was a research project originally created by Apple’s AI teams and then shared, including via the company’s own Machine Learning Research site.

What’s important is that it shows the extent to which Apple wants to be seen as keeping its promise that Apple Intelligence models are trained on licensed data.

But that’s not the big picture. As mentioned earlier in the week, Apple Intelligence does also train its models using “publicly available data collected by our web-crawler.”That admission reflects the extent to which tech companies are using information published online to create new AI products from which they subsequently profit.

Making public data private

The issue is that by turning other people’s creative works into data, and then profiting from that data, tech firms aren’t playing fair. 

Speaking to Proofnews, Dave Farina, the host of “Professor Dave Explains,” put it this way: “If you’re profiting off of work that I’ve done [to build a product] that will put me out of work or people like me out of work, then there needs to be a conversation on the table about compensation or some kind of regulation.”

To some extent, the focus on YouTube data distracts from that critical argument, which is that the generative AI (genAI) tools coming into common use today are likely to have been trained by information created by humans and shared online. That’s the kind of information picked up by webcrawlers, including Apple’s.

But data quality is a real issue here, and the search for the best data inherently means that the best data sources are the highest octane of fuels to power training AI.

The drive for quality means content is king

Consider just two of the challenges AI researchers face.

• Automated data grading systems might reject old, out-of-date, or false information, but some still gets through, which is why AI systems so often develop hallucinations (the current descriptor for fake information) or exhibit questionable morality (racist or gender-biased language).
• Data also has a finite lifespan. Facts can and do change over time, and maintaining high-quality data is an essential bulwark against the classic “garbage out” generated by irrelevant information, or high-grade information that becomes irrelevant over time.

What this means is that in their quest for high-quality information, AI companies inevitably seek high-quality data sources. When you translate that into activity picked up from the open public web, that in itself implies that creatives currently battling against tech firms for compensation for use of their material in training AI systems have a good point.

Because the best and most current information they create is worth something, both to the creators, those who consume it, and also to the people who own and train the machines that harvest their data from it. Indeed, given that AI by its nature becomes a tool directly available to everyone and across every supported language, it seems plausible to think the value of that information might actually grow once it is used to train an AI model.

So, while Apple might not be using YouTube data for its Apple Intelligence models, it will be using other data curated across the public web. And while Apple might at least try to avoid using data it should not exploit this way — and is honest enough to have responded to the current YouTube controversy — not every AI firm does the same. And once the machine is trained it cannot be untrained.  

Please follow me on Mastodon, or join me in the AppleHolic’s bar & grill and Apple Discussions groups on MeWe.

When generative AI popularity and marketing hype went into overdrive last year, just about every enterprise launched a wide range of genAI projects. And for various reasons, very few of them delivered the kind of return on investment that CEOs and board members had expected.

That meant that 2024 has become the year of AI postmortems and recriminations about why projects went sour and who was to blame. What can IT leaders do now to make sure that genAI projects launched later this year and throughout 2025 fare better? Experts are suggesting a radical rethinking of how ROI should be measured in genAI deployments, as well as the kinds of projects where generative AI belongs at all.

“We have an AI ROI paradox in our sector, and we have to overcome it,” said Atefeh “Atti” Riazi, CIO for media enterprise Hearst, which reported $12 billion in revenue last year. “Although we have [years of experience] measuring the ROI for IT on lots of other projects, AI is so disruptive that we don’t really yet understand its impacts. We don’t understand the implications of it long term.”

When boards push down genAI mandates — and LOBs go rogue

After OpenAI captured the attention of the industry when consumer fascination with ChatGPT surged in early 2023, Conor Twomey observed a “wave of euphoria and fear that swept over every boardroom.” AI vendors tried to take advantage of this euphoria by marketing their own version of FUD (fear, uncertainty, and doubt), said Twomey, head of AI strategy at data management firm KX.

“Every organization went down the same path and said, ‘We don’t know what this thing is capable of.’”

That sparked a flood of genAI deployments ordered from boards of directors and, to a lesser extent, CEOs. This was happening to an extent that has not been seen since the early days of web euphoria around 1994.

“That was something different with generative AI, where a lot of the motion came top-down,” said Rajiv Shah, who manages AI strategy for Snowflake, a cloud data storage and analytics service provider. “Deep learning, for example, was certainly hyped up, but it didn’t have the same top-down pushing.”

Shah says this top-down approach colored and often complicated the traditional requirements for ROI analysis prior to major rollouts. Little wonder that those rollouts failed to meet expectations.

And mandates from above weren’t the only source of pressure IT leaders faced to push through genAI projects. Many business units also brought AI ideas to IT, and IT pointed out why they would be unlikely to be successful. And those departments often said, “Thanks for the input. We are doing it anyway.”

Such projects tend to shift focus away from companies’ true priorities, notes Kelwin Fernandes, CEO at AI consultant NILG.AI.

“I see genAI being applied in non-core processes that won’t directly affect the core business, such as chatbots or support agents. These projects lack support and long-term engagement from the organization,” Fernandes said. “I see genAI not bringing the promised ROI because people moved their priorities from making better decisions to building conversational interfaces or chatbots.”

Inflated expectations, underestimated costs

Early genAI apps often delivered breathtaking results in small pilots, setting expectations that didn’t carry over to larger deployments. “One of the primary culprits of the cost versus value conundrum is lack of scalability,” said KX’s Twomey.

He points to an increasing number of startup companies using open-source genAI technology that is “sufficient for introductory deployments, meaning they work nicely with a couple hundred unstructured documents. Once enterprises feel comfortable with this technology and begin to scale it up to hundreds of thousands of documents, the open-source system bloats and spikes running costs,” he said.

“Same goes for usage,” he added. “When genAI is inserted into a workflow ideal for a subset of users and then exponentially more users are added, it doesn’t work as hoped.”

Patrick Byrnes, formerly senior consultant for AI at Deloitte and now an AI consultant for DataArt, attributes some of the inflated ROI expectations for generative AI projects to the impressive performance delivered by the earliest genAI applications.

“If you go into Gemini or ChatGPT and ask it something basic, you can get an incredible response right away,” he said. Expecting similar results on a larger scale, “some enterprises did not start small. Right out of the gate, they went with high-impact customer facing efforts.”

Indeed, many of the ROI shortcomings with genAI deployments are a result of executives not thinking through the rollout implications sufficiently, according to an executive in the AI field who asked that her name and affiliation not be used.

“Automation driven by AI leads to productivity gains, but often the cost to enable it is overlooked,” she said. “Enterprises focus on model development, training, and system infrastructure but don’t accurately account for cost of data prep. They spin up massive data sets for AI, but small errors can make it useless, which also leads employees to mistrust outputs, leading to costs without ROI.”

Another overlooked factor, she noted, is that many AI vendors are currently focused on customer acquisition, keeping costs down in the short term. “Then they will ratchet up prices with an eye toward profitability, which will lead to higher costs for enterprise users in the future.”

Those costs are not likely to get meaningfully better by 2025. IDC noted that the costs with generative AI efforts are extensive.

“Generative AI requires enormous levels of compute power. NVIDIA’s workhorse chip that powers the GPUs for datacenters and the AI industry costs ~$10,000 per chip,” the analyst firm said in a September 2023 report. “Operational costs are in the range of $4 million to $5 million monthly, and businesses expect model training costs to exceed $5 million. Added to this are electricity costs and datacenter management.”

The hallucination challenge

On top of all this is the fact that genAI periodically hallucinates, meaning that the system makes things up. That will deliver a bitter surprise if the company is trusting it to analyze critical data in healthcare, finance, or aerospace — and even if it is simply relying on genAI to accurately summarize what happened during a meeting.

For business managers who are used to trusting the numbers generated by a spreadsheet projecting revenue growth, that can be unsettling. Those executives are used to the projections failing because an employee’s assumptions turned out to be too optimistic, but they are not used to Excel lying about the mathematical result of 800 numbers being multiplied.

And it cuts into ROI because all generative AI output must be closely fact-checked by a human, erasing many of the perceived productivity gains.

Hearst’s Riazzi sees the genAI hallucination issue as temporary. “Hallucinations do not bother me. Eventually, it will address itself,” she said.

More importantly, she argues that business simply needs to apply the same supervision and oversight to genAI that it has for decades with its human employees, stressing that “people hallucinate as well” and coders have been known to write “buggy code.”

“Human error is already a big issue in medicine and patient care,” Riazzi said. “There is a lot of bad data out there, but there is no difference [in managing hallucinations] from what we are already doing today. We see a lot of data cleansing going on.”

NILG.AI’s Fernandes is doubtful that genAI hallucinations will ever go away, but he says that shouldn’t necessarily be a dealbreaker for any application. It is simply a matter of enterprises adjusting their thinking to deal with an imperfect reality, something they already have experience doing.

“We have quality assurance to reduce production errors, but errors still exist, and that’s why we have return policies and warranties. We use the QA process as a fallback plan of the factory errors and the warranty as a fallback plan of the QA,” he said. “All those actions reduce the probability of failure to a certain point. They can still exist; we have learned to do business with those errors. We need to understand — on each application — what the right fallback action is for an AI error.”

Looking for ROI in all the wrong places

Even when genAI succeeds, its results are sometimes less valuable than anticipated. For example, generative AI is a very effective tool for creating information that is generally handled by lower-level staffers or contractors, where it is simply tweaking existing material for use in social media or e-commerce product descriptions. It still needs to be verified by humans, but it has the potential for cutting costs in creating low-level content.

But because it often is low level, some have questioned whether that is really going to deliver any meaningful financial advantages.

“Even before AI, the market for mediocre written and visual content was already fairly saturated, so it’s no surprise that some enterprises have discovered there is limited ROI in similar mediocre content generated by AI,” said Brian Levine, a managing director at consultant Ernst & Young.

What ROI should look like for enterprise genAI

KX’s Twomey questioned whether many senior enterprise executives have a realistic handle on what ROI should mean in a generative AI rollout, especially in the first year where it is mostly an experiment rather than a traditional deployment.

“Enterprise deployment of genAI has slowed down — and will continue to do so — as enterprises experience an increase in costs that exceeds the value they are getting,” Twomey said. “When this happens, it tells me that enterprises aren’t understanding the ROI and they’re not appropriately controlling TCO.”

And therein lies the conundrum: How can executives appropriately control the total cost of ownership and appropriately interpret the return on investment if they have no idea what either should look like in a generative AI reality?

This gets even more difficult when secondary ROI factors are considered, such as market and customer/prospect perceptions, Twomey points out.

“This complexity with transiting — and scaling — AI workflows in production has been prohibitive for many enterprise deployments,” he said. “The repercussions are clear losses in time, money, and effort that can also result in competitive disadvantages, reputational damage, and stalled future innovation initiatives.”

It may even be premature to measure ROI monetarily for genAI. “The value for enterprises today is to practice, to experiment,” said DataArt’s Byrnes. “That is one of the things that people don’t really appreciate. There is a strong learning component to all of this.”

Focusing genAI

But while experimentation is important, it should be done intelligently. EY’s Levine notes that some companies are inclined to trust generative AI too much when it comes to methodology, allowing the software to figure out how to obtain the desired information. 

Consider the example of a large and growing retail chain that turned to genAI to figure out the best locations for its next 50 stores. Given insufficient guidelines, the AI went off the rails and returned completely unusable results, according to inside sources.

Instead of simply telling the AI to make recommendations for the best places to launch stores, Levine suggests that the retailer would be better served by coding very extensive and very specific lists of how it currently evaluates new locations. That way, the software can follow those instructions, and the chances of it making errors is somewhat reduced.

Would an enterprise ever tell a new employee, “Figure out where our next 50 stores should be. Bye!”? Unlikely. The business would spend days training that employee on what to look for and where to look, and the employee would be shown lots of examples of how it had been done before. If a manager wouldn’t expect a new employee to figure out how to answer the question without extensive training, why would that manager expect genAI to fare any better?

Given that ROI simply means value delivered minus cost, the best way to improve value is to increase the accuracy and usability of the answers provided. Sometimes, that means not giving genAI broad requests and seeing what it chooses to do. That might work in machine learning, but genAI is a different animal.

To be fair, there absolutely are situations where it makes sense to set genAI loose and see where it chooses to go. But for the overwhelming majority of situations, IT will see far better results if it takes the time to train genAI appropriately.

Reining in genAI projects

Now that the initial hype over genAI has died down, it’s important for IT leaders to protect their organizations by focusing on deployments that will bring true value to the company, say AI strategists.

One suggestion for trying to better control generative AI efforts is for enterprises to create AI committees consisting of specialists in various AI disciplines, Snowflake’s Shah said. That way, every single generative AI proposal originating anywhere in the enterprise would have to be run by this committee, who could veto or approve any idea.

“With security and legal, there are so many things that can go wrong with a generative AI effort. This would make executives go in front of the committee and explain exactly what they wanted to do and why,” he said.

Shah sees these AI approval committees as short-term placeholders. “As we mature our understanding, the need for those committees will go away,” he said.

Another suggestion comes from NILG.AI’s Fernandes. Instead of flashy, large-scale genAI projects, enterprises should focus on smaller, more controllable objectives such as “analyzing a vehicle’s damage report and estimating costs, or auditing a sales call and identifying if the person follows the script, or recommending products in e-commerce based on the content/description of those products instead of just the interactions/clicks.”

And instead of implicitly trusting genAI models, “we shouldn’t use LLMs on any critical task without a fallback option. We shouldn’t use them as a source of truth for our decision-making but as an educated guess, just like you would deal with another person’s opinion.”

More by Evan Schuman:

• Renegade business units trying out genAI will destroy the enterprise before they help
• AI managing AI that is monitoring AI: What could possibly go wrong?
• US makes new move to rein in China’s advanced chip manufacturing
• GenAI might be the least-trustworthy software that exists. Yet IT is expected to trust it.
• Privacy policies have gone insane. Doubt it? Consider Instacart

Anthropic released an iOS version of its popular chatbot Claude for the iPhone in May; now, it’s time for an Android version.

The artificial intelligence (AI) company announced the Android iteration on Tuesday.

With the help of Claude, users can have conversations in a number of languages, including English, German, French, Spanish and Italian. The new app reportedly can be used with all subscriptions, which includes Pro and Team. Business users have the option to sign up for a monthly subscription that costs roughly $31 per user. The minimum number of users that can be registered is five.

Users who can’t download the app from Google Play or the App Store, can still access Claude via the web at claude.ai .