COMMISSIONED: Being stuck in the middle is no fun.

Just ask the titular character Malcom, of the TV series “Malcolm in the Middle,” (2000-2006) who struggles to stand out among his four brothers. In the earlier sitcom, “The Brady Bunch,” (1969-1974) Jan Brady feels overshadowed by big sister Marcia and younger sis Cindy.

Polite (or impolite) society has a name for this phenomenon, which describes the challenges children sandwiched between younger and elder siblings feel within their families: Middle child syndrome.

Reasonable minds differ on the legitimacy of middle child syndrome. Is it real or perceived and does it matter? Even so, it can be hard to compete with siblings – especially brothers or sisters who enjoy the lion’s share of success.

The middle children of the global economy

As it happens, the global economy has its own middle children in the form of small- to medium-sized businesses, which find themselves competing with larger enterprises for talent, capital and other vital resources.

Yet like their larger siblings, SMBs must innovate while fending off hungry rivals. This dynamic can prove particularly challenging as SMBs look to new technologies such as generative AI, which can be resource intensive and expensive to operate.

No organization can afford to overlook the potential value of GenAI for their businesses. Seventy-six percent of IT leaders said GenAI will be significant to transformative for their organizations and most expect meaningful results from it for within the next 12 months, according to recent Dell research.

Fortunately, SMBs wishing to capitalize on the natural language processing prowess of GenAI can do so – with the right approach: Using a small language model (SLM) and a technique called retrieval augmented generation (RAG) to refine results.
You may have noticed I called out an SLM rather than a large language model (LLM), which you are probably more accustomed to reading about. As the qualifiers imply, the difference between the model types is scale.

LLMs predict the next word in a sequence based on the words that have come before it to generate human-like text. Popular LLMs that power GenAI services such as Google Bard and ChatGPT feature hundreds of billions to trillions of parameters. The cost and computational resources to train these models is significant, likely putting building bespoke LLMs out of reach for SMBs.

SMBs have another option in building small language models (SLMs), which may range from a hundred million to tens of billions parameters and cost less to train and operate than their larger siblings.

SLMs can also be more easily customized and tailored for certain business use cases than LLMs. Whereas LLMs produces long form content, including whole software scripts and even books, SLMs can be used to build applications such as chatbots for customer service, personalized marketing content such as email newsletters and social media posts and lead generation and sales scripts.

Even so, whether you’re using an LLM or an SLM, GenAI models require enough computational resources to process the data, as well as data scientists to work with the data, both of which may be hard for SMBs to afford. And sure, organizations may use a pre-trained model but it will be limited by the information it knows, which means its accuracy and applicability will suffer.

RAG fine-tunes models with domain knowledge

Enter RAG, which can add helpful context without having to make big investments, thus democratizing access for SMBs. RAG retrieves relevant information from a knowledge repository, such as a database or documents in real time, augments the user prompt with this content and feeds the prompt into the model to generate new content. This helps the model generate more accurate and relevant responses for the information you wish your model to specialize in.

For example, at Dell we show organizations how to deploy RAG and Meta’s LLama2 LLM to retrieve domain-specific content from custom PDF datasets. The output was used to show how an organization might theoretically use RAG and an LLM to train a help-desk chatbot.

SMBs can use an SLM with RAG to build a more targeted and less resource-intensive approach to GenAI. Effectively, the combination affords them a very accurate tool that delivers more personalized information on their company’s data – without spending the time and money building and fine-tuning a custom model.

Getting started with RAG may seem daunting to SMBs but organizations can repurpose a server, a workstation or even a laptop and get started. They can pick an open-source LLM (such as LLama2) to begin the process. Dell calls this the GenAI easy button.

That way organizations can bring the AI to their data, keeping control of their sensitive corporate IP while freeing up IT resources as they innovate.

SMBs play an important role in the economy by contributing to innovation. Yet too often they’re relegated to Malcom or Jan status – the oft-underestimated and neglected middle children of the global economy.

By combining the right approach and technology tools, SMBs can leverage GenAI to accelerate innovation, enabling them to better compete and woo new customers – rather than feeling lost in the middle of the corporate pack.

To learn more, visit dell.com/ai.

Brought to you by Dell Technologies.

Commissioned: Generative AI runs on data, and many organizations have found GenAI is most valuable when they combine it with their unique and proprietary data. But therein lies a conundrum. How can an organization tap into their data treasure trove without putting their business at undue risk? Many organizations have addressed these concerns with specific guidance on when and how to use generative AI with their own proprietary data. Other organizations have outright banned its use over concerns of IP leakage or exposing sensitive data.

But what if I told you there was an easy way forward already sitting behind your firewall either in your datacenter or on a workstation? And the great news is it doesn’t require months-long procurement cycles or a substantial deployment for a minimum viable product. Not convinced? Let me show you how.

Step 1: Repurpose existing hardware for trial
Depending on what you’re doing with generative AI, workloads can be run on all manner of hardware in a pilot phase. How? There are effectively four stages of data science with these models. The first and second, inferencing and Retrieval-Augmented-Generation (RAG), can be done on relatively modest hardware configurations, while the last two, fine-tuning/retraining and new model creation, require extensive infrastructure to see results. Furthermore, models can be of various sizes and not everything has to be a “large language model”. Consequently, we’re seeing a lot of organizations finding success with domain-specific and enterprise-specific “small language models” that are targeted at very narrow use cases. This means you can go repurpose a server, find a workstation a model can be deployed on, or if you’re very adventurous, you could even download LLaMA 2 onto your laptop and play around with it. It’s really not that difficult to support this level of experimentation.

Step 2: Hit open source
Perhaps nowhere is the open-source community more at the bleeding edge of what is possible than in GenAI. We’re seeing relatively small models rivaling some of the biggest commercial deployments on earth in their aptitude and applicability. The only thing stopping you from getting started is the download speed. There are a whole host of open-source projects at your disposal, so pick a distro and get going. Once downloaded and installed, you’ve effectively activated the first phase of GenAI: inferencing. Theoretically your experimentation could stop here, but what if with just a little more work you could unlock some real magic?

Step 3: Identify your use cases
You might be tempted to skip this step, but I don’t recommend it. Identify a pocket of use cases you want to solve for. The next step is data collection and you need to ensure you’re grabbing the right data to deliver the right results via the open source pre-trained LLM you’re augmenting with your data. Figure out who your pilot users will be and ask them what’s important to them – for example, a current project they would like assistance with and what existing data they have that would be helpful to pilot with.

Step 4: Activate Retrieval-Augmented-Generation (RAG)
You might think adding data to a model sounds extremely hard – it’s the sort of thing we usually think requires data scientists. But guess what: any organization with a developer can activate retrieval-augmented generation (RAG). In fact, for many use cases this may be all you will ever need to do to add data to a generative AI model. How does it work? Effectively RAG takes unstructured data like your documents, images, and videos and helps encode them and index them for use. We piloted this ourselves using open-source technologies like LangChain to create vector databases which enable the GenAI model to analyze data in less than an hour. The result was a fully functioning chatbot, which proved out this concept in record time.

_{Source: Dell Technologies}

In Closing

The unique needs and capabilities of GenAI make for a unique PoC experience, and one that can be rapidly piloted to deliver immediate value and prove its worth to the organization. Piloting this in your own environment offers many advantages in terms of security and cost efficiencies you cannot replicate in the public cloud.

Public cloud is great for many things, but you’re going to pay by the drip for a PoC, it’s very easy to burn through a budget with users who are inexperienced at prompt engineering. Public cloud also doesn’t offer the same safeguards for sensitive and proprietary data. This can actually result in internal users moving slower as they think through every time they use a generative AI tool whether the data they’re inputting is “safe” data that can be used with that particular system.

Counterintuitively, this is one of the few times the datacenter offers unusually high agility and a lower up front cost than its public cloud counterpart.

So go forth, take an afternoon and get your own PoC under way, and once you’re ready for the next phase we’re more than happy to help.

Here’s where you can learn more about Dell Generative AI Solutions.

Brought to you by Dell Technologies.

COMMISSIONED: Allocating application workloads to locations that deliver the best performance with the highest efficiency is a daunting task. Enterprise IT leaders know this all too well.

As applications become more distributed across multiple clouds and on premises systems, they generate more data, which makes them both more costly to operate and harder to move as data gravity grows.

Accordingly, applications that fuel enterprise systems must be closer to the data, which means organizations must move compute capabilities closer to where that data is generated. This helps applications such as AI, which are fueled by large quantities of data.

To make this happen, organizations are building out infrastructure that supports data needs both within and outside the organization – from datacenters and colos to public clouds and the edge. Competent IT departments cultivate such multicloud estates to run hundreds or even thousands of applications.

You know what else numbers in the hundreds to thousands of components? Jigsaw puzzles.

Workloads Placement and… Jigsaw Puzzles?

Exactly how is placing workloads akin to putting together a jigsaw puzzle? So glad you asked. Both require careful planning and execution. With a jigsaw puzzle – say, one of those 1,000-plus piece beasts – it helps to first figure out how the pieces fit together, then assemble them in the right order.

The same is true for placing application workloads in a multicloud environment. You need to carefully plan which applications will go where – internally, externally, or both – based on performance, scalability, latency, security, costs and other factors.

Putting the wrong application in the wrong place could have major performance and financial ramifications. Here are 4 workload types and considerations for locating each, according to findings from IDC research sponsored by Dell Technologies.

AI – The placement of AI workloads is one of the hottest topics du jour, given the rapid rise of generative AI technologies. AI workloads comprise two main components – inferencing and training. IT departments can run AI algorithm development and training, which are performance intensive, on premises, IDC says. And the data is trending that way, as 55 percent of IT decision makers Dell surveyed cited performance as the main reason for running GenAI workloads on premises. Conversely, less intensive inferencing tasks can be run in a distributed fashion at edge locations, in public cloud environments or on premises.

HPC – high-performance computing (HPC) applications ALSO comprise two major components – modeling and simulation. And like AI workloads, HPC model development can be performance intensive, so it may make sense to run such workloads on premises where there is lower risk of latency. Less intensive simulation can run reliably across public clouds, on premises and edge locations.

One caveat for performance-heavy workloads that IT leaders should consider: Specialized hardware such as GPUs and other accelerators is expensive. As a result, many organizations may elect to run AI and HPC workloads in resource-rich public clouds. However, running such workloads in production can cause costs to soar, especially as the data grows and the attending gravity increases. Moreover, repatriating an AI or HPC workload whose data grew 100x while running in a public cloud is harsh on your IT budget. Data egress fees may make this prohibitive.

Cyber Recovery – Organizations today prioritize data protection and recovery, thanks to threats from malicious actors and natural disasters alike. Keeping a valid copy of data outside of production systems enables organizations to recover lost or corrupted due to an adverse event. Public cloud services generally satisfy organizations’ data protection needs, but transferring data out becomes costly thanks to high data egress fees, IDC says. One option includes hosting the recovery environment adjacent to the cloud service – for example, in a colocation facility that has a dedicated private network to the public cloud service. This eliminates egress costs while ensuring speedy recovery.

Application Development – IT leaders know the public cloud has proven well suited for application development and testing, as it lends itself to the developer ethos of rapidly building and refining apps that accommodate the business. However, private clouds may prove a better option for organizations building software intended to deliver a competitive advantage, IDC argues. This affords developers greater control over their corporate intellectual property, but with the agility of a public cloud.

The Bottom Line

As an IT leader, you must assess the best place for an application based on several factors. App requirements will vary, so analyze the total expected ROI of your workloads placements before you place them.

Also consider: Workload placement is not a one-and-done activity. Repatriating workloads from various clouds or other environments to better meet the business needs is always an option.

Our Dell Technologies APEX portfolio of solutions accounts for the various workload placement requirements and challenges your organization may encounter as you build out your multicloud estate. Dell APEX’ subscription consumption model helps you procure more computing and storage as needed – so you can reduce your capital outlay.

It’s true: The stakes for assembling a jigsaw puzzle aren’t the same as allocating workloads in a complex IT environment. Yet completing both can provide a strong feeling of accomplishment. How will you build your multicloud estate?

Learn more about how Dell APEX can help you allocate workloads across your multicloud estate.

Brought to you by Dell Technologies.

Commissioned: It’s not a revolutionary statement to say data has value – how many times have we heard it’s the new oil? But what might not be readily apparent is how much. Up until this point, the vastness of data – and lack of tools to efficiently parse it – has made it almost impossible to analyze at scale. But that’s all changing thanks to generative AI (GenAI), as well as broader AI breakthroughs on the horizon. The bigger question is, how do we measure it? How do we begin to understand it? What examples can we point to that show exactly how massive the value of data has become?

The good news is the internet, possibly the largest source of data value on earth, provides a great case study in data, its usage, and its exponentially growing value. Let’s take a look at why the internet might be the most visible and timely battleground to analyze and learn just how critical data will be in this new AI era.

The ebb and flow of the internet

We started with links and search engines. Early portals gave way to Google search dominance – a page with a single search bar that took you where you wanted to go. This soon gave way to social media platforms that brought your attention back to centralized hubs where you could scroll through curated lists of content that were brought to your feed. In an effort to create freedom from platforms and their algorithms and democratize access and ownership of digital content, we saw the rise of Web3 and the promise of the internet moving back towards decentralization. However, the Web3 movement relied on advanced technical skills and a trust in a broad community that had some high-profile challenges in recent years.

Could GenAI be the next iteration of the Web? A Web4?

GenAI has burst onto the scene and has become a sensation that every organization is now looking at. It’s changing how developers and ops folks are working. Every organization is rushing to explore how it can impact their business, touted in many areas. Let’s unpack how it changes the game for the internet.

GenAI offers ease of use. It’s why it’s captured the attention of users and organizations everywhere, and why it offers the potential to reshape the internet. Because GenAI suggests a return to a centralized web consumption experience, it’s not surprising that search engines are jumping in on this trend and trying to bolster their positions. Simply put, it’s doing something Web3 never did: making it much easier for users to get to what they want.

Here’s how it works: instead of going to a recommended destination where they must identify the answer themself, the user simply asks the GenAI application a question and the answers are brought to them in a conversational way. This is a potentially earth-shattering level of change to the internet – for the user to be able to interrogate the data and land on exactly what they were looking for is very elusive. This suggests the new Web4 era will be centered around personalization and the ability to interact with data similar to how we interact as humans. It is also creating the battle lines for what the future of the internet could look like, by bypassing the existing players and their platforms and surfacing up results in conversational responses.

GenAI training and internet scraping

When building and training a foundation model for GenAI, data quantity and quality is critical to achieving the best outcomes. As such, the internet was one of the earliest places that many of these AI models looked to. Where else could you get so much data from so many active participants? Plus, it was data that was freely available and, in many cases, the users of these platforms were actively giving their data away. But it gets murky because data ownership and whether there were intellectual property protections in play still is not readily understood nor legally tested.

Add to that many of the early players in this space were startups with a “move fast and break stuff” mentality, and AI and the internet very much is the wild west. We’ve seen early battlegrounds drawn by companies like Getty Images, who see GenAI images that pull from their archives as derivative works and are arguing these tools are more like Napster than something novel and new. But perhaps an area that is sparking the biggest seismic shifts is in social media, where typically, the platforms are granted some level of access and assigned rights via terms of service and offer a free platform as a result. The challenge here is that many had built extensions to their platforms using APIs or allowed unfettered access to users, and now they see GenAI as a massive threat to their efforts and valuation.

Social media strikes back

Recently, we’ve seen many social media companies go on the offensive, restricting access to GenAIs and leaving their users in the crossfire. It’s not hard to see why this is occurring; these AIs can collect and consume a sea of content and provide a contextual search of that content that is completely personalized.

What a value proposition for an audience. With things like GitHub Copilot, a simple GenAI prompt can surface documentation or code snippets in seconds. Stack Overflow, Reddit and Twitter have all moved to start charging for API access to their site’s content. It makes sense; if a perceived competitor is gathering and using all your data to gain a competitive advantage, why wouldn’t you seek compensation or limit access? Reddit has also recently moved to restrict third-party apps, and interestingly, that has sometimes put them at odds with their community moderators.

Then there’s Twitter. Over the 4th of July weekend, the social media platform began to temporarily limit user access to its content. We’re also beginning to see some of this battle spill over into other web properties, such as news outlets and GenAI powered search defeating paywalls. In a world where content and data become products themselves, we will continue to see this tug of war. Decisions are being made that are shaking the internet to its very foundation, and the sole reason for any of this is just how precious this data is.

What the internet can tell you about your own data

When thinking about your enterprise or business data versus what might commonly be found on the internet, consider this: your data’s probably way more valuable. We’re seeing tech behemoths fight tooth and nail to protect their data and IP, even when it’s user-generated and potentially available in many other platforms and forms, or full of low-quality data like spam and bot networks. With GenAI, the proportion of data that holds value has increased exponentially. This means that organizations may have to re-evaluate their existing notions on data because GenAI has changed the equation.

If social media companies work this hard to limit access and bolster their competitive position with data, leaders embracing AI must take similar steps. Lean into the world of ambiguity – the use cases may not yet be obvious – but the answer will lie in the data and retention policies must change as a result. Consider where your data sits and how best to get AI to it, because data gravity will still play a role. Understand how it’s used. Data and Intellectual Property leakage must be avoided; the data itself and the potential training it offers a foundation model could potentially reduce your differentiation.

Ultimately, what we are watching now in social media will play out repeatedly in other spaces; data will be the great differentiator. In this space, that means taking a hard look at your AI solutions and ensuring you are limiting areas of exposure. We’re rapidly approaching a world where every organization is data-driven and using AI. This means it will be more important than ever to protect your most valuable asset – your data – and never outsource your core competencies.

Learn how GenAI and automation lower the bar for data center management in our latest podcast, The Great Equalizer: GenAI and AI Transforming the Data Center and learn how to bring generative AI to your organization.

Brought to you by Dell Technologies.

Commissioned: Call it an overnight success years in the making. Generative AI (GenAI) has exploded on the scene as one of the most critical areas organizations need to invest in. Decades of AI innovation paired with a natural language interface created the magic that has captured our collective attention. This is a wave for the ages. In oceanography there’s a term for these rare and unusually large phenomena: “rogue wave,” an outlier where the height is greater than twice the significant wave height. It’s an apt term for what we’re seeing with GenAI, and the challenge with these waves is they often materialize out of nowhere. Who had GenAI as the most important investment opportunity for CIOs in 2023 on their bingo card?

Catching the wave versus being caught under it

A boat can float on the water; it can also sink in it. Technology leaders everywhere, as captains of their respective boats, are rushing to stay ahead of this potentially crushing wave. The challenge is where to start? A decade ago, IT leaders worried about shadow IT projects springing up outside of governance; today they have a new concern: shadow AI.

At the same time, they’re also seeing opportunities. Forward looking and agile organizations are eagerly piloting GenAI solutions, often in the software development space where things like GitHub Copilot have really captured the mindshare of developers. What’s interesting is that while 70 percent of developers surveyed by Stack Overflow say they are already using or plan to use GenAI in coding, only 42 percent trust the accuracy of the output in their tools. This tells us we’re still relatively early in the GenAI cycle, but the rate of acceleration might keep some leaders up at night.

While there’s a broader conversation to be had on developing a winning GenAI strategy and who should be leading the charge, today we’ll focus on the role data will play and why it’s critical to your AI strategy. The good news? You can take steps to address it now.

Data’s value is only increasing with GenAI

As organizations become increasingly digital, every activity in their business is effectively exhausting data. Now not all data is equal; some data is inherently more valuable. For example, data that’s proprietary or hard to acquire, private or confidential data and, of course, data that’s useful to the business. This is where it gets tricky, though, because today very little of this data is meaningfully used. For instance, only 26 percent of ITDMs say all innovation efforts are based on data insights. This is because historically the volume of data has greatly surpassed our ability to analyze it.

Enter GenAI. Now imagine for a second that same corpus of data can be parsed with AI in minutes, if not seconds (depending on how much power you’re putting behind it). Suddenly, the long tail of data which was previously incomprehensible, has now been unlocked. This puts us in a quandary because existing logic on data retention and the understanding of how data can be used, or even its useful longevity, has now completely changed.

Getting your data house in order

As you start your GenAI journey, you must start first by getting your data house in order. Because you might not know what questions to ask your data today, but at some point you will – and if you haven’t retained that data, those answers will always elude you.  You can begin building the strategy and experimenting, but getting a handle on your data now is key. With that in mind, here are four areas to consider:

• Collection: digital end points or data creation areas are currently generating data? Are they connected? Is the data just living on an edge device somewhere never being utilized?

• Curation: Do you have a way to tag and classify data so its value and usage restrictions are known? Do you know who would benefit from having access to this data, and have you also appropriately applied labeling to understand privacy, governance, and intellectual property protections?

• Storage: Is there a process for centralizing this data and ensuring access? Have you looked into connecting different IT environments, potentially clouds? Have you established an effective data tiering strategy to align with the data’s value and lifecycle?

• Protection: Can you protect against a data loss or ransomware event? Can you ensure you’re meeting your governance and data sovereignty requirements? Do you understand the risks with various data and have you built an approach to mitigate them and secure access?

Walk before you run

I know it’s an incredibly exciting time and the way GenAI has captured our mindset has been very consumer oriented. There’s a lot of work for IT to do to catch up. In many ways it’s like in the “bring your own device” wave of innovation; it was very easy for consumers to move quickly. There’s no existing legacy infrastructure or data to consider; simply buy a smartphone and you’re up and running. But as we saw in that time period, the backend requirements to enable work from anywhere in a secure manner took years to catch up. Avoid the temptation to barrel right into something without first setting the stage for your success. While you build the strategy for the next decade, be sure to triage your existing environment as well.

Learn more about our Dell APEX portfolio of as-a-service offerings and how Project Helix helps organizations leverage generative AI to drive emerging use cases and deliver value.

Brought to you by Dell Technologies.

Commissioned: To centralize or decentralize? That was once the salient question for many enterprises formulating a strategy for deploying artificial intelligence. Whether it was nobler to support a singular AI department or suffer the slings and arrows that accompany distributed AI projects.

Tectonic shifts in technology can render such debates moot. Is that happening now, as generative AI catalyzes creativity in businesses, enabling employees to create texts, images and even software code on the fly?

Some of these experiments are useful; others not so much. There will be more failures along the path to innovation, which is littered with the bones of fallen tech projects.

What is crystal clear: Generative AI has democratized AI consumption for enterprises in ways that previous AI applications could not. The genie, in its many forms and functions, has shot out of the bottle.

The way it was

To streamline and curate our AI competency or allow projects to roam unchecked and hope for the best? It’s a fair question, with mixed approaches.

Over the years, some organizations consolidated AI capabilities in one department, often established as an AI center of excellence (COE). The COE was often composed of database engineers, data scientists and other specialists trained in querying machine learning (ML) models.

The inverse of COEs was highly decentralized. In classic, do-it-yourself fashion, business leaders experimented with some tools in the market on AI projects that might eventually foster innovation. Naturally, these projects tended to be more rudimentary than those created by COE members.

Both approaches had their pros and cons.

Centralizing AI functions afforded organizations the ability to dictate strategy and policy and control costs, thereby reducing risks. But COEs’ dedication to rigorous processes had its drawbacks. Typically, the COE received specifications and built a deliverable over several months. Over a long enough timeline, the goal posts moved. As data grew stale, the output rarely resembled the desired outcome.

Conversely, distributed AI functions granted business experts the freedom to quickly experiment and explore so that data remains fresh and current. Projects may have lead to some insights that were harder to cultivate in an AI COE, which lacked the domain expertise of a business line.

However, ad-hoc efforts often resulted in projects with no demonstrable ROI for the business. And lacking the kind of guardrails present in a COE, these efforts were often risky to the business.

How organizations approached AI varied from business to business, based on leaderships’ philosophies and appetite for risk, which are informed by internal capabilities and competencies.

Generative AI changed the paradigm

The arrival of generative AI clarifies the question of whether to centralize or distribute AI functions.

Today, average Joes and Janes interface directly with AI technologies using natural human language rather than special tools that query AI models.

Knowledge workers create cogent texts using Google Bard and ChatGPT. Graphic designers craft new image content with DALL·E and Midjourney. Software developers write basic apps with Copilot or Codeium.

Increasingly, employees layer these capabilities, creating mashups of text, graphic and code creation technologies to generate marketing content, analytics reports, or other dashboards – without the help of data experts who might spend months putting something more sophisticated together.

To be clear, generative AI cannot replace the expertise offered by AI COE specialists. It can’t teach somebody the intricacies of TensorFlow, the magic of Kafka, or other sophisticated tools used to query AI and ML models – yet.

Generative AI has democratized content creation as much as smartphones have facilitated access to information to anyone on the go – anywhere in the world.

Thinking through the implications

IT departments often hold the keys to many technologies, but generative AI is a different animal, requiring IT leaders to consider the impact of its use within the department and across the broader business.

As with technologies that are new to your business, you’ll huddle with C-suite peers on rules and guardrails to make sure the business and its employees are covered from a compliance, risk and security standpoint. And you’ll guard against potential lawsuits alleging content created by generative AI tools infringes on intellectual property rights and protections.

Yet this may be easier said than done for many organizations.

Fewer than half of U.S. executives surveyed by KPMG said they have the right technology, talent and governance to implement generative AI. Moreover, executives plan to spend the next 6 to 12 months increasing their understanding of how generative AI works and investing in tools. This is critical for the C-suite and board of directors, according to Atif Zaim, National Managing Principal, Advisory, KPMG.

“They have a responsibility to understand how generative AI and other emerging technologies will change their business and their workforce and to ensure they have sustainable and responsible innovation strategies that will provide a competitive advantage and maintain trust in their organization,”

To be sure, the democratization of generative AI means your rivals have ready access to these tools, too. Take care not to lose the name of action.

How will your organization use these emerging technologies to future proof your business and gain competitive advantages?

Learn more about our Dell APEX portfolio of as-a-service offerings and how Project Helix accelerates business transformation and unleashes productivity with trusted AI.

Brought to you by Dell Technologies.