Great Expectations

Over the last two years, artificial intelligence has captured investors’ attention as the main driving force for technology markets. Whereas debates around names like Nvidia, AMD, or Super Micro were previously limited to technology geeks and IT circles, they have now become household names as their stock prices soared and garnered media attention.

The key to the growth has been the ongoing arms race as large tech companies bet big on AI and spend aggressively on the necessary infrastructure to power these solutions. Capital expenditures at Microsoft, for example, rose 66% year-over-year to $11 billion while Alphabet’s capex practically doubled to $12 billion in the latest quarter.

The euphoria is by no means limited to technology companies either. In 2023, AI was mentioned over 30,000 times on earnings calls as CEOs and CFOs sought to weave the technology into their product roadmaps and narratives.

Below is a snapshot of IDC’s latest views in three key areas of the AI value chain:

1: AI in Semiconductors

To date, much of investors’ attention has been concentrated on the semiconductor players involved in the AI value chain – and for good reason.

In 2022, IT Infrastructure for AI semiconductors was just $42.1 billion. By 2023, IDC estimates it had risen 64% to $69.1 billion and by 2024 we expect the market to accelerate by 70% to $117.5 billion. By the end of 2027, we forecast that IT Infrastructure for AI semiconductors will reach $193.3 billion representing a 5-year CAGR of 35.7%.

The chart below shows CPUs, GPUs, FPGAs, ASICs, Analog, Memory, and other semiconductor ICs that support AI workloads across the datacenter and cloud infrastructure. It also includes communication infrastructure such as switches and routers.

While a lot of the growth to date has occurred within the datacenter for AI training, we expect the next wave of growth to come as networks continue to virtualize and as AI is infused into network infrastructure workloads. After which, we expect the edge to represent the next big opportunity as billions of connected devices at the edge will double in volume by the end of our forecast period, which will generate a large part of the data that is driving AI inferencing.

For a deeper dive into the topic, watch IDC’s 2024 Semiconductor Market Outlook webinar on demand here.

2: AI in Servers

Looking at the overall server market, we see two diverging trends playing out.

In 2023, global shipments of servers declined 19.4% to 12 million units as macroeconomic pressures continued to slow enterprise investments. However, over the same period, average selling prices or ASPs rose 37.1% to $11,376 as GPU-server growth soared from hyper-scalers and large cloud providers expanding their GPU deployments – largely driven by AI demand. The rise in ASPs offset the lower volumes leading the total market to grow 10.5% to US$136 billion.

Homing in specifically on AI servers, IDC estimates that the segment comprised approximately 23% of the total server market in 2023 and will increasingly represent a larger portion of the market going forward. By 2027, we forecast the AI server market’s revenue to reach $49.1 billion with a key assumption that GPU-accelerated server revenue will continue to increase share among all acceleration types.

“For clarity’s sake, IDC defines AI Servers as servers that run one or more of the following applications:

AI platforms: Software platforms dedicated to AI application development; most AI training occurs here.
AI applications: Applications whose prime function is to execute AI models; most AI inferencing happens in this category.
AI-enabled applications: Traditional applications with some AI functionality.

This means IDC’s definition includes servers that run AI with the support of a coprocessor, such as a GPU, an FPGA, or an ASIC, referred to as “accelerated servers”, including servers that run AI on the host processors only, referred to as “non-accelerated servers”.

3: AI in Devices

While much of the AI workloads today occur in the cloud and datacenters, there are also compelling arguments for running AI locally on devices such as PCs and smartphones. This is for three primary reasons:

• Privacy and security: Enterprises concerned with uploading sensitive data to a public cloud may instead store it locally on their own devices to retain more control.
• Cost savings: It can be more cost-effective over the lifetime of a device to run AI workloads locally to limit the need to access expensive cloud subscriptions or resources.
• Performance and latency: Running AI on-device can eliminate the round trip that workloads make between the cloud and back over networks.

AI PC Market

While the market is still relatively nascent, IDC expects AI PC shipments to begin ramping up in 2024 and become pervasive by 2027. Our Worldwide Personal Computing Device Tracker forecasts 54.2 million AI PC units shipped in 2024 representing 21% of the total PC market. By 2028, we expect this proportion to reach almost 60% of the market at 166.4 million AI PC units.

Notably, we expect that the commercial segment will lead the AI PC market growth as businesses pilot the technology and identify the most pressing use cases.

AI Smartphone Market

Most smartphones today already have some degree of AI capabilities integrated, especially around photography. What IDC defines to be “next-gen AI smartphones” are devices with a system-on-a-chip (SoC) capable of running on-device Generative AI models more efficiently leveraging a neural processing unit (NPU) with 30 Tera operations per second (TOPS) or more performance.

In 2024, our preliminary estimate is for 170 million AI smartphones to be shipped comprising about 15% of global volumes. This represents a more than 3x increase from 2023 shipments, and we expect the share of AI smartphones to continue increasing as more concrete use cases emerge.

We discuss our forecasts in more detail in our AI Devices webinar here.

Looking Ahead: AI Use Cases and Monetization

As the industry continues to evolve, AI will become pervasive across mediums and deployments. While much of the heavy lifting and training will continue to occur in the cloud, more inferencing can be expected to happen on devices and at the edge. This makes it especially critical for investors to track the ways and pace that AI is dispersing across the value chain.

Importantly, to sustain the momentum that has already occurred in capital markets, two things need to happen:

• Use cases for AI must deliver on their promises of enhanced productivity and efficiency. While increasingly fast and powerful hardware continues to be created, what solutions those new capabilities unlock and what “killer apps” eventually emerge is far more important.
• Monetization paths need to be clear and achieved at a pace that investors deem acceptable. While investors have largely been accepting of skyrocketing capital expenditures to date, increasing scrutiny will likely occur as shareholders await the uplift in revenues to assess returns on investment.

While certainly not exhaustive, below is a snapshot of current AI use cases:

To learn more on how you can make IDC an extension of your research process in making sound investment strategies, explore IDC Investment Research and sign up for the IDC Tech Investor Newsletter.

Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here.

We’re a few weeks into what I’m calling the AI PC Rally of 2024, where nearly back-to-back industry events pull back the curtain to reveal more AI capabilities and use cases to make the latest generation of PCs and phones more compelling.

So far, Google I/O and Microsoft Build have passed. In a few more weeks’ time, Computex and Apple’s WWDC beckon. We’ll revisit those when time comes (and a deeper report for clients is on the way from my teammates Tom Mainelli and Linn Huang), but a number of things have caught our device team’s eye so far.

Microsoft Build was one of the events that we were most eager to see given all of the interest around Copilot and its potential to pivot the industry. Specific to devices, the big question was not just the new features that Microsoft would unveil, but also whether their existing AI models would migrate from the cloud to the device to take advantage of NPUs unlocked by suppliers like Qualcomm.

To that end, Microsoft delivered 40 local AI models via its Windows Copilot Runtime layer, not to mention Microsoft’s own 3.3 billion parameter SLM called Phi-Silica to run locally on AI PCs. Not surprisingly, creative use cases were one major focus, as seen in the impressive Cocreator drawing app. Other use cases included live captions, Auto Super Resolution upscaling, as well as Copilot even helping users to play games like Minecraft. These won’t dramatically change our outlook for 20% of PCs this year to be AI-enabled, but the demos were a plus nonetheless.

What was more provocative was the Recall for Windows 11 feature, which perhaps has the most potential for changing user behavior by providing users with a scrollbar to easily search their PC activity including web browsing, meeting notes, and productivity files for recalling later. But it needs an allocation of at least 25GB of storage for roughly three months of screenshots, which raised privacy concerns.

Of course, privacy is one of the main reasons for running an AI model locally on the device’s NPU rather than in the cloud (and that is on top of the encryption, automatic deletion, and options to exclude applications or disable the feature altogether), but fears of exposed paper trails triggered strong responses nonetheless, especially when involving screenshots. It is worth pointing out that Rewind AI, a similar third-party app with an inclination toward Apple users, captures a user’s screen too, and yet hasn’t drawn as much scrutiny, perhaps because of its lower profile.

Another important thing to watch is how developers take advantage of the hardware and software tools at their disposal, which is one of Microsoft’s objectives for its Build conference after all. Given the rocky history of Windows on ARM, it was certainly refreshing to see native apps like Photoshop, Spotify, and Amazon Prime available. More importantly, third party browsers like Google Chrome, Firefox, and Brave are now native, playing a critical role as more applications become browser-based. Legacy applications – which can be important in enterprises in particular – can still be run through the Prism emulator, which Microsoft claims to be as efficient as Apple’s Rosetta 2. Qualcomm even told gaming developers two months ago that many games will run through emulation at full speed, although some big titles like Roblox, Valorant, League of Legends, PUBG, and Fortnite, don’t run due to anti-cheat drivers at the kernel-level.

And of course, there was a big hardware reveal that the Windows ecosystem has been eagerly awaiting in light of all of the attention that Apple’s MacBooks have been gaining in recent years. An ecosystem of OEMs will be rolling out what Microsoft deems Copilot+ PCs. It’s an odd name, but the systems look promising with an NPU capable of at least 40 TOPS as well as 16 GB RAM and 256 GB of storage, with offerings starting at $999 and shipping on June 17th.

Performance benchmarks naturally are of interest, but power efficiency via the NPU is also one of the pitches, with a range of Snapdragon X Elite proof points such as 20% better battery than the latest 15″ MacBook Air and double battery life of an Intel-based Surface Laptop 5. Qualcomm’s many OEM design wins included:

Dell offered designs spanning its Latitude, Inspiron, and XPS, which is notable given that Dell tends to lean toward Intel for its commercial-heavy customer base. Lenovo’s Yoga Slim 7x and ThinkPad T14s Gen 6, the former of which also uses a proprietary “Lenovo AI Core” chip, which is likely the LA3 that it has used on Legion gaming laptops for power efficiency.
HP and Acer both offered products that featured their own AI branding, which was obvious not only in its product naming suffixes, but also with their own logos on the products themselves. Acer’s logo is even featured on the touchpad and lights up when Copilot is activated.
ASUS launched a product in its Vivobook line, which is targeted at creators but on a more budget-friendly level than its ProArt line with with discrete GPUs. Samsung naturally leveraged its broader ecosystem by including its Knox secure enclave to share data with Galaxy phones and bundled a free 50″ TV in some geographies.
Microsoft’s own Surface Pro line for 2024 included both detachable tablet and clamshell offerings, with the former offering not only an OLED option but also a wireless Flex keyboard.

Not to be outdone, Intel talked up its upcoming Lunar Lake platform, whose NPU also does 45 TOPS and thus also can power Copilot+ PCs. We are sure to hear more about Lunar Lake at Computex next month along with archrival AMD’s Strix Point. See many of you in Taipei!

Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here.

There’s no way around it. The time to plan for AI skills and roles is now. Just a year into the GenAI hype cycle, the question is no longer whether enterprises must skill up employees for the age of AI. It’s when and how they should do it.

For leading organizations in every sector, the heat is on. According to IDC data excerpted in IDC’s Skills and the AI-Enabled Business study, some 40% of enterprise and line-of-business respondents believe their GenAI tech investments will continue in the foreseeable future. That was slightly ahead of their IT skilling investments, which 37% of respondents predicted would be the most persistent investment moving forward.

FIGURE 1 : AI and Skills for Business Growth

The two topics – GenAI and skills – are directly related. Organizations in all sectors and geographies face a widening shortage of IT skills, including skills related to security, cloud, IT service management and AI. And in addition to being the subject of skills development, GenAI technologies can and do speed training. But there’s a wider aspect for enterprises to consider, too. Organizations must make sure leaders, employees, partners and, eventually, customers become fluent with the AI and GenAI-fueled tools and processes. Soon these will be foundational to most business processes.

Amid growing IT skills shortages, the stakes are high. To remain competitive, global IT and business leaders must move to accept and deploy AI as a transformational force, one that will fully remake roles, skills requirements, and the very nature of innovation and creativity in the enterprise. This article presents three strategies business and IT stakeholders should embrace now to ready their enterprises for the age of AI moving forward.

1. Implement GenAI to Improve and Speed Training For All Skills

As the IDC AI study notes, enterprises across geographies already face a severe IT skills shortage. Now they face rising demand for AI and GenAI capabilities, too, a reality that exacerbates the substantial skill gaps so many enterprises now report.

Some 30% of IT and LOB leaders see skills and labor shortages as top risk factors for tech strategies and budgets in the coming year. In IDC’s Future Enterprise Resiliency (March 2024), 62% of IT leaders told IDC that a lack of skills had resulted in missed revenue growth objectives (IT Skills Survey, Jan. 2024).

More than 60 percent say the dearth of skills also has led to quality problems and a loss of customer satisfaction overall. And IDC predicts that, by 2026, more than 90% of organizations worldwide will feel similar pain, adding up to some $5.5T in losses caused by product delays, impaired competitiveness and lost revenue.

Notably, GenAI can help to improve IT training outcomes. In fact, more than half of IT leaders tell IDC they have already begun leveraging GenAI tools to create and update courses (42%) and analyze skill assessment interviews and transcripts (46%). Down the road, IDC expects IT training tools to add more AI and GenAI technology. That will allow for personalizing courses to the skills, roles and learning styles of employees, a development that should add up to faster and better skilling outcomes overall.

At this writing, almost every major IT training platform has either announced or delivered GenAI features to help personalize training. These features include GenAI chatbot tutors for enterprise learners, simulations to help tech staff practice problem-solving and AI-powered games and challenges for skill and learning reinforcement. Some training programs now leverage AI to adapt content to the pace, learning style and role of the learner. Digital Adoption Platform (DAP) vendors are following suit, too. Most major players in the category now or soon will offer rich AI data analytics that help enterprises identify learning patterns and bottlenecks.

2. Focus on Developing a Holistic Array of Skills

No enterprise survives on just tech skills alone. To compete in the age of AI. They also need digital business skills, leadership skills and human skills.

Likewise, employees in HR, marketing and communications need to better understand the nature and technical dependencies of new applications essential for guiding their teams. Absent of sufficient technical knowledge and critical judgement about how things function, they will end up misguiding them.

When considering skill gaps in the enterprise, be sure to account for the broad swath of skills employees will need to get the job done. Take time to map the skills each role now requires against what skills will be needed in the foreseeable future.

3. Deliver AI Training Across the Organization

In addition to focusing on the technical, leadership and human skills around AI and GenAI, enterprises should take care not to lose focus on the humans who truly power a business. In a recent 2024 IDC survey, only 36% of organizations told IDC they are mandating AI and GenAI awareness. Enterprises must do a lot better than that. Without proper support and socialization to help humans learn how to work and partner with AI systems, AI initiatives will fall short.

Start with awareness. That is the key to any successful tech implementation, but it is most critical with AI and GenAI which, understandably, can make employees nervous about losing their jobs. Implement mandatory training sessions, workshops and seminars to ensure that employees understand how AI and GenAI play into company strategy overall. They must understand how the organization intends to use such tech to enhance current roles and create new ones.

Automation, after all, isn’t new. Employees should understand that GenAI and AI simply continue and accelerate the need for upskilling and cross-skilling. While dispelling any misconceptions about AI-based automation stealing jobs, leaders should highlight exactly how AI can augment human capabilities. Frame the promise of AI in terms of reassigning rote work, which allows employees to focus on more strategic tasks.

FIGURE 3: Automation technologies impact on employees over 18 months

With AI and GenAI, enterprise leaders should create and foster a culture of trust that keeps humans in the loop. CIOs should create an environment where employees can securely experiment with AI tools, learn from their mistakes and get feedback. Make sure to set up regular team meetings where stakeholders across all functions – not only technical roles – can openly discuss AI projects and results. Transparency matters.

In addition to changing job roles, AI and GenAI technologies are already making an impact on how marketers create content, how HR reps seek out and hire new talent and how financial analysts offer loans. The list of functional use cases goes on, mandating that organizations invest in continuous learning and skill development in technology.

As AI and GenAI evolve, so too, will the skills needed to work effectively with them. CIOs should invest in continuous learning and development programs to ensure their teams keep up with the latest AI trends and technologies. The goal isn’t to turn every employee into an AI expert, of course. But each employee does need to know how and when to partner with AI, and they must do so with best practice AI ethics and compliance in mind.

By focusing on augmenting the capabilities of employees and fostering a culture of learning and AI-human collaboration, organizations can better navigate the challenges and seize the opportunities of the AI age.

Contributing author: Amy Loomis, Ph.D. – Research Director, Future of Work

Learn what matters most to your customers with IDC’s AI Use Case Discovery Tool—find out more.

IDC predicts that by 2025, the top 1000 organizations in Asia will allocate over 50% of their core IT spend on AI initiatives, leading to a double-digit increase in the rate of product and process innovations¹. Moreover, according to a recent IDC survey, 35% of Asia/Pacific Japan (APJ) organizations will increase their IT budget to fund their AI Everywhere initiatives². Both are proof of the ongoing AI revolution in the region.

As AI adoption continues to increase in the region, some organizations will find out the hard way though that they cannot simply replicate what peers or competitors are doing. Worse, some will realize they cannot buy or build just any AI solution they fancy and hope for the best.

As a framework to assess their organization’s AI Everywhere readiness, IDC drafted a framework outlining the key elements of successful AI implementations. First, it is imperative for organizations to be strategic in deploying AI in preparation for industry disruptions. Second is the importance of creating a roadmap that prioritizes use cases based on an organization’s distinct requirements. These first two are all about planning for success.

Next will be to build their enterprise on a foundation of intelligent apps, data and models to drive productivity, enhance operational efficiency, and create exceptional customer, partner, and employee experiences, among other benefits. Fourth will be the importance of having a robust digital infrastructure able to support AI workloads at scale to fully harness AI’s potential. These two are all about transforming their people, processes, technology, business models, and data readiness.

And, finally, the necessity of a rigid AI and data governance system to ensure data security, and safety and trust of their users.

In short, organizations who are already or about to embark on an AI journey will need to have a foundation of various digital innovations in place and to AI-engineer their AI adoption path before they can ensure AI creates the most significant business impact for their organizations.

It goes without saying then that tech vendors must master their customer’s AI journeys and readiness. Tech vendors must guide tech buyers every step of the way in effectively aligning their business objectives and priorities to their AI Everywhere initiatives. But, at the same time, tech vendors must also understand that each customer has unique objectives that require unique solutions. To succeed, tech vendors must be equipped to help them assess their markets better. Tech vendors should have the capabilities to demonstrate their AI solutions’ value, grow their business, create new opportunities, and elevate brand awareness.

IDC recently hosted a discussion on this topic with a group of leading global IT vendor CMOs in Singapore during a Sunrise Kopi briefing on AI Everywhere. At the event, IDC Research and IDC Custom Solutions had a lively discussion and advised them on how they can improve their AI solutions’ go-to-market strategies while aligning these to their customer’s objectives and challenges. Below are some of the key points discussed during the session:

Generate demand in a crowded market. Almost every vendor is touting an AI solution. Tech vendors need to raise their brands above the noise of the over 200 AI use cases available in the market by demonstrating the business impact of their “real AI’ solutions through effective content marketing.

Go beyond the CIO – Convincing the CEO through other decision influencer personas.  AI is more of a strategic initiative than an IT investment.  The decision-making process involves multiple personas with the need to convince the CEO and the board.  Tech vendors need to understand the priorities of these different personas and position their solutions appropriately, and not just focus on the CIO agenda.  IDC shared some of its published research on the AI investment priorities and buyer behavior of different personas across industries, and how to drive targeted marketing and enable sales teams to engage more effectively with these personas.

Communicate brand position clearly and change brand perception to an AI provider.  There is a need to cut across the hype and demonstrate who has a “real AI” solution.  We advised them on how to leverage thought leadership assets to show their understanding of the buyer journey and its corresponding issues, and to clearly demonstrate their solution’s business value and ability to generate tangible and significant business outcomes.

Shorten the decision-making process.  Although the buying decision involves multiple personas, the CEO and CIO must engage and align on the strategy.  A lot of conversations need to happen between the CEO and CIO.  IDC discussed its AI Readiness Maturity Model that helps bring all parties into a common assessment of where they are in their AI journey and aligns all stakeholders into a common decision pathway.  This tool can be used to measure partner/channel readiness to sell AI solutions as well as open an engagement opportunity for sales.

Tracking the dark funnel.  How do we track buyers who are clicking through, researching into the topic but are not picked up by the sales/marketing funnel?  IDC shared some of the TCO/ROI tools that can help tech vendors assess their customer’s environments and help draw them from the dark funnel into the sales/marketing funnel.  In addition, IDC also talked about leveraging media amplification and lead-generation services to facilitate the conversion of buyers in the dark funnel as marketing qualified leads.

In summary, there is a lot of noise in the market that muddles tech buyers’ visions as they search for suitable, legitimate, and reliable AI solutions to help them address their unique business needs. Given the right data, insights, and go-to-market strategies, tech vendors can clear tech buyer’s views if they plan, market, and sell their AI solutions well, and stand out as the preferred and trusted AI technology vendors of choice.

IDC Custom Solutions helps tech vendors enhance results with tailored insights and proven tools.

Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here.

¹ Source: IDC FutureScape: Worldwide Artificial Intelligence and Automation 2024 Predictions – Asia/Pacific (Excluding Japan) Implications
² Source: IDC Future Enterprise Resiliency and Spending Survey Wave 1 (APJ N =300)

For organizations globally, experience matters, be it the employees, customers, suppliers, partners, and the business itself.  In the digital world, an exceptional experience will create technology stickiness, while also potentially reshaping the relationship between constituents.

With great experience, an individual, regardless of the role within an organization, will look at it positively and is usually not opposed to continuing on with it. But if the experience is negative, another opportunity that is more positive will quickly overtake the negative experience, shifting away from a potential business decision to another that is more appealing. This results in lost revenue, profitability, and/or future growth for additional products and services.  

Individuals cement relationships based on the experience they have with others.  Sometimes relationships are forged immediately and other times, they take time. Regardless, relationships have been at the heart of businesses for years. Robin Sharma, an author and speaker says it well, “the business of business is relationships; the business of life is human connection.”

Human Experience in the Digital World

The IDC FutureScape: Worldwide Intelligent ERP 2024 Predictions, finds by mid-2024 30% of global organizations will take advantage of humanlike interfaces in their enterprise applications to gain more insights quickly, improving decision velocity.

At the heart of this prediction is the use of Generative AI (GenAI) and its ability to elevate the user experience to a new digital level.  If an individual no longer must search through data, spreadsheets, reports, and across enterprise applications to find information and can interact with technology differently, it changes the experience. 

A digital world experience means instead of searching through many data sets, an individual can interact with the technology in a conversational form, by inputting text, or interacting with a chatbot or digital assistance. This information can be surfaced in a matter of moments creating a likely positive experience.  This aspect requires the large language models (LLMs) that generate a human like recommendation based on the sets of data the system has been trained on.

With a multitude of data that can be tied to the systems overall, the individual will discover a wealth of information that delivers more insights, opportunities to weigh different points and elements against each other, and thoughtful considerations for better decisions. These information interactions mimic exchanges of helpful information between individuals, enabling faster and more decisive actions. Individual and organizational productivity gains are initially massive and then enable both to start to see the power of technology enabling a better experience while also improving overall performance.  And it creates a positive experience and an opportunity to recreate this methodology for the next interaction. From a technology point of view it creates more stickiness of the applications and data.

Experience Creates Differentiating Value and Relationships

Using a mobile phone or tablet, one has a different experience with the technology than they do with their enterprise software. An individual can talk or type what it needs with the mobile devices and they provide answers.  While not always correct and in some cases needing more clarification, it is quicker than using enterprise software applications.  The mobile devices represent technology platforms with a multitude of cloud software enabled by a common design, even though the software is all different.  The value is clear in the use of the software on the devices and its incorporation into ones life. 

But with current enterprise software, mostly on-premise and legacy, the experience is less than stellar and doesn’t even begin to mirror the mobile experience. Automation of simple tasks is typically lacking, importing/exporting of data and sharing it is done in a plethora of applications, and integrating the workflows and data to enable a decision point can take time.

In the digital world of on-demand experience and answers, most enterprise software falls short.  Unfortunately finding the information is dependent upon the individual organization and its technology stack, as well as preference for particular technologies.  The value derived is not only a bad experience but also one fraught with creating a better solution than the currently used technology solution.  This only leads to devaluing the current technology and moving the value to the employee that can navigate to the right solution the fastest. 

In this way the experience is a competition of legacy technology savviness to meet performance requirements.  With so many organizations still in the modernization stage with their enterprise software, the struggle towards a better experience and a better value for their employees overall continues to be unattainable. 

Innovation Creates and Reshapes the Experience

In the absence of personal relationships in the digital world, experience is a replacement to help foster a better relationship with an organization, employee, customer, supplier and partner.  The better the experience, the more replication of the process and usage of the enterprise software.  In a nutshell the experience is positive and the usage will continue to grow. 

Shaping the experience in the enterprise software world is GenAI which improves the business by streamlining operations through the automation of business tasks.  Typically workflows are long and cumbersome and don’t work from one line of business application to another nor are tasks the same for mirror types of processes such as ordering and buying.  This itself makes it hard for the organization to interact with each part of the business and come together to solve a business problem. 

GenAI can help streamline long tasks by learning the workflows and data that impact it, and reshaping it to a shorter workstream. Once this is done some of the previous processes go away or become autonomous because they have been consumed into the workflow already.  So, for instance, if I need to call customer service I might wait in a queue for awhile – to the point where they need to call me back and it could be a few days. But if I use the enterprise software that I ordered through, I can ask the question and a bot or agent will come up with several recommendations for me.  In this way, the system has been trained and can help me immediately.  When it can’t, a ‘live’ person will come on and will work with me to solve my issue.  This person will have all information in front of them and can come back to me with more information to solve my issue.  Innovation is creating a new experience for both me and the operator. 

Experience Matters

Organizations are finding experience improves productivity and metrics, from financial metrics to customer satisfaction scores, asset maintenance and production run time, employee knowledge and well-being, and brings a reduction in non-value add tasks than can be quickly automated.  As organizations mature in their usage of more experience-orchestrated business applications, they will find more shared value powered by intelligence.  At the heart of the business is experience.  The experience is reshaping the usage of enterprise applications and pushing the enterprise application vendors and their ecosystem of partners to bring more value to the organization. 

The questions to ask are few but critical:  How will this new digitally intelligent experience reshape your organization?  Do you have the right technology and services team in your business helping you unleash the heart and pulse of your organization so it can run a digitally intelligent business? 

If not, rethinking your strategy is critical to your survival in the digital world.  IDC finds those organizations that cannot unleash the power of experience with innovative technologies like AI, will be responding to disruptions and making decisions much slower than their counterparts.  Those that rely on intelligent technologies to create a better experience and operate at a much faster decision velocity will gain competitive advantage quickly and in what seems like an instantaneous moment. 

One thing is clear: the B2B buying journey is increasingly resembling its B2C counterpart. With B2B buyers spending a significant portion of their time online, the temptation for marketers to flood the digital sphere with content is strong.

However, the truth is that more content doesn’t necessarily equate to more success. The digital landscape is cluttered with content, making it imperative for marketers to differentiate their offerings with high-quality, impactful solutions. This involves leveraging advanced analytics to understand customer behavior, personalizing content to meet the specific needs of different segments, and optimizing digital channels for maximum reach and engagement.

Bombarding potential clients with irrelevant or low-quality content can be akin to sending them digital junk mail. Instead, the key lies in crafting fewer pieces of high-quality content that truly resonate with your audience.

What We Know about Successful Digital Marketing Content

Successful content must withstand scrutiny from three critical factors: search engines, social sharing platforms, and trust. To truly make an impact, content must not only be optimized for search engine visibility but also be compelling and shareable across social media channels. Communities and networks matter. Additionally, trust is paramount – content must be credible, reliable, and resonate with the audience, on a persona level, to establish and maintain trustworthiness.

So, how can B2B marketers captivate their audience amidst the noise of the digital space?

Three Proven Methods To Stand out and Forge Meaningful Connections With B2B Buyers

Know Your Customers: It all starts with understanding your audience inside and out. Take the time to dive deep into their vertical and identify their pain points. What challenges are they facing? What are their goals? By gaining a comprehensive understanding of your customers, you can tailor your content to address their specific needs and interests.

Unduplicable Content: In the current digital age, marked by an unprecedented surge in online content largely fueled by AI-generated material, the synergy between human creativity, in the form of thought leadership, and AI capabilities has never been more critical. Thought leadership, characterized by original, insightful, and forward-thinking content, distinguishes itself in a sea of generic and often repetitive information. While AI can swiftly generate vast quantities of content, it’s the human touch that infuses it with authenticity and nuanced understanding. As AI continues to streamline content creation, making it more accessible and abundant, the challenge for individuals and organizations is to produce content that is unduplicable—content that resonates on a deeper level, offering unique perspectives and fostering trust and engagement among audiences.

Leverage Rich Industry Insights: In today’s information-driven world, B2B buyers crave content that is backed by credible research and insights. By leveraging the latest industry research, you can create content that not only educates but also positions your brand as a trusted authority in your field. From white papers to interactive tools, there are various formats you can utilize to deliver valuable insights to your audience.

For example, consider partnering with a reputable research firms like IDC to develop content that is grounded in unbiased data and analysis. Here are a few successful tools we use:

IDC InfoBrief – A source for data-driven insights presented in a visually captivating format! Dive into essential audience data derived from published and primary research, all expertly analyzed by IDC analysts. Whether it’s market trends or technology analysis, each InfoBrief provides a high-level assessment of audience needs and strategic recommendations for technology adoption.

IDC Business Value White Paper – Provides an in-depth ROI and business value analysis of your company’s products and solutions based on in-depth interviews with your end-user base. Each white paper combines customer interviews, business value analysis and model and Analyst technology expertise.

Business Value Snapshot Tool – Your buyers are provided a business value assessment for investing with your solution, in a web-based and self-service calculator format. Our business value calculators are based on IDC research that ties in your solution benefits through an interactive experience which includes up to five assessment questions. The user experience concludes with a personalized Business Value Snapshot report (one-page summary) that is emailed to your prospect. The result is an interactive asset that generates real-time leads.

Content marketing services play a crucial role in building and nurturing your customer connections through the creation and distribution of valuable, relevant, and consistent content. The goal is to attract and retain a clearly defined audience, ultimately driving profitable customer action.

Content marketing services should focus on understanding the audience’s needs and interests to develop a strategic content plan that includes a mix of formats such as blogs, videos, infographics, and case studies. By providing content that educates, informs, and entertains, businesses can establish themselves as thought leaders in their industry, build trust with their audience, and support their overall digital marketing strategy. Effective content marketing services are about telling a brand’s story in a way that resonates with the target audience, encouraging engagement, and fostering long-term relationships.

To truly captivate B2B buyers in today’s digital landscape, marketers must shift their focus from quantity to quality. So, before hitting the publish button on your next piece of content, take a step back and ask yourself: does this add genuine value to my audience? If the answer is yes, you’re on the right track to success in your B2B digital marketing.

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Content Marketing Practice

In recent years, we’ve witnessed a significant shift in the technology landscape, with AI-enabled automation, data, and cloud-native architecture emerging as powerful enablers of business transformation. This evolution has transformed how SaaS applications are built and deployed. Most users will experience the power of AI-enabled automation through the SaaS applications they use daily.

Today, delivering superior user experiences is not a one-time event. Expectations change with each new set of features and capabilities and every emerging alternative to address business challenges, creating a flywheel for innovation. The ability of a SaaS provider to systematically capitalize on this flywheel effect directly impacts net revenue retention (NRR) and growth.

The financial prospects for SaaS and cloud software are incredibly promising. By 2027, global revenue from SaaS and cloud software is projected to reach a staggering $1,004 trillion, growing at a Compound Annual Growth Rate (CAGR) of 18.5%, significantly higher than the slightly more than 3% CAGR observed in perpetual licensed software. Notably, SaaS applications, which accounted for over half of total cloud software revenue in 2023, are expected to maintain their robust growth trajectory, reaching $504 billion in revenue by 2027. The assurance of business transformation, ongoing disruptions, and the rapid pace of supplier innovation all point to double-digit growth in the near future.

The Rapid Rise of Generative AI
2023 was a landmark year for AI, with GenAI dominating conversations across industries. The advent of GenAI marks a new chapter in the digital journey, necessitating adjustments in strategy, structured investment in new technologies, and the cultivation of new skills within organizations. GenAI is poised to significantly influence where and how investments in AI are made, with its software experiencing a 5-year CAGR of 37% and representing nearly 30% of AI spending. From the start, IDC has invested in developing research and methodologies, including a GenAI Path to Impact Framework that features more than 260 industry and functional use cases. This high-impact work continues to evolve alongside GenAI infrastructure, software, and services advances.

GenAI is initially poised to revolutionize SaaS applications in three key areas: enhancing user productivity, data, and model-driven analytics for faster decision-making and creating dynamic, personalized content for superior user engagement. IDC CloudShare emerging ISV research shows that more than 50% of ISVs are investing significantly in generative AI with a focus on automated testing and improving quality assurance (40.2%), providing smart recommendations (39.8%), autonomous developer training (37%), and rapid prototyping to accelerate innovation (33.7%).

These advancements will enable Independent Software Vendors (ISVs) to optimize customer experience, accelerate innovation cycles, and adapt to future customer needs and market trends more efficiently. Longer-term, advanced, and GenAI will disrupt how applications are developed and transform the traditional categories used to define business software, i.e., enterprise resource management, supply chain management, customer relationship management, and human capital management, to name a few.

Navigating the Monetization Dilemma
One of the most pressing challenges facing SaaS providers is monetizing GenAI features. The variable cost associated with GenAI usage introduces complexity to the monetization strategy. While some providers grapple with whether to charge directly for these innovative features or enhance product value indirectly, it’s clear that developing an effective monetization strategy is crucial for achieving lasting impact. According to IDC SaaSPath 2024 research, 77% of buyers are willing to pay between 10% and 30% more for GenAI-enabled SaaS applications.

The GenAI Journey for SaaS Providers
The journey towards integrating GenAI into SaaS offerings has accelerated rapidly over the past 18 months. As mentioned, more than half of ISVs are already investing significantly in GenAI, with established plans for training and purchasing GenAI-enhanced software and services. This investment reflects the growing buyer demand for GenAI across all industries and geographies, underscoring its potential to redefine the SaaS landscape. Currently, AI is considered integral to the application’s value.

The Role of Use Cases in Driving GenAI Adoption
As the variety and number of use cases for GenAI expands, the focus is increasingly shifting toward revenue generation. Tech buyers are willing to invest in software with embedded GenAI capabilities, indicating a readiness to spend more on these advanced features. This trend highlights the importance of prioritizing and addressing industry-specific use cases with the greatest near-term opportunity to enhance value while exploring opportunities to expand into new use cases systematically.

The convergence of SaaS and AI, particularly GenAI, represents a significant milestone in the evolution of business software technology. As we navigate this new era, organizations must adapt strategies, invest in cutting-edge technologies, and embrace the opportunities presented by GenAI. At IDC, we are committed to supporting businesses in this journey by developing, defining, and defining the value for GenAI use cases to drive business impact across industries and functions.

Learn what matters most to your customers with IDC’s AI Use Case Discovery Tool—find out more.