In the rapidly evolving landscape of technology, Artificial Intelligence has emerged as a beacon of innovation, with companies looking to increase operational efficiency, cost savings and improve employee productivity through AI initiatives.

AI and Generative AI Spending Across the Globe

IDC has recently unveiled the latest release of its Worldwide AI and Generative AI Spending Guide, 2024 V2. Presently, the global Artificial Intelligence market stands at nearly $235 billion, with projections indicating a rise to over $631 billion by 2028.

The three leading industries in terms of Artificial Intelligence spending are Software and Information Services, Banking, and Retail. Combined, these sectors are projected to allocate approximately $89.6 billion towards AI in 2024, representing 38% of the global AI market. With an impressive five-year Compound Annual Growth Rate (CAGR) of 27%, their collective investment is anticipated to surge to nearly $222 billion by 2028. Notably, Generative AI accounts for more than 19% of the total investment across these three industries, highlighting its growing significance in the AI landscape.

Worldwide AI and Generative AI Spending by Industry

Industries Key Highlights in Artificial Intelligence

The Software and Information Services industry is known for its dynamic nature and rapid growth, is now at the forefront of integrating Artificial Intelligence to revolutionize how services are delivered and consumed. With a spending of $33 billion in 2024, companies are using AI to make the software development lifecycle more efficient and error-resistant through AI lifecycle software and predictive models. Enhancing information services by personalizing content delivery based on user data, thus improving user engagement. Artificial Intelligence is also driving innovation by creating new products and tools for data analysis and market trend prediction, helping businesses stay competitive. Additionally, AI is augmenting and automating operational processes, increasing efficiency, and reducing costs by allowing functions such as human resources to focus on strategic tasks. This comprehensive integration of AI is not only streamlining operations but also fostering the development of high-quality, adaptive software and services.

The Banking industry, a market with approximately $31.3 billion in investments in AI in 2024, is using the technology to enable banks to offer personalized customer experiences through machine learning and data analytics, allowing banks to tailor services to individual preferences. AI-powered chatbots and virtual assistants provide round-the-clock assistance for both basic and complex tasks, improving customer service and allowing human representatives to focus on intricate issues. Robotic Process Automation (RPA) streamlines back-office operations, reducing costs and errors. AI algorithms enhance fraud detection and risk management by analyzing transaction patterns and customer behavior for real-time action, thus protecting assets and building trust. In investment banking, AI-driven algorithmic trading analyzes market data for quick, strategic trading decisions, while AI also improves risk assessment models by predicting market shifts, aiding in informed investment decisions and risk management.

Spending in AI for the retail industry is reaching around $25 billion in 2024. Artificial Intelligence is revolutionizing the retail industry by creating personalized shopping experiences through machine learning and data analytics, enabling retailers to understand and cater to individual customer preferences. This leads to enhanced customer engagement and loyalty with customized product recommendations and targeted marketing. AI also addresses inventory management challenges by predicting demand patterns and optimizing stock levels, reducing overstock and stockouts. AI-powered chatbots and virtual assistants improve customer service by handling inquiries efficiently, enhancing customer satisfaction. Additionally, in physical stores, Artificial Intelligence enhances the shopping experience with technologies like smart mirrors for interactive advertisements, promotion and product information and AI-driven systems for improved store security and layout optimization.

Regional Outlook in Artificial Intelligence

In both the Americas and EMEA regions, the banking industry emerges as the top spender in Artificial Intelligence, with market sizes estimated at approximately $19 billion and $8 billion for 2024, respectively. These markets are experiencing robust growth, with five-year Compound Annual Growth Rates (CAGRs) of 30% and 32%. Conversely, in the Asia-Pacific and Japan (APJ) region, the Software and Information Services Industry takes the lead in AI investments, boasting a nearly $11 billion market size, characterized by early and rapid adoption of certain AI technologies. A common trend across all three regions is the relatively low AI spending in the agriculture and fishing industry, marking it as the industry with the least investment in AI technologies.

Conclusion

The integration of Artificial Intelligence across various industries is not just a trend but a transformative shift that is reshaping the landscape of business, technology, and customer interaction. From retail to banking, software and information services to healthcare, AI is enhancing efficiency, personalizing experiences, and opening new avenues for innovation and growth. It’s clear that AI’s potential to drive operational excellence, understand and predict consumer behavior, and innovate product development is unparalleled. As industries continue to harness the power of AI, IDC continues to follow this journey closely and offers the latest insights in about AI and Generative AI spending across the globe.

Learn more about IDC’s AI and GenAI Spending Guide by downloading this product overview.

Find out what matters most to your customers with IDC’s AI Use Case Discovery Tool.

Contributing Author: Mariana Fang – Research Analyst, Data & Analytics

Artificial intelligence (AI) has been around for some time, but the recent surge in popularity from Generative AI has made consumers and businesses excited and wary at the same time. While it is natural to be cautious with new technologies at first, the more businesses are willing to explore and evaluate the technology, the faster they will enjoy its benefits and be prepared for the ever- changing environment that surrounds them. Global investment in AI technologies is experiencing a robust upward trend, with projections indicating sustained growth in the coming years. This dynamic growth is driven by the pursuit of more efficient processes, tailored services, and innovative solutions.

The 2024 V2 release of the Worldwide AI and Generative AI Spending Guide introduces significant updates, including broader technology coverage, a unified dataset perspective of Generative AI alongside the rest of AI, and a refresh of AI use case categorizations.

This comprehensive analysis has identified over 250 functional use cases, meticulously examined and defined by a diverse team of IDC analysts across various research domains. These use cases are organized into 13 functional areas, with the addition of industry-specific use cases to offer an extensive overview of the AI spending landscape. Consequently, this release of the Worldwide AI and Generative AI Spending Guide encompasses a total of 42 modeled use cases, spanning both functional and industry-specific AI applications.

IDC’s WW AI and GenAI Spending Guide Use Cases

Use Cases Highlights

The AI Infrastructure Provisioning use case, which encompasses the spending with the IT infrastructure and resources for AI systems from infrastructure service providers, underscoring its pivotal role in the Artificial Intelligence ecosystem. It represents the largest AI investment area with expenditure reaching $30.3 billion for the year 2024. Projected to grow to $47 billion by 2028, this use case accounts for approximately 30% of the total global spending in Artificial Intelligence. It is heavily used in particular in the Software and Information Services industry.

The use case Augmented Fraud Analysis and Investigation has emerged as a significant industry-specific use case, drawing over $17 billion in investments in 2024 alone, and showcasing a remarkable five-year Compound Annual Growth Rate (CAGR) of 31%. This application is widely adopted across various sectors, notably within the financial industry, which extensively utilizes its capabilities. It is designed to detect illegal or illicit financial activities characterized by intentional deception and/or misrepresentation within different organizational areas, such as operational and financial.

Leveraging AI, these systems employ rule-based learning to pinpoint transactions indicative of fraudulent activities or an increased fraud risk. The systems autonomously learn to identify a wide array of fraud schemes perpetrated by both employees and customers.

Another popular one is the AI-enabled Customer Service and Self Service use case, commanding an impressive $16.7 billion in spending in 2024, represents a universally adopted solution across industries globally. This innovative approach streamlines customer query resolution by autonomously generating knowledge from received queries, eliminating the necessity for live agent involvement. It boasts the capability to curate pertinent articles, recommend new ones based on responses, and engage customers across multiple languages. Furthermore, it enables the delivery of highly personalized products or bundles, precisely timed and optimally priced across various channels, among other advanced functionalities.

The Augmented Threat Intelligence and Prevention use case, a $13.3 billion market in 2024, identifies the banking sector as its primary adopter across various industries. This application employs sophisticated systems that analyze intelligence reports, distill essential information, organize data into a standardized format, and integrate this information into the workflow. By correlating disparate data points, it effectively identifies threats to databases, systems, websites, and organizations, enhancing security measures and safeguarding assets.

Regional Outlook

In both the Americas and the Asia Pacific and Japan (APJ) regions, the AI Infrastructure Provisioning and AI-enabled Customer Service and Self Service use cases stand out as the most prominent. Combined, these two use cases account for 20% ($28 billion) of the total AI spending in the Americas and 28% ($12.8 billion) in the Asia Pacific and Japan region for the year 2024, highlighting their significant contribution to the overall investment in Artificial Intelligence within these regions.

While for the EMEA region, the Augmented Fraud Analysis and Investigation use case emerges as the frontrunner, closely followed by the Augmented Threat Intelligence and Prevention use case. Collectively, these two use cases constitute 17% of the region’s AI spending in 2024, amounting to $8.6 billion, showcasing their prominence in EMEA’s Artificial Intelligence investment landscape.

Conclusion

The integration of Artificial Intelligence into business operations has become a tangible reality for numerous organizations. Understandably, apprehensions about the unknown—such as the potential return on investment (ROI) of such technology, the optimal timing, and the most strategic regions for investment—can initially seem daunting. However, the pathway to making informed decisions, such as concerning the adoption of new technologies, is significantly smoothed by acquiring deeper insights. At IDC, we are committed to continuously enhancing our data and insights to empower businesses at every stage of their journey, ensuring decisions are made with confidence, professionalism, and a forward-looking perspective.

Learn more about IDC’s AI and GenAI Spending Guide by downloading this product overview.

Contributing Author: Mariana Fang – Research Analyst, Data & Analytics

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

The rapid evolution of AI technologies worldwide highlights a strong and growing commitment from both the public and private sectors to leverage AI for innovation, competitive advantage, and tackling complex challenges. As AI and Generative AI become increasingly integrated into various business domains, IDC offers a detailed perspective on this transformation with a comprehensive overview of AI spending by technology type, geography, and use case.

In 2024, organizations are projected to spend $235 billion on AI, a figure that is expected to nearly triple, reaching over $630 billion by 2028, fueled by an almost 30% compound annual growth rate (CAGR). Generative AI, a subset of this broader AI ecosystem, accounts for 17.2% of global AI spending today. Its growth is particularly remarkable, with projections indicating it will make up 32% of AI investments by 2028, driven by a staggering 60% five-year CAGR.

Key Drivers of AI Investment

Several factors are driving this surge in AI spending:

Technological Advancements: Innovations in AI, including machine learning, natural language processing, and computer vision, are rapidly expanding the potential applications of AI. These advancements are encouraging organizations to invest more heavily in AI solutions.
Economic Competitiveness: AI is increasingly seen as essential for enhancing operational efficiency, spurring innovation, and maintaining a competitive edge in the global market.
Data Explosion: The exponential growth of data has created an urgent need for advanced data analysis, interpretation, and security. AI is playing a pivotal role in helping organizations manage and make sense of this vast data landscape.
Consumer Expectations: As consumers demand more personalized, efficient, and innovative services, companies are turning to AI to meet these rising expectations.

IDC’s AI and Generative AI Spending Guide is designed to keep pace with these developments, offering the most up-to-date market insights through a comprehensive and high-quality forecast. This latest release features expanded technology coverage and a modernization of AI use cases, ensuring our clients have the best data to support their strategic decisions.

Learn more about IDC’s AI and GenAI Spending Guide by downloading this product overview.

AI Spending by Technology: A Closer Look

AI Platforms have emerged as the leading area of investment, accounting for nearly 25% of the overall AI core IT spend by 2028. Within this category, Generative AI Software Services stand out as the most significant and fastest-growing segment, with a 70% five-year CAGR. To provide a clearer view of the landscape, IDC has expanded its coverage of AI Platforms, offering insights into vendor market share as well as forecasts for industry and use case opportunities.

While software represents about 57% of AI and Generative AI spending, hardware and services each account for approximately 24%. Service providers are particularly active in investing in server and storage hardware, especially as they seek to offer Infrastructure-as-a-Service (IaaS) to enterprise customers. This focus on infrastructure highlights the critical role that robust hardware plays in supporting the expanding demands of AI applications.

Regional Insights: AI Spending Across the Globe

From a regional perspective, the Americas lead the way in AI investment, commanding nearly 60% of global AI spending and experiencing a 30% five-year CAGR. EMEA follows as the second-largest region, capturing 23% of global spending with growth rates comparable to those in the Americas. Meanwhile, organizations in the Asia-Pacific and Japan (APJ) region, who were early adopters of certain AI technologies, may see their global share decrease slightly by 2028 as other regions accelerate their investments and catch up.

Looking Forward: Navigating the Future of AI

As we look ahead, AI spending is expected to continue its robust growth, driven by new applications and innovations. However, this rapid expansion also underscores the importance of developing ethical frameworks, cultivating skilled workforces, and implementing transparent policies to ensure that AI is used responsibly and effectively. The future of AI holds great promise, but it also demands careful stewardship to maximize its benefits while minimizing potential risks.

IDC’s commitment to providing deep insights and forward-looking analysis remains unwavering, as we help our clients navigate the evolving AI landscape with confidence and clarity.

Conclusion:

As AI continues to redefine industries and reshape global markets, staying informed about its trends and impacts is critical for making sound strategic decisions. IDC’s latest insights into AI and Generative AI spending offer a clear and comprehensive guide to navigating this dynamic landscape. By understanding the key drivers, technological advancements, and regional differences, organizations can better position themselves to harness AI’s full potential. As we move forward, the focus must remain on not only embracing these innovations but also ensuring they are deployed ethically and sustainably, paving the way for a future where AI benefits all.

Contributing author: Mariana Fang – Research Analyst, Data & Analytics

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

With each positive interaction a customer has with a brand, they expect similar or higher levels of service in the future. Unfortunately for brands there is no finish line, only continuous improvement to create better experiences.

Brands realize that putting the customer at the center of their business is a way to deliver consistent, personalized, and timely engagement across digital and physical channels and across marketing, sales, service, and support functions.  

According to IDC’s September 2023 Future Enterprise Resilience and Spending (FERS) survey, respondents ranked delivering great customer experience as their top focus area to derive customer value. Brands have a clear mandate to augment personalized experiences and acquire and retain customers through customer experience (CX) technology investments.

To fulfill that mandate, customers should first prioritize continuous integration of dynamic data across touchpoints and deliver high-quality data using Customer Data Platforms (CDPs). IDC’s July 2024 Future of Customer Experience (FoCX) Survey identified that over the next 12 months, 77.8% of respondents plan to increase technology spending for CDPs.  

Secondly, using AI and GenAI driven processes and tasks will help to better identify and segment audiences, uncover new levels of customer insights and create effective engagements. IDC’s April 2024 FERS survey shows that spending on GenAI–related infrastructure, models, applications, and services is expected to increase by an average of 64% across all companies. The survey also shows that companies that report an 80% success rate with their GenAI proof-of-concept efforts ranked “access to required high-quality data sets” as a top five success factor.  

The final point is that acquiring customer data that fuels personalization and engagement is back in the news with Google’s latest planned announcement that it won’t be deprecating third-party cookies. 

Google announced that it is introducing a new experience in Chrome that lets users make an informed choice across their browsing habits. While regulators decipher the plan and users decide on choices they face, organizations should continue to investigate what zero-, first- and second-party data they need to build segments and models with trust. Customers strongly prefer brands that are transparent and prioritize their data security and privacy, leading to a stronger, trust-based relationship. 

Customer Data Platform to Enhance Customer Experience  

According to IDC’s 2024 CX Path Survey, the top business outcome that organizations want to achieve from implementing CDPs is enabling customers to curate contextual experiences. CDPs provide high-quality data and analytics for this and other use cases involving growing revenue streams and delivering differentiated experiences with high value business outcomes. CDPs must include the following key components:  

Aggregation: Ingest, integrate, cleanse, resolve and consolidate individual-level customer data from multiple sources and formats and determine which attributes and dimensions to include in a profile or segment.  

Engagement: Activate segments for campaigns, advertising, and messaging across different channels and audience groups defined by multiple attributes and dimensions. Includes next best action, recommendations, etc. based on end-user choices and preferences synchronized across channels. 

Insights: Descriptive, diagnostic, and predictive analytics to understand the complexities of the customer journey, predict future behaviors and tailor marketing efforts. Augment it with GenAI to drive automation and improve productivity for users to engage with CDPs and improve self-service.  

Orchestration: Shared set of services will help to deliver a common orchestration layer for workflows, event management, scheduling, and rules. Having a solid framework for data governance and AI governance will help to balance personalization versus privacy, trust, and transparency.  

GenAI and CDP to Drive Productivity and Personalization 

While vendor roadmaps for AI are advancing, narrow down on which GenAI use cases you want to pursue and what does it take to implement the prioritized ones in context of CDPs. In parallel, define and develop the metrics and analysis required to justify investment in the selected use case or two. Organizations should use GenAI to improve productivity for CDP users and how it can deliver personalization to meet rising customer expectations in the following ways: 

Custom GenAI models trained on CDP data are used for generating personalized content like product descriptions, custom messaging, landing pages, email copy.  

Combine retrieval-augmented generation with GenAI models to provide grounded, trusted responses by extracting information within CDP and other knowledge repositories. 

Conversational AI assistants enable marketers to query and interact with data or describe the customer journeys they want to create using natural language, making it more intuitive and efficient for marketers.  

Dynamic segmentation allows for real-time adjustments to customer segments based on their behaviors and interactions analyzed by GenAI models with marketing campaigns.  

Synthetic data generation helps in augmenting datasets where actual data is sparse or limited, enhancing the robustness of AI models. This approach is particularly useful in scenarios where data privacy concerns limit the availability of real data.  

Prepare for the Next Phase of Customer Experience 

According to IDC FutureScape 2024 Predictions, Customer Data Platforms will deliver high-quality data for predictive AI and GenAI, activating 80% of real-time personalized customer interactions at scale for G2000 firms with four times engagement gains by 2026. Organizations need to identify primary use cases that highlight the growing importance of unified customer data beyond marketing and across sales, customer service, and field service.

They also need to quickly build a picture of full journey and behaviors exhibited by customers by accessing intent data, service and support data, and customer interactions captured in unstructured sources in a secure and trusted manner. Finally, understand what is practical today with GenAI and how it will automate CDP tasks and workflows to make marketers more productive and use it to build personalized content for activation in the best channel preferred by the customer.  

Is your firm ready to take the next steps to meet rising customer experience expectations? Organizations need to prioritize investments in customer data platforms to deliver high-quality data for GenAI use case that will add to marketing productivity, enable CDP automation, and adopt trust- and governance-based marketing programs to drive personalization at scale and streamline customer experiences.  

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

With Broadcom’s acquisition of VMware in 2023, the new VMware by Broadcom has undergone an overall program of merger and reorganization, which in turn has led to a restructuring and consolidation of many of the most prominent and popular VMware product offerings.

As a result of some of these changes, many customers are seeing significant cost increases at both purchase and renewal for VMware products, with new more expensive subscription bundles replacing common perpetual software licenses. These changes have caused much consternation and concern for customers, with even Broadcom CEO Hock Tan acknowledging and referenced these post-merger; 

“We overhauled our software portfolio, our go-to-market approach and the overall organizational structure. We’ve changed how and through whom we will sell our software. And we’ve completed the software business-model transition that began to accelerate in 2019, from selling perpetual software to subscription licensing only – the industry standard. 

Of course, we recognize that this level of change has understandably created some unease among our customers and partners. But all of these moves have been with the goals of innovating faster, meeting our customers’ needs more effectively, and making it easier to do business with us. We also expect these changes to provide greater profitability and improved market opportunities for our partners.” 

What is this means for customers 

VMware by Broadcom has recently made significant changes which includes the end of sale of perpetual licenses, change of license metrics and announcement of new product bundles. 

End of Sale of Perpetual Licenses 

Where customers have not already transitioned to Subscription licenses, Broadcom by VMware is mandating subscription transitions at renewals and purchase, with the majority of Perpetual Licenses removed from General Availability. For many customers this shift to subscription increases annual costs with subscription licenses generally being more expensive than equivalent perpetual support and maintenance. 

Changes of License Metric 

In 2020 VMware amended license metrics for many of its most popular products from per CPU to Per Core, with an entitlement of 32 cores per CPU license/subscription. In 2023 VMware shifted this entitlement to 16 cores per CPU subscription, which for many customers with high core counts effectively doubles their license requirements, thus resulting in further purchasing and increased costs. Furthermore in 2024 with the introduction of the new Subscription bundles, VMware by Broadcom moved to per core licensing overall. 

New Product Announcements  

Upon completing the acquisition, VMware by Broadcom moved quickly to consolidate and “simplify” their product offerings, effectively bundling many popular products into a small number of distinct combined product offerings whilst discontinuing individual sales of individual products. Below is the feature comparison of these new offerings: 

The key challenge for buyers is that it severely limits flexibility in terms of product choice. As an example, a customer previously purchasing vSphere Enterprise and vCenter Server, at renewal will no longer be able to purchase those licenses individually, instead now having to purchase subscriptions to VMware Cloud Foundation or VMware vSphere Foundation, thus having to purchase the likes of Tanzu and vSAN, regardless of whether that customer requires these products. These bundles come at an increased cost above the previous individual subscription costs, reflecting the extra value they give customers, however are unavoidable for customers regardless of their need or requirement. 

Divestment of VMware Products 

Further to Broadcom’s acquisition, in February 2024 Broadcom agreed to divest VMware’s End User Computing decision to KKR, which has become a standalone company called Omnissa. 

“Workspace ONE and Horizon are best-in-class platforms chosen by many of the world’s leading enterprises to create seamless and secure digital workspaces with interoperability across increasingly complicated technology stacks,” said Bradley Brown, Managing Director at KKR. “We see great potential to grow the EUC Division by empowering this talented team and investing in product innovation, delivering excellence for customers and building strategic partnerships.” 

As a standalone company, the EUC Division will continue to be run by its existing management team led by Shankar Iyer” 

For some EUC customers, this may dilute the overall Broadcom investment into another company, potentially resulting in lower Broadcom discounts, and therefore, not only extra costs for remaining VMware investments but also for those divested EUC products. 

Longer Term Deals 

In addition to these changes, we note that Broadcom are incentivizing longer term deals, offering the most optimal pricing for customers considering 3–5 year terms. Where customers opt for short term renewals, pricing per unit is often much higher, effectively disincentivizing these options and limiting short term extensions. 

Quotation Delays 

As might be expected with such wide-reaching changes to commercials and business structure, customers are reporting that renewal quotations are taking longer than expected to be provided, which in turn gives less time to review and negotiate.  

Reseller Restructures 

Along with changes to commercial offerings, Broadcom have restructured their channel sales and resellers. For larger companies, Broadcom has chosen to take these accounts direct, which means customers may no longer have the support of their reseller in ascertaining requirements and facilitating negotiations. Negotiating direct can be very different to via a reseller and may require more preparation and potentially leveraging other Broadcom interests, interactions and investments. 

What might the cost impact be? 

Whilst every customer scenario is different, overall we see that large cost increases are inevitable and indeed crucial to VMware by Broadcom’s growth strategy. Customers exiting and renewing long-term deals may see compounded impact from multiple changes that have as yet not affected them whilst in contract. In real terms customers may see cost increases anywhere from 100% to potentially as high as 800% at renewal   

Predominantly the transition to new bundled products is impacting customer costs the most, however these extra bundled entitlements equally bring extra added value which may be beneficial to customers and potentially give opportunity for replacing incumbent products and costs in the technology estate, or optimizing current VMware by Broadcom deployments and operations. 

What should customers do? 

Act now to understand potential impact and risk, potentially modelling future cost scenarios to drive visibility internally and gain traction for strategy review and change 

Understand future strategy and requirements, and how these might align to VMware technology and offerings 

Consider the benefits of the new product bundles and whether they represent value and/or opportunity for other vendor displacement and indirect savings 

Review entitlements to understand how the divesture of VMware EUC products to a separate company might impact investments overall 

Review VMware use cases and identify whether other vendors solutions might be feasible and cost effective to migrate to 

Noting the earlier comment regarding long lead times for quotations, push for an early quotation to quantify cost increases and allow as much time as possible for post-proposal negotiations 

Understand the impact of changes to reseller involvement to the purchasing organization and the impact that may have. As a corollary, seek external counsel from market intelligence partners to address any knowledge gaps left by changing resellers 

Benchmark existing BAU deals to establish a baseline and retain a benchmarking partner that can provide accurate data on new products, pricing metrics and license rules 

Note that any previous price protection or fixed renewal costs agreed in contract may likely no longer apply as they are generally centered on like for like renewals, which are no longer available given the product changes 

Create a post-renewal strategy and timeline for any decisions made that may require more time to implement with forward milestones to review progress 

Recognize that any current or future partial reductions in subscription quantities will generally not generate linear savings at renewal and might be modelled prior to ascertain impact. 

In Summary 

Product changes and cost increases are almost inevitable for customers renewing post Broadcom acquisition, therefore immediate planning for those potential increases and the leverage of Broadcom VMware technologies in both the short and long term is key. IT Buyers should look to highlight these changes to key Stakeholders, Budget Holders and Strategic decision makers at the earliest opportunity to allow as much time as possible to determine the extra value and opportunities in these new bundles and/or where necessary discuss impact mitigation options.

Ascertaining, modelling and benchmarking potential costs may give further impetus and leverage so that these changes are high on the agenda, and where customers maintain internal IT/Technology risk registers, this technology might be added as a commercial risk in order to gain further visibility and traction. 

IDC’s Sourcing Advisory Services (SAS) provides you with the industry’s most-recognized price benchmarks, analyst advice and IT optimization insight.  

Ateequr Rahim contributed to this blog.

This year, the convergence of cloud computing and generative AI (GenAI) is creating unprecedented opportunities for innovation. For growing tech vendors and startups, leveraging these technologies is not just a competitive edge—it’s a necessity. This blog explores how GenAI is transforming cloud infrastructure, offering practical insights and strategies to help your business thrive. 

The Convergence of Cloud and GenAI 

Cloud computing provides scalable, on-demand resources that allow businesses to be agile and responsive. Generative AI, on the other hand, brings advanced machine learning capabilities that can turn vast amounts of data into actionable insights and automate complex tasks. The intersection of these technologies marks a paradigm shift, enabling smarter, more efficient, and highly adaptive cloud environments. 

Key Benefits of Integrating GenAI with Cloud Infrastructure 

Predictive Scaling and Resource Allocation 

AI models can forecast workloads based on historical data, enabling dynamic resource provisioning. This means your cloud infrastructure can automatically scale up or down based on demand, ensuring optimal performance and cost-efficiency. Predictive scaling helps avoid over-provisioning and under-utilization, common pitfalls in traditional cloud management. 

Predictive scaling involves using AI to analyze patterns in data usage and forecast future needs. This proactive approach ensures that resources are available when needed, without wasting money on unused capacity. For example, an e-commerce platform might see spikes in traffic during holidays. AI models can predict these spikes and adjust resources accordingly, ensuring smooth operation during peak times. 

Automated Cloud Cost Optimization 

Managing cloud costs is a critical challenge for startups and growing tech vendors. GenAI can analyze spending patterns and recommend cost-saving measures. AI-driven tools can help you select the most cost-effective instances, rightsizing your infrastructure, and even automate budget alerts and recommendations. This ensures that you get the most out of your cloud investment without unnecessary expenses. 

Cost optimization is not just about reducing expenses but about making smart investments in cloud resources. AI can provide detailed insights into where money is being spent and identify areas where savings can be achieved. For instance, AI might suggest moving non-critical workloads to less expensive storage options or shutting down underutilized instances automatically. 

Intelligent Load Balancing and Traffic Management 

Efficient load balancing and traffic management are crucial for maintaining high performance and user satisfaction. AI-powered traffic prediction models can anticipate traffic spikes and direct load accordingly, optimizing resource use and minimizing latency. Additionally, smart CDN optimization and adaptive application performance management ensure that your applications run smoothly, even under varying load conditions. 

AI can predict traffic patterns and dynamically adjust load balancing to ensure optimal performance. This is particularly important for applications with fluctuating traffic, such as social media platforms or online gaming services. By distributing traffic efficiently, AI helps prevent bottlenecks and ensures a seamless user experience. 

Enhancing Cloud Security with GenAI 

Security is a paramount concern for any tech business. GenAI enhances cloud security by providing advanced anomaly detection, adaptive security policies, and automated threat response. 

Anomaly detection involves using AI to identify unusual patterns in data that could indicate a security breach. By continuously monitoring network traffic and user behavior, AI can detect and respond to threats in real time. Adaptive security policies use AI to adjust security measures based on current threats, ensuring robust protection. 

Automated threat response leverages AI to triage incidents, contain threats, and even predict future vulnerabilities. This proactive approach to security helps businesses stay ahead of potential threats and maintain a strong security posture. 

AI-Driven Cloud Management 

GenAI streamlines cloud management through intelligent monitoring, automated incident response, and performance optimization. 

Intelligent monitoring involves using AI to analyze logs and performance metrics, identifying issues before they become critical. This proactive approach minimizes downtime and ensures smooth operation. Automated incident response uses AI to classify and route incidents efficiently, often resolving common issues without human intervention. 

Performance optimization is another key area where AI can make a significant impact. By continuously analyzing performance data, AI can identify bottlenecks and recommend optimizations to improve efficiency. This ensures that your cloud infrastructure runs at peak performance, delivering the best possible user experience. 

Cloud-Native AI Development 

Developing AI models in a cloud-native environment offers significant advantages in terms of scalability and flexibility. 

Containerized environments package AI models with all their dependencies, ensuring consistency and portability. This simplifies deployment and scaling, allowing businesses to respond quickly to changing demands. Kubernetes, a popular orchestration tool, automates the deployment, scaling, and management of containerized AI services, providing robust and reliable infrastructure for AI workloads. 

Serverless architectures offer another layer of efficiency. Event-driven AI processing allows code to be executed in response to specific events, optimizing resource usage and reducing costs. This pay-per-use model means businesses only pay for the computational power they consume, making it a cost-effective solution for many applications. 

MLOps, the practice of combining machine learning with DevOps, is essential for managing the lifecycle of AI models. Automating the deployment, monitoring, and retraining of models ensures they remain accurate and relevant over time. Version control for data, models, and code is crucial in maintaining consistency and reproducibility. Continuous integration and deployment (CI/CD) pipelines enable frequent updates and improvements, keeping AI systems at the cutting edge of performance and reliability. 

Future of Cloud AI 

Looking ahead, the integration of AI with emerging technologies like quantum computing and autonomous systems will further revolutionize cloud infrastructure. Companies that stay ahead of these trends will be better positioned to leverage new opportunities and maintain a competitive edge. 

Quantum computing will enable the development of more sophisticated models and algorithms, accelerating research and innovation. Hybrid quantum-classical cloud architectures will become more prevalent, allowing organizations to harness quantum computing’s power for specific tasks while leveraging classical computing for others. 

The next generation of cloud infrastructure will be characterized by autonomous systems powered by AI. These self-organizing and self-optimizing environments will manage resources, detect anomalies, and resolve issues without human intervention, reducing operational complexity and enhancing system reliability. 

Conclusion 

The convergence of GenAI and cloud computing is revolutionizing how tech vendors and startups manage their infrastructure. By embracing predictive scaling, automated cost optimization, intelligent load balancing, and advanced security measures, businesses can ensure their cloud environments are efficient, secure, and highly adaptive. As AI continues to evolve, integrating these technologies will be crucial for staying competitive and driving innovation in the digital age. 

Questions? Reach out to learn how we can support your company’s growth in the AI-driven cloud landscape. 

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

As GenAI increasingly becomes mainstream, its applications in smartphones are fast becoming a key design vector for smartphone manufacturers.

IDC’s latest forecast estimates that GenAI smartphone shipments will grow 364% year-over-year in 2024, reaching 234.2 million units, growing to 912 million units in 2028 implying a compound annual growth rate (CAGR) of 78.4% for 2023-2028.

Over the past year, many market participants have announced their own set of artificial intelligence (AI) tools to demonstrate smartphone UX changes. These are based on various foundational models, large and small language models (LLM and SLM) enabling generative AI (GenAI) features with on-device processing, and multimodal input and output. The OEMs have a hybrid approach to enabling AI features-device-based for localized and cloud-based for heavy computational activities.

While many of these AI features are presently limited to premium smartphones, we should expect to see these trickle down the pecking order made possible by the use of cloud-based AI solutions as these devices will lack necessary hardware. However, a reduced scope and privacy/latency remain key considerations. Below are some of these features and how they are different across the OEMs.

OEMs are Adding Their Unique Flavor with AI Features

While the AI features fall within the same broad categories for all major platforms and devices, each OEM imprints their unique signature, along with the tools from the likes of Google and OpenAI.

Almost every key OEM has announced features/tools for photo/video editing, writing/editing, translation and interpretation, summarizing, search enhancement etc., targeting the most used smartphone features. To most users, it may not matter if AI is the enabler as long as they get a better outcome. Case in point, users are more interested in the final portrait photo than in the hardware, software, or AI driving it.

A big hit, according to the market participants, are Circle to Search, a Google AI feature tied to Android 14, mentioned by Samsung in their earnings in Apr 2024 as the most used feature and Eraser which OPPO claimed is used 15 times per day on average. Live translation is another extremely useful feature overcoming the barriers of language and can be used conveniently even in an offline mode. There is also increased focus on wellness, where Google, for example has Sleep and Snore detection, Samsung recently announced Sleep Apnea detection, and Apple has been talking about monitoring vitals.

AI Features – An Extension of Brand Message for the OEMs

While the end-use for GenAI features is driven by the same user needs, OEMs use AI features as an extension of their brand message and stand out from the others.

Apple announced GenAI capabilities as “Apple Intelligence”, indicating AI is central and all under one hood on its devices, with features designed to have cross-functionality across various apps and iPad, iPhone, and Mac devices. It might not be a radically new way of how a user interacts with the iPhone, but enhancing the app functionalities and fun activities such as creating personalized memories, Genmojis and avatars. The revamped Siri with access to underlying user data (emails, messages, photos, locations, files etc.,) can be more context-aware, while sticking to its central message around privacy even as the user connects to ChatGPT. Apple’s vertically integrated approach relies largely on in-house language models and its private cloud infrastructure, while partnering with OpenAI for ChatGPT. 
Google continued with its legacy of using software to enhance smartphone capabilities. Pixel 8 Pro enabled many on-device GenAI features by running language models on the device. It has a host of features such as call management (Clear Calling, spam calls, Call Assistant); photo/video editing (Photo/Audio Eraser, Best Take); communication (Proof Read, Smart Reply, Summarize, Magic Compose) etc. For Google, it can be a blurry line between what is unique to Pixel smartphones vs the rest of the Android lineup. Google has been managing this by bringing some of the features to Pixel smartphones first before they go to the wider Android players.
OPPO, in continuation of its focus on camera and photography features, has features such as AI Best Face, AI Eraser, AI Studio and AI Clear Face, while also expanding AI features to broader spectrum of AI applications for communication and productivity. OPPO also introduced Social Media Creation tools to assist in creating content specifically tailored for social media platforms.
Samsung launched Galaxy AI tools on its flagship Galaxy S24 series and on Galaxy Z6 Foldables recently, and extended some of these features to its older models, focusing on communication and productivity. Live translation as well as real-time Interpreter are standout features. Galaxy AI includes Photo Assist, Instant Slow-Mo, AI summarization, Chat Assist and Magic compose writing/editing tools. Features such as dual-screen mode for Interpreter are customized for the foldable form factor. 
While other Android players have also announced AI tools – Xiaomi’s AI-generated subtitles for video calls and Image Editor, Honor’s eye-tracking AI functions, Motorola’s personalization and privacy-oriented features – partnership with Google remains vital to have access to the broader set of tools integrated with the Android operating system.  

Another area of differentiation is the size and number of LLMs and the training material used which impact performance and UX. Apple uses its own language models, and OpenAI for tasks that are beyond its realm. Android players use Gemini models (Pro and Nano) and multiple other different-sized models. OPPO has its own SLM that uses 7 billion parameters as well as an LLM AndesGPT in addition to using Gemini. There are partnerships with other tech companies including Qualcomm, MediaTek, and Microsoft.

Further, there are differences in the execution of these features and their accessibility for third-party apps.

iOS developers and apps have experience of automating tasks or working with features such as Siri Intents and Shortcuts. For example, developers and apps with SiriKit already integrated into their apps could see immediate enhancement with new Siri capabilities. Some of their AI tools such as writing tools would be easily available to developers for their third-party apps.
Google also provides developers with APIs and SDKs to integrate Gemini AI into their apps. However, Google, OEMs and developers have to work with diverse hardware of multiple Android smartphones and ensuring adequate testing for seamless integration.

AI will be Central in Driving UX Across Smartphones

The opportunity and the challenge for OEMs is to deliver UX that matches the AI hype. Specifically, OEMs can leverage AI to get past user fatigue from the hardware features that have faced backlash as being boring. Just as we now use dictation, text prediction and photo editing, these new AI features will also become everyday tools on smartphones. In any event, below is a summary of key action items that OEMs will need to bring front and center as they embark on this next phase of evolution of smartphones.  

Overall, a more personalized, intuitive and user-friendly experience that doesn’t interrupt the normal workflow can create more stickiness. While iPhone users have high loyalty, it’s not the same for Android users who easily switch between brands. A radical shift such as opting for an app-less device is not yet for the masses, while a traditional interface with an app may be a bit outdated and inefficient for an evolved user. An in-between approach with a generative user interface could take the users on the AI track more gradually.  
Network connectivity will also play a key role to have a faster, smoother connection and a seamless experience for using AI features, especially if there is cloud processing. Together with faster processing on the device, Wi-Fi 6E/7 and 5G network connectivity will help in faster response with lower latency. This will also help with democratization of AI by enabling more smartphones with cloud-AI features.
User privacy will remain central to these experiences, partly enabled by on-device AI but also reflected in a company’s philosophy to ensure user data remains private while also contextualizing it to provide more personal results. The side benefit of on-device AI is that by spreading out the processing to on-device AI across the installed base of devices, much less server CAPEX is required than would be if AI was only processed on servers.
As these features go beyond the native apps and start to be integrated in the third-party apps, it will be imperative to work with the developers to bring AI features to more apps and ensuring easy ways for developers to integrate AI features into their apps.

OEMs are riding on the AI hype to develop and integrate the AI features in their smartphones and at the same time educating and convincing the users of the benefits of AI. Afterall, to drive more users to their brand, AI capabilities can have consequences beyond immediate upgrade to a new smartphone.

For a look at industry and segment spending forecasts for broader AI and GenAI use cases, read about IDC’s ⁠AI and GenAI Spending Guide.

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