Why Do Organizations Switch Their Service Providers?

Companies today use service providers across all facets of the business. From modernizing technology and streamlining operational processes to strategic consulting and IT outsourcing, services providers provide vast value, expertise, and increased efficiency to their customers, enabling them to spend more time and energy focused on their core functions. These relationships between service providers and their clients are forged over time, typically years or even decades, and many providers often promote the average length of time they retain clients as a performance indicator.

However, what happens when customers start questioning the relationship’s value?

Many customers are often reluctant to switch providers because of the time it takes for a new provider to understand their business, and there are also both direct and indirect costs associated with switching providers. Sometimes, regardless of these drawbacks, customers still choose to move on. What developments usually result in customers reaching this decision? IDC recently examined this question utilizing its Services Path data, looking across all types of services, to understand the reasons why companies ultimately choose to make the switch.

IDC’s Services Path program contains comprehensive data and guidance on the mind and journey of services buyers, for professional services, outsourcing services, managed services, and engineering services. The data set is based on a global survey of approximately 2600 organizations, spanning all sizes and industries, and covers topics ranging from adoption, budgeting trends, and purchasing preferences, to pricing and contract options and detailed customer satisfaction ratings for hundreds of vendors.

Services Path data shows there are several factors that consistently stand out as the primary reasons why customers choose to switch service providers. The top 3 reasons, examined by role, are shared below (in rank order):

IT Respondents:

Enable more rapid digital transformation (DX) and modernization
Improve service quality and raise service levels
Lower cost

Line of Business Respondents:

Improve service quality and raise service levels
Enable more rapid digital transformation (DX) and modernization
Improve data analytics and decision support

Digital transformation, modernization, and improved SLAs/service levels are the top reasons driving companies to make replacements. However, there are some differences between IT and LOB personnel in terms of what’s driving their decisions to switch. For example, IT places a much greater emphasis on lowering costs than line of business employees. Conversely, LOB leaders view improving corporate sustainability as a significantly greater driver of replacements than IT folks.

To help reduce customer switching risk and further bolster the stickiness of long-term relationships, services firms should make note of these top switching drivers and ensure they clearly demonstrate their ability to accelerate their clients’ DX initiatives and deliver high-quality and cost-effective services. 

Customers today are looking for strategic partners that can be leveraged broadly across the business, who understand the nuances of their business, have deep expertise in their industry, and can provide fast proof of value. Firms that continually deliver on these needs will be well-positioned to maintain long-term clients and minimize the risk of customer attrition.

To learn more about IDC’s Services Path program, watch this short video.

Contributing author: Jason Bremner – Research Vice President, Worldwide Services

Artificial intelligence is consuming the attention of IT and business leaders. So much so that many organizations are racing to hire or promote individuals to the role of chief AI officer. Call it the rise of the CAIO. 

The rapid and widespread interest in this strategic job role has helped inspire the CAIO Summit, which will be held this October in Washington, D.C. This is the second such summit – the first was held earlier this spring. It is being hosted by the CDO Club, which also hosts the CDO Summit. 

I had the opportunity recently to speak with the man behind the CAIO Summit, David Mathison. We discussed the ideal background for an individual targeted for the CAIO role, the technology and business skills they should bring to the table, and the personal traits that add to a candidate’s likelihood of success. 

The Ideal Background for a CAIO Candidate 

A successful CAIO should possess a combination of technical expertise, strategic vision, leadership skills, ethical awareness, and the ability to collaborate effectively across disciplines and teams, Mathison says. This multidimensional skill set enables them to drive AI innovation and create value for the organization. 

A CEO typically expects the CAIO to be a strategic leader. They should be able to drive innovation, deliver results, uphold ethical standards, foster collaboration, and effectively communicate the value of AI to all stakeholders, Mathison explains. These expectations will vary depending on the organization’s industry, size, strategic priorities, and the specific objectives outlined for the CAIO role. 

Although artificial intelligence has been around for decades, interest in it has skyrocketed in the last few years — largely due to the popularity of generative AI tools such as ChatGPT. Since many organizations have little prior experience with AI from a strategic perspective, CEOs look to the CAIO to develop a long-term vision and road map for AI adoption and innovation. 

The CAIO should anticipate future AI trends, opportunities and challenges and ensure that AI strategies align with the organization’s long-term goals and objectives, Mathison explains. 

Top Experiences, Skills, and Traits Needed in the CAIO Role 

Mathison says the following are AI-related skills and qualifications that a CAIO should have in order to be successful: 

Deep understanding of AI technologies: A CAIO should have a strong technical background and a deep understanding of various AI technologies, including machine learning, natural language processing, retrieval-automated generation (RAG), and robotics. 

Data science and analytics proficiency: This is essential to effectively leverage data-driven insights and develop AI models. Statistical analysis, data visualization, and predictive modeling skills are required. 

Risk management and compliance knowledge: They should understand risk management principles and regulatory compliance requirements related to AI and ensure that AI initiatives adhere to legal and ethical standards. 

Strategic vision and leadership: A CAIO should possess strong strategic planning and leadership skills to develop and execute a comprehensive AI strategy aligned with the organization’s goals. This involves setting priorities, making informed decisions, and inspiring teams to achieve objectives. 

Knowledge of ethical and responsible AI practices: Given the ethical implications of AI, a CAIO should be well-versed in ethical considerations and trustworthy, responsible AI practices. This includes addressing issues related to fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. 

AI growth and learning mindset: Given the rapid pace of AI innovation, a CAIO should have a growth mindset and a commitment to continuous learning around what is a rapidly evolving technology. They should stay updated on the latest developments in AI technologies, trends, and best practices. 

AI project management skills: Proficiency in project management is important for a CAIO to effectively plan, execute, and monitor AI projects. This includes defining AI project scopes, allocating resources, managing timelines, and mitigating risks. 

There are some skills and traits that a CAIO should possess that are not unique to this leadership role. They include business acumen, ability to communicate and engage stakeholders, mastery of change management, and collaboration and interdisciplinary thinking. Assuming that a CAIO has all of the above skills and traits, attitude is equally important for success.   

Mathison recommends that a CAIO be patient and persistent. Driving AI adoption and transformational change takes time, patience, and persistence. CAIOs must stay focused on the long-term vision and objectives and celebrate incremental successes along the way, he says.  

Finally, a successful CAIO must gain a thorough understanding of the organization’s industry, business model, goals, and challenges. They should align AI initiatives with the organization’s strategic objectives and identify opportunities for AI to create value and drive innovation. 

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

The Asia/Pacific healthcare sector is at a pivotal moment, as the industry is transitioning from just ‘care anywhere’ to ‘AI everywhere in care management’ phase. This essential transition is supported and driven by the focus on robust clinical data sets and evolving connected care ecosystem. To efficiently manage the care delivery process, care providers are prioritizing enhancing clinical, operational and administrative workflow productivity.

As a result, GenAI has emerged as a transformative force, with a huge potential to revolutionize the future of healthcare workflow processes and care delivery system.

Dedicated Budget for Generative AI, Initial Investments Focused on PoCs

IDC’s recent survey shows that in Asia/Pacific over half of healthcare and life science organizations are planning to have a dedicated budget for GenAI. IDC data also shows that almost 40% of healthcare providers and nearly a third of life sciences firms in the region are currently focused on proofs of concept (POCs) of GenAI models as part of testing tools and solutions. This trend is mainly owed to the nascent stage of tech adoption and to the testing of the scalability and partnership ecosystem by healthcare organizations.

GenAI adoption is set to create a positive impact on clinician experience, patient engagement, and workflow efficiency management for healthcare provider organizations. In life sciences, the initial impact will be mostly on drug discovery and design, enhanced patient engagement, streamlined clinical trials, and patient safety. Data is at the core of AI, and this is driving healthcare organizations to increase their focus on EHRs and investments on cloud adoption to make their digital infrastructure and data platforms more robust and secure.

Evolving Partner Ecosystem

Most healthcare and life sciences organizations are of the opinion that GenAI models that leverage their own business data will give them a significant advantage over competitors. Those with mature and secure clinical data foundations, such as multi-cloud and hybrid cloud architectures are better positioned to take full advantage of GenAI at a faster pace. At the same time, the focus on data security has increased as the healthcare sector faces intense threats through cyberattacks. As a result, the GenAI ecosystem involves IT SPs, security and platform providers, and hyper-scalers in solution deployments.

Time to Align GenAI Use Cases for Healthcare Organizations

As the demand for GenAI intensifies, healthcare and life science organizations in the region are shifting their focus towards healthcare-specific GenAI use cases. To accommodate this demand, IDC recently released a study documenting GenAI use cases in the healthcare provider segment GenAI Use Case Taxonomy, 2024: The Healthcare Industry, and life sciences sector GenAI Use Case Taxonomy, 2024: The Life Sciences Industry, addressing business impact, metrics, and data modality for each use case.

Current GenAI case study deployments in the Asia/Pacific region predominantly address clinical documentation and summarization, content creation for clinician-to-patient communication, personalization of patients/consumer experiences, patient engagement, and drug discovery and design. These use cases are set to accelerate personalization, care experience, and workflow productivity.

Challenges on the Journey to GenAI Adoption

The journey to GenAI adoption is not without risks. Regulatory risks and higher infrastructure costs are the limiting factors for GenAI adoption among healthcare and life sciences organizations in the region. Organizations will carefully consider tech partnerships and align use cases to implement GenAI solutions effectively and safely. IDC data shows that healthcare CIOs expect a software provider to have robust data security, seamless and intuitive AI models to work with, and GenAI models that support a broad range of content, both structured and unstructured

Road Ahead

Moving ahead, creating a robust clinical data foundation, aligning GenAI use cases between organizational priorities with that of the tech providers’ offerings, and exploring flexible pricing options, along with trust and transparency in the solution engagement would ensure a smooth transition of GenAI use case deployments from PoCs into production.

GenAI adoption in healthcare and life sciences, though at its nascent stage, is set to have a significant impact on enhancing clinician efficiency, improving workflow productivity, and hyper-personalization of patient experience. Currently, there is increased priority towards POCs as part of GenAI model deployments but this is set to transition to full-fledged deployments supported by matured clinical data sets, regulatory support, enhanced skill sets, clinician buy-in, and alignment of GenAI use cases with organizational priorities.

For additional reference, please access IDC Health Insight’s report GenAI in Healthcare and Life Sciences: Current Trends and Future Potential in Asia/Pacific.

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

Hyper-experimentation with Generative AI (GenAI) dominated the conversations of business and technology leaders in organizations of all sizes, across all industries, and in all countries for the past 18 months.

Checking in with CIOs and business leaders 18 months later, we can report that a typical enterprise identified hundreds of GenAI use cases. They launched dozens of Proofs of Concept (POCs), but they put less than six into production, so far. This GenAI scramble is not sustainable for enterprises, or for technology providers who care about converting POCs into sustainable, long-term business.

Is it time to write off GenAI as just another over-hyped tech story that generates lots of bubbles, but doesn’t have a lasting impact? Uh…no. Doing so would also be a big mistake.

The focus on GenAI experimentation obscures the reality that most organizations are already invested in AI across their business. We surveyed 889 IT leaders in May 2024, and 84% believed (42% strongly) that AI/GenAI is the next strategic corporate workload like ERP or ecommerce was before. AI is already embedded into how they engage with customers, how they monitor activities in their factories and warehouses, and how they automate tasks such as “procure to pay”.  They also know that securing employees’ devices and their critical systems depends upon aggressive use of AI by security product and services providers.

The casting of a wide net when it comes to GenAI experimentation increases your CIOs’ awareness of the extent of overall AI use, but also shows how fragmented and even duplicative that use is. Your business and IT leaders setting 2025 tech investment plans need to develop an enterprise wide-AI strategy, building on the “lessons learned” while doubling down on the demonstrated benefits of GenAI for boosting business outcomes.

2025 will be the year of the AI Pivot. How effectively you set priorities, make decisions, and address barriers will decide if you are ready to fuel business growth on an AI foundation or will still be racing to catch up a couple years from now.

Where should you start? IDC’s AI Adoption Model

How severely were your GenAI experimentation efforts limited in the following areas?

Strategy: Depth of relationship between business and tech teams in PoC prioritization, development, and execution is a key success factor. Poor coordination between IT and lines of business (LOBs) is one of the most often cited factors contributing to low success rates. My colleague Ewa Zborowska has some good suggestions on how to reduce Pilotitis. The critical next step? Building a use case prioritization roadmap.

Governance: GenAI experimentation across functions and with ties to multiple data sets overwhelms narrow, siloed IT governance processes. Organizations with high success rates noted their ability to quickly integrate responsible AI into strong, comprehensive governance practices. Especially important, they have solid, cross-functional data-sharing governance practices.

People: Most early POCs focus on individual actions or basic process improvements, exposing significant if under-appreciated bottlenecks. What is most exposed, however? Disconnects between senior executives and employees on the consequences. IT teams are caught in the middle as promised productivity gains fall short due to lack of training or fear of consequences.

Apps: “Now (or Coming Soon) with GenAI” is a recurring theme for technology providers. The infusion of GenAI into business, IT Ops, data management, and developer apps affects decisions on which POCs to pursue and raises the stakes in “build versus buy” decisions for production launches. The biggest ask? “Please, help us show quantifiable value!”

AI Platforms: Companies are already using diverse AI platform components across a wide range of individual AI efforts. The GenAI scramble increases the use of disparate and nascent tools and technologies across AI and GenAI specific lifecycles strains resources. What’s missing? Reusability and scale.

Data: The GenAI scramble highlights the importance and exposes weakness when it comes to identifying, quality assuring, and integrating data sets for production launches. Access to high quality data contributes to high rates of success. Past decisions to treat data during app development as a byproduct or waste product, with no thought about the importance of metadata, however, means too much “dark data”.

Infrastructure: Despite the hype about lack of access to infrastructure (extremely expensive GPUs), most enterprises do not see this as a major issue. Currently, siloed infrastructure solutions and existing as-a-Service funding models support hyper-experimentation. Where they fall short is scaling for production. Excessive costs “at scale” break ROI calculations.

What’s Next?

Prioritizing goals and investments will vary depending upon how significantly you were affected in all 7 areas. The AI pivot is about reaching the end states you need to succeed in each.

You are ready to accelerate business growth and competitive success with an AI-fueled business operating plan covering organization, culture, resources and operations. AI, not just GenAI, is fully integrated into your enterprise business strategy. It includes a targeted set of AI/GenAI super use cases that deliver maximum business impact across multiple processes and domains. You are also setting up a multi-stakeholder, unified AI governance model that aligns with your AI-fueled strategy. Most importantly, you are ensuring that effective use of AI assistants, advisors, and agents is at the core of AI-aware workforce planning and training.

Of course, setting up an AI-fueled business model is irrelevant if you aren’t shifting to an AI-ready technology operating model, ensuring that you can cost effectively and securely scale the use of AI capabilities anywhere. You are confident that you can track the costs and benefits associated with AI-infused processes & apps. You are moving towards adoption of a unified AI platform that improves data and model use as well as app dev/deployment.

You are addressing the “dark data”  with AI-Ready Data based on the adoption of a “managing data as a product” strategy, ensuring that quality, accessibility, and governance of data isn’t an afterthought. Finally, infrastructure is no longer siloed, and scaling costs are no longer an impossibly high barrier to innovation. Your tech operating model is built on infrastructure that is interoperable, fit-for-purpose, and intelligently optimizable based on workload-specific cost, placement, and scale requirements.

Executing an AI Pivot may appear daunting and will require commitment across the organization, but there is good news.

Hyper-experimentation was a necessary step. You have more clarity about GenAI uses in your organization, and you gained that clarity much faster than companies ever have before.
GenAI tools and techniques advanced significantly during hyper-experimentation, making it faster, cheaper and easier to roll out prioritized super uses cases.
You’ve gained more experience with the potential benefits and disruptions of GenAI in your own POCs and in the products/services you are using from tech suppliers.

IDC is ready to help you quickly assimilate all the “lessons learned” and help you find the products, services, and partners that can help you execute the AI pivot and accelerate to the next level.

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

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.