IDC

There’s no way around it. The time to plan for AI skills and roles is now. Just a year into the GenAI hype cycle, the question is no longer whether enterprises must skill up employees for the age of AI. It’s when and how they should do it. For leading organizations in every sector, the heat is on. According to IDC data excerpted in IDC’s Skills and the AI-Enabled Business study, some 40% of enterprise and line-of-business respondents believe their GenAI tech investments will continue in the foreseeable future. That was slightly ahead of their IT skilling investments, which 37% of respondents predicted would be the most persistent investment moving forward. FIGURE 1 : AI and Skills for Business Growth Source: Future Enterprise Resiliency & Spending Survey Wave 3, IDC, April 2024, N=887 The two topics – GenAI and skills – are directly related. Organizations in all sectors and geographies face a widening shortage of IT skills, including skills related to security, cloud, IT service management and AI. And in addition to being the subject of skills development, GenAI technologies can and do speed training. But there’s a wider aspect for enterprises to consider, too. Organizations must make sure leaders, employees, partners and, eventually, customers become fluent with the AI and GenAI-fueled tools and processes. Soon these will be foundational to most business processes. Amid growing IT skills shortages, the stakes are high. To remain competitive, global IT and business leaders must move to accept and deploy AI as a transformational force, one that will fully remake roles, skills requirements, and the very nature of innovation and creativity in the enterprise. This article presents three strategies business and IT stakeholders should embrace now to ready their enterprises for the age of AI moving forward. 1. Implement GenAI to Improve and Speed Training For All Skills As the IDC AI study notes, enterprises across geographies already face a severe IT skills shortage. Now they face rising demand for AI and GenAI capabilities, too, a reality that exacerbates the substantial skill gaps so many enterprises now report. Some 30% of IT and LOB leaders see skills and labor shortages as top risk factors for tech strategies and budgets in the coming year. In IDC’s Future Enterprise Resiliency (March 2024), 62% of IT leaders told IDC that a lack of skills had resulted in missed revenue growth objectives (IT Skills Survey, Jan. 2024). More than 60 percent say the dearth of skills also has led to quality problems and a loss of customer satisfaction overall. And IDC predicts that, by 2026, more than 90% of organizations worldwide will feel similar pain, adding up to some $5.5T in losses caused by product delays, impaired competitiveness and lost revenue. Notably, GenAI can help to improve IT training outcomes. In fact, more than half of IT leaders tell IDC they have already begun leveraging GenAI tools to create and update courses (42%) and analyze skill assessment interviews and transcripts (46%). Down the road, IDC expects IT training tools to add more AI and GenAI technology. That will allow for personalizing courses to the skills, roles and learning styles of employees, a development that should add up to faster and better skilling outcomes overall. At this writing, almost every major IT training platform has either announced or delivered GenAI features to help personalize training. These features include GenAI chatbot tutors for enterprise learners, simulations to help tech staff practice problem-solving and AI-powered games and challenges for skill and learning reinforcement. Some training programs now leverage AI to adapt content to the pace, learning style and role of the learner. Digital Adoption Platform (DAP) vendors are following suit, too. Most major players in the category now or soon will offer rich AI data analytics that help enterprises identify learning patterns and bottlenecks. 2. Focus on Developing a Holistic Array of Skills No enterprise survives on just tech skills alone. To compete in the age of AI. They also need digital business skills, leadership skills and human skills. Likewise, employees in HR, marketing and communications need to better understand the nature and technical dependencies of new applications essential for guiding their teams. Absent of sufficient technical knowledge and critical judgement about how things function, they will end up misguiding them. When considering skill gaps in the enterprise, be sure to account for the broad swath of skills employees will need to get the job done. Take time to map the skills each role now requires against what skills will be needed in the foreseeable future. 3. Deliver AI Training Across the Organization In addition to focusing on the technical, leadership and human skills around AI and GenAI, enterprises should take care not to lose focus on the humans who truly power a business. In a recent 2024 IDC survey, only 36% of organizations told IDC they are mandating AI and GenAI awareness. Enterprises must do a lot better than that. Without proper support and socialization to help humans learn how to work and partner with AI systems, AI initiatives will fall short. Start with awareness. That is the key to any successful tech implementation, but it is most critical with AI and GenAI which, understandably, can make employees nervous about losing their jobs. Implement mandatory training sessions, workshops and seminars to ensure that employees understand how AI and GenAI play into company strategy overall. They must understand how the organization intends to use such tech to enhance current roles and create new ones. Automation, after all, isn’t new. Employees should understand that GenAI and AI simply continue and accelerate the need for upskilling and cross-skilling. While dispelling any misconceptions about AI-based automation stealing jobs, leaders should highlight exactly how AI can augment human capabilities. Frame the promise of AI in terms of reassigning rote work, which allows employees to focus on more strategic tasks. FIGURE 3: Automation technologies impact on employees over 18 months Source: Future Enterprise Resiliency & Spending Survey Wave 3, IDC, April 2024, N=887 With

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

Gen AI is Transforming Experiences In this era of AI everywhere, one thing is clear: GenAI is not just another technological advancement. It is significantly impacting many aspects of enterprises – and experiences are a huge component of this. GenAI is truly becoming a game changer in terms of the unprecedented level of hyper personalization it brings to the table. Imagine a situation where you, as a customer, are ordering groceries online. A GenAI -enabled agent can anticipate ahead of time what items you tend to put in your cart and provide you with multiple alternatives of those out of stock, depending on your desired delivery date and time. Further, it can share with you active promos on items you tend to purchase without you having to look or search for them one by one. This would make my day to day tasks so much easier. The value discussion is evolving – moving away from traditional methods of measuring success of CX initiatives, where it is customer metrics such as customer satisfaction, or financial metrics such as revenue, and profitability. Organizations are looking to connect the value of their CX efforts to the impact on all the stakeholders in this experience ecosystem – internal and external. APJ organizations are recognizing this opportunity and moving fast to act on it. According to IDC’s FERS Wave 3 2024 Survey, 49.6% of APJ organizations are in initial testing stage, while 40.3% are investing significantly in GenAI. However, 55.2% organizations are still struggling to connect AI-powered applications and technology projects to business outcomes, according to IDC’s FERS Wave 1 2024 survey results. There is still a long path to traverse to realize value from potential of these modern technologies. Enter the Experience-Orchestrated Business (X-OB) Model To design experiences that span processes, applications, channels, and intelligent exchanges between the entire ecosystem of stakeholders, IDC has put forth the construct of the experience-orchestrated (X-O) business. An X-O business thrives due to its ability to deliver shared experience value powered by intelligence. To compete in an AI everywhere world, digital businesses must orchestrate a meaningful value exchange between the organization and their key stakeholders. Data is vital to intelligent applications embedded in daily operations and decision-making. Insights help align actions with desired outcomes and ensure that investments deliver the desired results for the experience-orchestrated business. Using AI-enabled technology to optimize journeys and automate workstream tasks, organizations can break down organizational silos and foster connectedness across the experience ecosystem. Where Does X-O Fit into the CX World This model provides a way for all the CX stakeholders to evaluate their capabilities across the four key pillars – connections, intelligence, culture, and actions. 1. Connections: This means transforming the environment we are working in towards more cross-functional collaboration, real-time data sharing, and integration. For customer service/support teams, this means they would be able to maintain context across interactions, reduce customer effort, provide more proactive customer engagement, and enhance their overall service quality. For marketing and sales teams, this means a unified brand voice, consistent communication, seamless transfer of leads from marketing to sales, and so on. When all three collaborate effectively, it can help unlock cross-selling/up-selling opportunities, integrated customer support, and consistency across channels and touchpoints. 2. Intelligence: Intelligence from automated processes can be used to optimize experiences further. Any new technology comes with risks – brand, data privacy, and compliance to name a few when it comes to GenAI. Building trust is critical – customers are becoming increasingly conscious and cautious about how and what data they are sharing across different apps and brands. Being able to do this effectively means customer support teams have automation in place to streamline customer service processes and speed up time to resolution. Further, AI is at the driver’s seat guiding them with context-aware prompts to reply to customers, or directly being able to address customer queries. Marketing teams can automate a greater number of manual tasks ranging from SEO, end-to-end campaign management, predicting future engagement trends, and identifying opportunity areas for improvement. There is also the element of being able to generate more relevant content, which includes hyper personalized campaigns, towards improved engagement and conversion rates. Sales teams would be empowered with the relevant customer context before calls, higher quality leads, and so on. 3. Culture: Culture, often ignored, forms a critical part of attracting, skilling, and retaining the right talent within an organization. More often than not, organizations tend to focus on output as a measure of success. This needs to change and become more outcome-oriented. For example, customer satisfaction from closed cases should be prioritized over number of cases (effective case resolution) closed in a given time interval (productivity). There is a need to establish joint and consistent metrics across CX, marketing, and sales. Service quality and the experience provided to the customer take precedence over productivity. CX, marketing, and sales teams gain incentives based on customer impact – how seamless they made the experience for the customer, continuous improvement based on predictive insights, recognizing sales reps who go the extra mile to resolve customer pain points over just those who bring in the most opportunities. 4. Actions: This refers to being able to engage stakeholders in a context-aware manner. This is a result of having the right tools and technologies in place to actively listen to the various cues (sentiment, intent, behavior, etc.), and convert into actionable insights. GenAI is great at consuming large amounts of structured and unstructured data. This large amount of data should be filtered to identify that of value. Once CX, marketing, and sales teams are armed with these insights – they can more effectively respond to customer needs ahead of the customer asking for it and in real time. All the customer micro-moments are opportunities to act fast, if you are slow, you lose out to many others in the market. Assessing where organizations are in their ability to have all these pillars in place, will help them identify the opportunity gaps