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Artificial intelligence is poised to reshape the IT industry and the way businesses operate. That new forecast comes from market intelligence firm IDC, which predicts that enterprise spending on generative AI (GenAI) from now through 2027 will be 13 times greater than the growth rate for overall worldwide IT spending.

IDC forecasts enterprise spending on GenAI services, software and infrastructure will grow from$16 billion in 2023 to$143 billion in 2027. Spending on generative AI over the four-year period to 2027 is expected to reach a compound annual growth rate (CAGR) of 73.3%.

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To help organizations better understand how to leverage GenAI technology for business success, IDC developed a new framework -the Generative AI Path to Impact -that explains key activities and elements along the path.

The Generative AI Path to Impact

IDC

Before any of the core technologies of GenAI are explored, IDC believes that the following key activities need to be put in place:

• Establish a Responsible AI Policy: This must include defined principles around fairness, transparency, protections, and accountability relating to the data used to train models, as well as how the results are used. A responsible AI policy should also provide transparency on the roles and responsibilities of developers, users, and other stakeholders, while addressing legal and compliance issues.

• Build an AI Strategy and Road Map: A set of defined, measurable, and prioritized GenAI use cases is required to align the organization on the key areas that will deliver the maximum business impact in the short, medium, and long term.

• Design an Intelligence Architecture: Managing the life cycle and governance of data, models, and business context for every use case is critical. The architecture should also include protocols for data privacy, security, and intellectual property protection.

• Reskill and Train Staff: New competencies will be required to build and use GenAI models, such as prompt engineers to write and test prompts for GenAI systems. Every organization must create a new skills map for core AI technologies and business capabilities to deploy GenAI at scale across the organization. Organizations should also build personalized training programs for key roles.

Enterprises will leverage generative AI and automation technologies to drive$1T in productivity gains by 2026. -IDC

- Vala Afshar (@ValaAfshar) October 12, 2023

The next step in defining the path to GenAI impact is prioritizing an identified set of use cases. IDC defines a use case as a business-funded initiative enabled by technology that delivers a measurable outcome. There are three broad types of GenAI use cases that need to be assessed:

• Industry: These involve more custom work and, in some cases, may require organizations to build their own GenAI models. Examples include generative drug discovery in life sciences and generative material design for manufacturing. Specialized use cases tend to be built around specific models and model providers, with custom integration architectures designed for individual clients.

• Business Function: These use cases typically involve integrating a model (or multiple models) with corporate data for use by specific departments or business functions, such as Marketing, Sales, and Procurement. Many organizations are already testing these types of use cases but are concerned about intellectual property leakage and data governance.

• Productivity: These use cases are aligned with work tasks, such as summarizing reports, creating job descriptions, or generating Java code. GenAI functionality for productivity improvement is being infused into existing applications, such as Microsoft 360 Copilot or Duet AI for Google. For many of these use cases, business value can be delivered through the content and data that the underlying foundation models have been pre-trained on.

IDC recommends adopting a "three horizons" framework to help organizations transform their business models using GenAI. 

• Horizon 1 focuses on near-term, incremental innovation.
• Horizon 2 focuses on disruptive innovation in the medium term.
• Horizon 3 focuses on long-term business model transformation.

The framework drives alignment across all business domains and helps prioritize key initiatives.

Enterprise 2023 per @IDC:
? only 12% of enterprises connect customer data between departments
? 42% of enterprises have underutilized data - move from data generation to decision velocity
? 42% of core IT spending is cloud (shift from cloud first to cloud economics)#CNX23 pic.twitter.com/LSx6bUwXJh

- Vala Afshar (@ValaAfshar) June 8, 2023

IDC's predictions for 2024 are largely centered around the emergence of AI as a major inflection point in the technology industry. "Every IT provider will incorporate AI into the core of their business, investing treasure, brain power, and time," said Rick Villars, group vice president, Worldwide Research at IDC.  Here are IDC's 2024 top ten worldwide IT industry predictions:

1. Core IT Shift: IDC expects the shift in IT spending toward AI will be fast and dramatic, impacting nearly every industry and application. By 2025, Global 2000 organizations will allocate over 40% of their core IT spend to AI-related initiatives, leading to a double-digit increase in the rate of product and process innovations.

2. IT Industry AI Pivot: The IT industry will feel the impact of the AI watershed more than any other industry, as every company races to introduce AI-enhanced products/services and to assist their customers with AI implementations. For most, AI will replace cloud as the lead motivator of innovation.

3. Infrastructure Turbulence: The rate of AI spending for many enterprises will be constrained through 2025 due to major workload and resource shifts in corporate and cloud data centers. Uncertainty about silicon supply will be joined by shortcomings in networking, facilities, model confidence, and AI skills.

4. Great Data Grab: In an AI Everywhere world, data is a crucial asset, feeding AI models and applications. Technology suppliers and service providers recognize this and will accelerate investments in additional data assets that they believe will improve their competitive position.

5. IT Skills Mismatch: Inadequate training in AI, cloud, data, security, and emerging tech fields will directly and negatively impact enterprise attempts to succeed in efforts that rely on such technologies. Through 2026, underfunded skilling initiatives will prevent 65% of enterprises from achieving full value from those tech investments.

Also: Businesses need a new operating model to compete in an AI-powered economy

6. Services Industry Transformation: GenAI will trigger a shift in human-delivered services for strategy, change, and training. By 2025, 40% of service engagements will include GenAI-enabled delivery, impacting everything from contract negotiations to IT Ops to risk assessment.

7. Unified Control: One of the most challenging tasks for IT teams in the next several years will be navigating the maturation of control platforms as they evolve from addressing a few basic systems to becoming a standard platform that orchestrates operations across infrastructure, data, AI services, and business applications/processes.

8. Converged AI: Today's fascination with GenAI should not delay or derail existing or other AI investments. Organizations must contemplate, trial, and bring to production fully converged AI solutions that allow them to address new use cases and customer personas at significantly lower price points.

9. Locational Experience: The accelerated adoption of Gen AI will enable organizations to enhance their edge computing use cases with contextual experiences that better align business outcomes with customer expectations.

10. Digital High Frontier: Satellite-based Internet connectivity will deliver broadband everywhere, helping to bridge the digital divide and enabling a host of new capabilities and business models. By 2028, 80% of enterprises will integrate LEO satellite connectivity, creating a unified digital service fabric that ensures resilient ubiquitous access and guarantees data fluidity.

Artificial Intelligence

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