S&P Global Market Intelligence provides essential insight into the pace and extent of digital transformation across the global technology, media and telecom industries. Through the 451 Research and Kagan products, our team offers differentiated insight and data on adoption, innovation and disruption across the technology, media and telecom markets, backed by a global team of industry experts, and delivered via a range of syndicated qualitative and quantitative research, strategic consulting solutions, go-to-market services, and live events.

Our research is organized into channels that align with the prevailing topics driving digital transformation. The research agenda for each channel outlines key themes and questions our research will address and analyst coverage and planned deliverables over the coming year to support our clients’ critical business decisions. The nature of this transformation means that many trends, such as generative AI, span multiple channels. Our research approach encourages analyst collaboration both within and between channels and other deep sector business units across S&P, allowing us to surface emerging trends before anyone else.

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Table of Content

Analyst Team

Research Areas

Hybrid cloud storage for a hybrid IT landscape

Coverage Analysts

Henry Baltazar, Craig Matsumoto, Jean Atelsek

Key topics & questions addressed

  • Data repatriation is becoming more common for organizations and is driven by several factors including data sovereignty requirements and cost concerns.
  • Egress charges continue to have a negative impact on the hybrid and multicloud aspirations of organizations.
  • Rapid data growth and limited budgets are forcing organizations to look beyond on-premises storage and data protection resources to leverage cloud services.

Enterprise edge infrastructure: innovation and opportunity

Coverage Analysts

Tiny Haynes, Brian Partridge, Henry Baltazar, John Abbott, Mike Fratto, Dan Thompson, Kelly Morgan, Rich Karpinski

Key topics & questions addressed

  • Which use cases are driving enterprise edge deployments?
  • Which workloads are supporting (or will support) these deployments? Where will they be deployed (edge, cloud, hybrid), and how (VM, containers, bare metal)?
  • What are the infrastructure requirements to support these deployments (compute, storage, networking, accelerators, power, footprint, noise level, ruggedization, etc.)?
  • What is the business case for adopting edge?
  • How will the colocation market need to adapt to meet edge demand?
  • How will edge be orchestrated and run?

Securely and reliably interconnecting the edge

Coverage Analysts

Tiny Haynes, Mike Fratto, Craig Matsumoto, Brian Partridge

Key topics & questions addressed

  • How will edge connectivity demands evolve over time?
  • How will enterprises stitch together networking capabilities into a reliable, manageable and cohesive network?
  • What is the competitive and cooperative landscape for edge networking?
  • What is the role of software-defined wide area networking (SD-WAN), cloud-native WAN, wireless and traditional connectivity for the edge?
  • What is the opportunity for low-Earth orbit satellites when building edge networks?

Assessing workload venues for AI — the rise of the edge

Coverage Analysts

Tiny Haynes, Alex Johnston, Brian Partridge, Rachel Dunning, John Abbott, Rich Karpinski

Key topics & questions addressed

  • Public cloud is the most popular venue for three stages of the machine learning process, but the edge is a key location for data collection used to build and train AI models.
  • Edge can mean many things, whether device edge, enterprise edge or network edge. By any of these definitions, edge is important in the evolution of AI both as a point of data collection and model inference.
  • What challenges do organizations face if they want to do more inference and training at the edge?
  • What are the use cases for more AI at the edge?
  • How will infrastructure be affected by efforts to support AI at the edge?

A total approach to enterprise automation — total automation

Coverage Analysts

Carl Lehman, Jay Lyman

Key topics & questions addressed

  • How low-/no-code digital automation platforms and robotic process automation (RPA) technology are converging and assimilating with process discovery technologies, hybrid integration platforms and generative AI to enable a new breed of intelligent automation platform.
  • The role of generative AI in process automation and how AI-driven processes get smarter as they execute to make recommendations, predictions and decisions.
  • The emergence of event-driven application development and how it affects enterprise automation strategy.

The current and future state of hybrid integration platforms

Coverage Analysts

Carl Lehman, Alex Johnston

Key topics & questions addressed

  • The ever-changing enterprise IT estate and the trends that drive the need for new forms of integration technology.
  • The role of generative AI in application, data and process integration and its implications for the enterprise.
  • The future state and capabilities of hybrid integration platforms.

Impact of AI on observability

Coverage Analysts

Mike Fratto

Key topics & questions addressed

  • How is AI being applied to observability data and platforms?
  • How are vendors improving enterprise confidence with AI?
  • What are the differentiating use cases that are attractive to the enterprise?

Cloud-native networking to support dynamic connectivity

Coverage Analysts

Mike Fratto

Key topics & questions addressed

  • What is driving the demand for cloud-native networking?
  • How are the products and services positioned in the market?
  • What differentiators are important to enterprise IT buyers?

Generative AI-assisted software development and IT operations

Coverage Analysts

Jay Lyman, Nick Patience, Alex Johnston

Key topics & questions addressed

  • Whether it is code generation, testing, security, documentation, or other parts of the development process, generative AI holds the promise of reducing manual tasks to free software developers to focus on new applications, features and innovation.
  • Generative AI is also playing an increasingly prominent role in IT operations, with a role in configuration, orchestration, reliability testing, incident response and other aspects of IT management.
  • While these benefits are sure to drive generative AI in both software development and IT operations, there are legitimate concerns about the technology, including data privacy and security, measuring and proving effectiveness, lack of skills and vendor support.

Serverless matures and evolves in the enterprise

Coverage Analysts

Jay Lyman, Jean Atelsek, William Fellows

Key topics & questions addressed

  • Adoption of serverless is growing in the enterprise, driven by maturing technology and adoption, open-source projects and a growing number of cloud-first companies seeking to offload traditional IT management tasks, such as configuration and provisioning.
  • Serverless technology has evolved beyond functions as a service and compute. Additional serverless services including storage, database and monitoring are expanding the applicability of serverless to more software and teams.
  • While deployment of serverless continues to grow across verticals, its traction comes alongside, rather than at the expense of, other cloud-native approaches. A growing number of organizations are leveraging serverless for new and existing applications, but this does not replace nor disrupt their existing cloud-native journey built on containers, microservices and Kubernetes.

Enterprise utility and readiness for a quantum future

Coverage Analysts

Ellie Brown

Key topics & questions addressed

  • With different qubit architectures jockeying to be thought of as the “leading” quantum technology, how will these differences influence workload placement?
  • Early attempts at networking quantum computers are gaining traction and meriting venture capital investment. How could a quantum internet impact the development of quantum cloud services?
  • What are the latest developments in quantum technologies (hardware, software, cloud platforms, networking and internet, control and readout systems, and sensing)?
  • As quantum technology develops, how do gains made in one area (i.e., quantum hardware) drive development and evolution across the entire field?
  • What are some of the key business problems identified as quantum-suitable?

Semiconductor innovations and disruptions

Coverage Analysts

John Abbott, Eric Hanselman, Craig Matsumoto

Key topics & questions addressed

  • How will the new world of “accelerated computing” affect the current general-purpose compute platforms that predominate in the enterprise and the cloud?
  • What challenges do customers face in moving to accelerated platforms, and what are the timescales?
  • How will global supply chains evolve to meet current demand and ensure national supplies and security in the future?
  • Chiplets and tiles: the emergence and implications of a critical new silicon ecosystem.

The AI infrastructure arms race

Coverage Analysts

John Abbott, Nick Patience, Perkins Liu, Alex Johnston

Key topics & questions addressed

  • What are the competitive advantages and disadvantages between the “GPU rich” and GPU poor”? How critical is timely access to modern infrastructure for large language model training and inference?
  • Competition is mounting in a sector that has long been dominated by a single supplier. What are the options going forward?
  • Routes to market: how generative AI infrastructure is playing out at high-performance computing research labs, hyperscale operators and specialist cloud service providers.
  • Cloud-native silicon: How new approaches to silicon design married to a full-stack software model can influence the performance, flexibility and power efficiency of a giant cluster.

Tracking telecom industry transformation: Network cloudification must drive monetization

Coverage Analysts

Brian Partridge

Key topics & questions addressed

  • When will O-RAN finally cross the chasm?
  • What will accelerating 5GSA deployment globally impact areas such as network slicing, private networks and network exposure?
  • How will telecom operators leverage secure access service edge and multi-access edge computing maturity to drive managed security/platform services tied to public telecom networks?
  • What opportunities do emerging workloads such as generative AI and augmented reality/virtual reality/metaverse create for public/private 5G providers?
  • How will operators take advantage of the CPaaS opportunity?
  • Hyperscale cloud providers are aggressively seeking to disrupt the telecom industry to capture more network workloads — beyond greenfield deployments such as DISH. Where are they seeing the most traction and why?