For greater than a decade, Forrester has been dedicated to researching AI and ML applied sciences and platforms. Throughout my 13-year tenure at Forrester, I had the privilege of working alongside our gifted AI analysts. Collectively, we’ve got repeatedly refined our market definitions and analysis focus to remain aligned with rising tech traits and enterprise wants. On this weblog, we introduce a brand new branding strategy for the AI and ML platform market, guaranteeing our insights stay related and helpful for our shoppers.
A Decade-Lengthy Journey In Serving to Purchasers Innovate With AI
Here’s a fast snapshot of Forrester’s protection of AI and ML applied sciences and platforms:
In 2015, we (kudos to Mike Gualtieri and Rowan Curran) pioneered Forrester’s analysis within the discriminative AI subject named predictive analytics. This analysis helped enterprise shoppers by offering actionable insights to anticipate buyer conduct and optimize decision-making to drive effectivity and income development.
In 2017, we rebranded the market as predictive analytics and machine studying in response to the rise of ML and deep studying (DL). This rebranding helped enterprise shoppers assess instruments that additionally leverage superior ML and DL strategies.
In 2022, we expanded this definition to AI/ML platforms, reflecting a broader view of AI with ML/DL on the core. This supplied our enterprise shoppers a broader perspective to undertake full-lifecycle AI/ML options, together with integrating them seamlessly into their surroundings to drive AI innovation in enterprise processes.
In 2023, within the China model of AI/ML platform Forrester Wave™, we included extra functionalities of basis mannequin help to replicate the market traits of generative AI (genAI). This Chinese language market analysis focuses on enterprise shoppers in China or doing enterprise in China to harness genAI capabilities, unlocking new alternatives for content material creation, automation, and personalised buyer experiences.
In 2024, in Forrester’s international AI/ML platform Panorama and Wave, we formally outlined genAI as one core use case with devoted analysis standards. We additionally emphasised AI readiness by incorporating DataOps into our framework.
Additionally in 2024, we printed the devoted Panorama and Wave analysis on AI basis fashions (FMs) for languages (AI-FML, aka giant language fashions [LLMs]). This genAI-focused analysis assists enterprise shoppers to judge LLMs to assist help quite a few genAI use instances.
Over the previous 18 months, AI expertise has seen exceptional developments. FMs have emerged as a cornerstone of contemporary AI, driving innovation and scalability. These fashions have led to breakthroughs in numerous domains, together with mannequin algorithms, retrieval-augmented technology (RAG), AI brokers, and AI {hardware} infrastructure. Companies worldwide are actively experimenting with these applied sciences, integrating AI into numerous functions to reinforce effectivity and drive development.
The Convergence Of AI/ML Platforms And FMs
The AI/ML platform and FM markets are quickly converging via two key traits. AI/ML platform suppliers are increasing their FM capabilities throughout your entire AI growth lifecycle — from knowledge administration to mannequin growth, deployment, and AI utility growth (significantly in brokers, app technology, and agentic workflows). These platforms are additionally integrating with widespread third-party fashions to raised serve builders. In the meantime, FM distributors are broadening their choices to incorporate complete platform options like API integration, information retrieval, and agent growth instruments. As our analysis reveals, enterprises sometimes don’t depend on a single LLM however fairly combine a number of fashions as important elements of their broader AI infrastructure.
The convergence of AI/ML platforms and FMs signifies a profound transformation in AI adoption throughout 4 key dimensions:
From discriminative duties to extra generative duties. AI has transitioned from primarily performing predictive analytics to producing new content material of varied modalities. GenAI is being utilized in numerous fields, akin to content material creation, customer support, doc automation, and TuringBots. This development highlights the rising significance of AI in augmenting human capabilities, automation, and increasing the boundaries of what machines can obtain.
From task-specific fashions to FMs. Enterprise AI has advanced from specialised fashions requiring domain-specific coaching to large-scale FMs pretrained on huge datasets that may be tailored for a number of use instances via fine-tuning and prompting. These FMs operate as versatile constructing blocks that may be personalized via fine-tuning and compression strategies. Organizations can adapt these pretrained fashions for particular use instances with out the in depth knowledge and computational necessities of conventional coaching approaches. This paradigm shift has dramatically accelerated AI growth cycles and optimized useful resource utilization, enabling speedy deployment of AI functions throughout various enterprise contexts.
From centralized deployment to heterogeneous structure choices. AI deployment has advanced from centralized approaches to quite a lot of heterogeneous choices throughout multicloud, hybrid cloud, and edge. This shift gives structure choices to attain the correct steadiness of scalability, resilience, and adaptableness. This enables AI platforms to function effectively in various and dynamic environments, respecting knowledge gravity and optimizing efficiency and value. This development is especially essential for functions that require real-time processing and low-latency responses, akin to autonomous automobiles and IoT edge workloads.
From tightly prescribed conduct to larger autonomy and self-improvement. AI methods are shifting from predetermined eventualities that rely closely on human design and planning, to extra autonomous approaches. With adequate intelligence, AI brokers have the potential to adapt to new eventualities via iterative studying, planning, and collaboration, making them goal-oriented, proactive, and environment-aware. This autonomy permits AI to deal with complicated and dynamic duties with larger effectivity and effectiveness, decreasing the necessity for fixed human oversight. The event of autonomous AI is paving the best way for superior functions in robotics, healthcare, and different fields the place adaptability and decision-making are essential.
Rebranding The Market To “AI Platform”
On account of this convergence, ranging from this 12 months we’ll fold within the AI-FML into this bigger platform and additional evolve our market terminology into “AI platform.” We are going to repeatedly refine our analysis round enterprise use instances, key functionalities, and analysis standards design, aiming to assist our enterprise shoppers in refactoring or redefining your expertise methods in AI adoption. For extra particulars, or if you need to share your ideas on this, please guide an inquiry or steerage session with us to debate.
