Introduction: Generative AI is widely considered the fastest moving technology innovation in history. It has captured the imagination of consumers and enterprises across the globe, spawning incredible innovation and along with it a mutating market ecosystem. Generative AI has also caused a copious amount of FOMO, missteps, and false starts. These are the classic signals of technology disruption—lots of innovation, but also lots of mistakes.
In this time of AI disruption, who has shined? In my view, it was those who are building and delivering pragmatic, enterprise-grade solutions. Those who have been invested in and have understood AI for some time. Those who have clear visions and goals for end results. They are AI innovators. With that in mind, I developed a list of companies and products that I felt were the AI innovation leaders of 2023. Two criteria factored heavily into the thinking: (1) enterprise focused (not consumer), and (2) general availability in 2023.
Here are the categories for AI Innovation Leaders of 2023:
- AI Product of the Year
- AI Company of the Year
- AI Stack of the Year
AI Product of the Year
In my view, five nominees floated to the top:
- Qualcomm’s Snapdragon X Elite and Snapdragon 8 Gen 3 SoCs
- Meta’s family of Llama AI models
- Adobe Firefly
- Microsoft Copilot
- IBM watsonx.governance
My winner is: Adobe Firefly
Here is why: Adobe Firefly is the most commercially successful generative AI product ever launched. Since it was introduced in March in beta and made generally available in June, at last count in October, Firefly users have generated more than 3 billion images. Adobe says Firefly has attracted a significant number of new Adobe users, making it hard to imagine that Firefly is not aiding Adobe’s bottom line.
The runaway success of Adobe Firefly is not a fluke. It is the result of a long-term investment by Adobe in the promise of AI. The methodology, culture, and process that Adobe brings to bear for leveraging AI are the methodology, culture, and process other enterprises should aspire to if they want to leverage AI to their own benefit. For more details, see Adobe Firefly: Blazing a Generative AI Application Trail.
Runner up: Qualcomm’s Snapdragon X Elite and Snapdragon 8 Gen 3 SoCs
Here is why: Qualcomm’s latest Snapdragon SoCs seem poised to introduce legitimate on-device AI, a concept that might seem to defy the current convention of compute-munching AI workloads. Not many players make GPUs. Even less make GPUs for “mobile” devices, primarily PCs and smartphones. Four to think of are Qualcomm, Intel, AMD, and Apple. Of those, which have been thinking about on-device AI the most and for the longest? Probably Qualcomm and Apple.
Qualcomm has been working with AI for more than 10 years. All that time, the company has been thinking about on-device AI. The only difference today is an even bigger opportunity with generative AI.
Qualcomm seems to be further along in terms of the partnerships and ecosystems to support on-device AI than others—including its AI stack and maybe a bigger developer ecosystem (particularly around mobile). Qualcomm has also thought pretty deeply about pragmatic and logical use cases for on-device AI such as camera-based use cases and video conferencing. For more details, see With Snapdragon, Qualcomm Sets the Pace for On-Device AI.
Runner up: Microsoft Copilot
Here is why: If Copilot rolls out smoothly, AI will become a mass market technology within the next 18 months. Microsoft will be tasked with educating the world and training the world how to best use AI tools.
If Copilot can seamlessly orchestrate apps as envisioned, work and personal productivity will rise simply based on Microsoft users alone, and fundamentally change the way we interact with software. Its success or even the promise of its success will spur even greater investment by enterprises to leverage the power of AI.
Copilot success will solidify Microsoft’s stranglehold market share for Windows OS and Microsoft 365 applications. It could create greater opportunities for Microsoft to gain market share in enterprise applications the company does not currently dominate, such as sales/marketing/customer relationship management (CRM) solutions such as Salesforce and Adobe and enterprise resource planning (ERP)-type software (SAP, Oracle, ServiceNow, etc.). Microsoft Teams becomes more powerful and increases Microsoft’s potential to grab more market share in collaboration tools. Finally, a Copilot success might give Microsoft a chance to break Google’s dominance in search. For more details, see Microsoft Copilot Will Be the AI Inflection Point.
Runner up: Meta’s Llama Models
Here is why: Meta has been a champion of two important trends: (1) open source AI models and (2) smaller language models. The Llama models, particularly starting with Llama-2 in July, have enabled countess developers and enterprises to launch AI initiatives and to experiment with how to leverage their proprietary data in that regard. Perhaps more important, the Llama models have quickly become proof points for the impact and effectiveness of smaller language models, delivering better results and requiring significantly less compute power to do so. Meta’s work has sparked more open source AI models and opened the door for on-device AI.
Runner up: IBM watsonx.governance
Here is why: AI risk management is foundational and critical to operationalizing AI. Enterprises will learn this either the hard way, through ignoring it, or the easier way, by embracing it. IBM is in a great position to help enterprises navigate AI risk management. IBM is one of the AI pioneers and an innovator in the AI space. Along with a handful of other companies, IBM has thought about and worked with AI for many years. That experience comes into play when thinking about how to operationalize AI, and what it takes to be successful with AI. IBM has gone through this process—it has had the time to think about what AI is and to experiment with how to use it. With the benefit of that experience, IBM understands AI risk and the AI lifecycle. For more details, see IBM watsonx.governance Tackles AI Risk Management.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Mark comes to The Futurum Group from Omdia’s Artificial Intelligence practice, where his focus was on natural language and AI use cases.
Previously, Mark worked as a consultant and analyst providing custom and syndicated qualitative market analysis with an emphasis on mobile technology and identifying trends and opportunities for companies like Syniverse and ABI Research. He has been cited by international media outlets including CNBC, The Wall Street Journal, Bloomberg Businessweek, and CNET. Based in Tampa, Florida, Mark is a veteran market research analyst with 25 years of experience interpreting technology business and holds a Bachelor of Science from the University of Florida.
