Knowing where your organization stands across nine layers of AI capability is the beginning, not the end. Part 2 of our enterprise AI framework series moves from diagnosis to action: how the SoT AI Operating Model drives build-buy-partner decisions, use case prioritization, market research, and AI strategy, and the questions every operational and technology leader should be asking right now.
Eighty-eight percent of organizations use AI. Only 31 percent have scaled it enterprise-wide. The gap is not the technology. It is the structure surrounding it. The SoT AI Enterprise Reference Model is a nine-layer enterprise AI framework that maps every capability an organization needs to build and sustain AI at scale, from infrastructure through strategy, technical and organizational dimensions treated as one inseparable system.
AI tools are not an AI strategy. Most mid-market companies are investing in AI that makes their existing operations faster or cheaper. While it is real value, it is rarely the kind that changes a competitive position.
This post discusses how the companies that are pulling ahead are thinking about it differently.
AI pilots are succeeding. Enterprise impact is not following. Across industries, organizations are accumulating pilot wins while the structural and infrastructure gaps that prevent those wins from reaching business performance go unaddressed.
This article draws on direct observations from fractional Chief AI Officer engagements to identify the scaling problems most organizations are not talking about enough, and what leadership teams can do to close the gap.
AI infrastructure investments are moving faster than most organizations can manage. Platforms are fragmenting. Vendors are pivoting. Regulatory requirements are tightening. For mid-market companies, a wrong bet is not just expensive. It is a setback there is no easy budget to recover from.
This post introduces a practical framework for future-proofing your AI infrastructure: a continuous lifecycle management strategy that helps CEOs, COOs, and CAIOs protect their investments, maximize flexibility, and manage change deliberately. Includes a self-assessment diagnostic and five action steps you can apply before your next AI investment decision.
AI is everywhere. For most organizations, meaningful business impact is not. Whether you are running a large enterprise or a mid-market company where every AI dollar has to earn its return, the challenge is the same: AI initiatives emerge faster than the structure needed to manage them.
This blog introduces the AI Operating Function, explains why it is missing in most organizations, and outlines what leaders can do to close the gap.
Fifty-six percent of companies saw no impact from their AI projects. This highlights an uncomfortable truth: the main AI challenge most organizations face today is not building AI models; it is building environments where AI can operate reliably.
This blog discusses the four infrastructure barriers that block AI and outlines practical steps leaders should consider.
AI enables organizations to transform their operations, enhance responsiveness, resilience and facilitate customer experience. While the decision to adopt AI is straightforward, organizations are faced with a “build, buy, partner” decision – build it themselves, buy and integrate technology, or partner with another organization to co-develop it together.
This article discusses some of the key management considerations involved in making a decision.
Eighty percent of AI projects fail. One of the reasons is the lack of data to train the AI model. Many enterprises underestimate getting the right data from the right places they need to support AI-enabled systems and operations. Without this foundation, AI initiatives stall, underdeliver, or fail to scale.
This blog explores why data and connectivity are critical to enterprise AI success and outlines practical steps leaders should consider.
Generative AI for IoT provides significant value and transformational benefits for adopting businesses. However, generative AI is still an emerging and evolving technology, and its adoption brings a variety of challenges and risks to businesses considering its use.
This article provides business leaders with an overview and understanding of some of the key generative AI for IoT risks and possible mitigation approaches.
The use of generative AI for IoT is poised to revolutionize business operations, automation, and decision-making. By combining structured and unstructured data, generative AI for IoT brings new capabilities and intelligence to enhance processing and analysis of operational data.
This article provides business leaders with an understanding of what generative AI for IoT is, and five opportunities that it provides businesses who adopt it.
Four out of five AI projects fail. One of the top causes of these failures is related to the problems AI is asked to solve. If you fail to specify the problem correctly from the start, you’re setting yourself up for failure before the first algorithm is even written.
This article provides business leaders with best practices and practical guidance on finding and selecting the right problems that lead to successful AI projects.
AI is not one technology, but many different types of artificial intelligence technologies. Each type of AI has different capabilities, strengths, and weaknesses. Applying the wrong type of AI technology to a task can lead to poor results and unacceptable risks.
This article provides business leaders with a working overview of the different types of AI to educate and inform on strategic decision-making around artificial intelligence initiatives.
Most IoT applications are based on a cloud centric architecture. Data collected from sensors and devices are sent to a gateway, which then transfers it to a cloud based IoT platform. For a growing set of IoT applications, including those that are mission critical, latency sensitive, or with high reliability needs, a new architecture is needed. An edge based architecture, with processing performed at the device, or in a gateway near the devices, is now emerging. This article provides an overview of edge or fog computing, and lists some common use cases.
The Internet of Things is set to transform and disrupt what we do and how we do it. But there is a coming revolution – the integration of computer vision, machine intelligence, data analytics, with IoT that promises a whole new set of disruptions.
This post is Part One of a series of briefings on the convergence of computer vision and the Internet of Things (IoT). It discusses what computer vision is, the impact of advanced machine learning algorithms, examples of use cases, and current challenges.