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 this is real value, it rarely is 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.
While a smart city is powered by technology and data, it is enabled and sustained by trust. Many people equate trust with privacy and cybersecurity. However, these are only two elements of many that create trust in a smart city. Trust must be embedded into the processes, policies and technology that create the city services. It must be integrated into its creators, users and beneficiaries from the very beginning.
This article introduces a holistic framework for building trust in a smart city for smart city planners and architects to consider.
Smart city innovation labs provide an organized structure for cities, community, experts, and vendors to collaboratively create viable solutions. Successful solutions piloted in smart city innovation labs can then be scaled and deployed into a city’s operations and infrastructure.
Based on our experiences in creating, launching and operationalizing San Mateo County’s Smart Region innovation lab (SMC Labs), we share ten best practices for civic innovation leaders planning their own innovation labs.
Whether you are planning or have already started your smart city journey, there are eight things that cities must get right. While smart cities are often associated with technology, it is but one component within the larger smart city ecosystem that needs to be addressed.
This article discusses the eight things that leaders and planners must get right, in order to build the sustainable and well functioning smart city.
The smart city is a complex ecosystem of people, processes, policies, technology and other enablers working together to deliver a set of outcomes.
Despite this, many planners today are not taking an ecosystem approach to smart city projects. This article introduces the smart city ecosystem framework, a more holistic and multi-dimensional approach, to building more sustainable, scalable and relevant smart cities.
IoT enables vendors to create an entirely new line of “smart” solutions for its existing and new markets. While the decision to go “smart” is straightforward, the decision of how to go “smart” is less obvious. Vendors are faced with a “build, buy, partner” decision – build it themselves, buy and integrate technology, or partner with another organization and go to market together.
This article discusses some of the key management considerations involved in making a decision.
Are you ready for IoT? Despite its transformational potential, most organizations are not ready. In an era of rapid disruption and digital transformation, IT executives and managers must lead the charge. They must bridge the gap between technology, business, engineering and operations. They are evangelists, teachers, facilitators and innovators.
This article lists six things IT managers must do to successfully accelerate IoT adoption within their organizations.
We are in a modern day gold rush, sparked by the Internet of Things (IoT). Thousands of companies, new and established, are planning “smart” solutions.
Amid all this, what IoT lessons learned from the past can today’s IoT companies apply to avoid mistakes of previous “gold rushes”? Are we smarter now, or are we making the same mistakes? The answer is yes and yes. This articles identifies five major mistakes made by dot-companies and five Iearnings for today’s IoT companies.
The real innovation of smart parking solutions is not in the technology. While smart parking solutions bring immediate value to drivers, parking enforcement agencies and cities, the real innovations and value will emerge once it is deployed and new beneficiaries emerge.
Smart parking is not all about parking. This article describes who the new beneficiaries are, shares examples of where new value is being created, and lists best practices to uncover real innovations.
In the digital enterprise, the strategic fusion of IT, operations technology [OT], audio-video [AV] with transformational technologies (Cloud, Internet of Things, AI, analytics, edge processing) leads to richer customer experiences, business acceleration, and operational agility. This fusion leads to new innovation and digital transformation of the organization.
This article highlights the new roles and expectations of IT in an age of digital transformation.
Don’t confuse IoT with innovation. The real IoT innovation is not in the technology, but in what it can do and what it allows organizations to become – intelligent, agile and adaptive, in creating new value for its customers.
This article describes five innovation paths with IoT solutions to consider when planning digital transformation projects, along with advice to get started on turning the Internet of Things into the Innovation of Things.
One common monetization approach for today’s IoT solutions is the subscription model. While it provides an attractive recurring revenue stream, subscription models require major investments in resources, time, capital and management commitment.
In this blog, we’ll highlight six key strategic and critical considerations managers must address when planning and building IoT subscription models. These considerations will determine whether the IoT subscription service is successful or not.
Despite the disruptive and transformative potential of IoT, selling IoT solutions is today’s emerging marketplace is challenging. Buyers face many barriers, ranging from a lack of awareness to fears of early obsolescence.
In this blog, we’ll share the eight things IoT solutions vendors must stop doing right away. Instead, we’ll share eight alternative best practices and strategies that they should be doing instead to drive market adoption.
Buyers face a dilemma with buying IoT solutions today. Buy an immature solution now and risk obsolescence in the near future, even if the solution has value for them today, or hold off buying until things become clearer.
In this blog, we will share common causes of obsolescence and a framework for futureproofing your IoT infrastructure. We will list some tactical practices to put in place to maximize the useful life of your IoT solution.
A recent study by Cisco found that three quarters of IoT projects were not successful. The reasons included long completion times, poor data quality, lack of internal expertise, integration and budget overruns.
In this blog, we will share ten best practices, from project planning to implementation, to help managers and project planners overcome common mistakes made with projects implementing emerging technology solutions.
Today’s IoT market is very dynamic, continuously evolving, and fragmented. No single vendor has a connected IoT stack. Partnerships are a critical business capability that IoT vendors must develop in order to be relevant in this type of marketplace.
This post, the second of two parts, describes ten best practices that vendors should use when forming and managing partnerships with other IoT partners.
Today’s IoT market is very dynamic, continuously evolving, and fragmented. No single vendor has a connected IoT stack. Partnerships are a critical business capability that IoT vendors must develop in order to be relevant in this type of marketplace. This post, the first of two parts, describes the basic partnership types, the relationship models and key engagement scope parameters.
Internet of Things (IoT) solutions offer tremendous and disruptive value for customers, but sometimes have the unintended effect of adversely impacting the channel that it is sold and serviced through. This results in slow adoption of IoT solutions, even if those solutions have significant and tangible customer value. This post highlights the two common product-market fit mistakes, and lists four best practices to facilitate channel adoption of innovative IoT solutions.
IoT or Internet of Things solutions, built on a cloud-based infrastructure, create opportunities for new business models and value delivery methods. While many IoT solutions are usually sold as a “product”, many vendors are now beginning to offer IoT “as-a-service”.
Selling a recurring revenue solution is not the same as selling an “one time” sale product. This post presents seven best practices for selling IoT as a service.
You found product-market fit and built your Internet of Things (IoT) solution. But do you know who your buyer is?
IoT solutions create value that cut across organizational boundaries. Identifying a single buyer or owner in today’s traditionally structured organizations is difficult. Unlike IT where there is a centralized buyer, IoT buying is decentralized.
This post describes the reasons for this, and provides six best practices for selling IoT solutions into corporate organizations.
A lot of the innovation around Internet of Things (IoT) is coming from start-up companies. But how do you buy a solution when the technology is still evolving, the use cases are emerging, and the company selling it may not be in business a year from now? Your “tried and true” sourcing practices that you use with your more established suppliers will actually increase the risks for both you and the start-up company that you are buying from.
This post describes the three main risks and highlights ten new strategies for buying IoT solutions for start-up companies.
There is no lack of IoT platforms, business models, and innovative products in the market today. However, today’s products are still immature point solutions. From technology standards, to business models, IoT will evolve over the next few years.
Given the immature state of IoT, what should transformation, business and IT managers do today?
This article discusses five things that managers should do now as they plan and deploy IoT strategies and projects.
Recently, I met with key executives from an industry trade group to discuss disruptive innovation in Silicon Valley.
One key concept I shared with them is that although many people equate innovation with technology, there are actually five types of innovation and that some or all may occur at the same time.
This post describes the five types of innovation, and provides managers with three takeaways to guide their innovation strategy.
Are you ready? The Internet of Things (IoT) is here and its ability to drive new innovation will be huge.
But how much is real and how much is hype? Today’s IoT are “point” solutions that don’t live up to the hype. They offer limited utility and solve a small set of problems.
This articles discusses what IoT will look like when it lives up to the hype. It provides fives takeaways for managers to do today to begin the transformation from Internet of Things to Innovation of Things.