Innovation and Technology
7 Deadly Mistakes That Kill Most Enterprise AI Projects

The Expectation-Reality Gap
Think of AI projects like icebergs. What executives see in vendor presentations and tech magazines is the gleaming tip above water – the finished, polished success stories. What remains hidden is the massive underlying structure of data preparation, infrastructure requirements, talent needs, and organizational change management that makes those successes possible.
This expectation-reality gap is perhaps the most fundamental reason AI projects fail. There’s a persistent mythology that AI is a magical technology you simply "apply" to business problems like a high-tech bandage. The truth is messier and more demanding.
Flying Without Instruments: The Data Dilemma
If there’s one factor that dooms more AI projects than any other, it’s poor data quality and governance. Organizations consistently underestimate both the quantity and quality of data required for AI to function effectively.
The reality is that AI systems are fundamentally data processing engines. Feed them poor data, and you’ll get poor results – a principle computer scientists call "garbage in, garbage out" that has existed since the 1950s but somehow keeps surprising executives.
Missing The Human Element
Another fatal error is treating AI implementation as purely a technical challenge rather than a socio-technical one that requires human adoption and integration.
I recall a manufacturing firm that spent $1.8 million on an AI system to optimize production planning. The technology worked perfectly in testing, but on the factory floor, supervisors continued using their traditional methods and simply ignored the AI’s recommendations. Why? Because no one had involved them in the development process, explained how the system worked or addressed their legitimate concerns about how it would affect their roles.
The Strategy Disconnect
Many AI projects begin with a critical flaw: they lack clear connections to genuine business problems and strategic objectives. They’re solutions in search of problems rather than the other way around.
I’ve watched organizations launch AI initiatives because competitors were doing so or because the C-suite read about the technology in a business magazine. These projects inevitably fail because they’re not anchored to specific, measurable business outcomes.
Talent And Governance Shortfalls
The AI talent gap remains enormous. Data scientists are in short supply, and those with the rare combination of technical expertise and business acumen are as scarce as diamonds in a sandbox.
Beyond talent, many organizations lack proper governance structures for AI initiatives. Who owns the project? Who makes decisions when trade-offs arise between speed, cost, and quality? Without clear accountability and decision frameworks, AI projects drift into ambiguity and eventually failure.
Skipping The Foundation Work
Think of enterprise AI as a house. You can’t build the roof before you’ve laid the foundation and framed the walls. Yet organizations routinely attempt to implement advanced AI capabilities before establishing basic data infrastructure and analytics competencies.
The Path Forward: Making AI Projects Succeed
The high failure rate of AI initiatives isn’t inevitable. Organizations that approach AI with appropriate planning, resources, and expectations dramatically improve their odds of success.
Start with problems, not technology. Identify specific business challenges where AI might provide solutions and articulate clear, measurable objectives. This anchors the project in business reality rather than technological possibility.
Conclusion
AI isn’t magic – it’s a powerful set of technologies that, when properly implemented, can deliver extraordinary business value. However, that implementation requires rigor, realism, and resources that many organizations underestimate.
FAQs
Q: What are the most common mistakes organizations make when implementing AI?
A: Underestimating the quantity and quality of data required, treating AI implementation as purely technical, and failing to involve end-users in the development process.
Q: What are the key factors that contribute to the high failure rate of AI initiatives?
A: Poor data quality and governance, lack of clear connections to genuine business problems and strategic objectives, and inadequate talent and governance structures.
Q: What are the essential steps to ensure AI project success?
A: Start with problems, not technology; invest in data quality and infrastructure; treat AI implementation as organizational change; take an incremental approach; and establish clear governance and decision-making frameworks.
Innovation and Technology
Social Enterprise and Technology

Using technology to create a better world, social enterprises are leveraging digital tools to tackle some of the world’s most pressing issues. From healthcare to education, technology is revolutionizing the way social enterprises operate and deliver impact. With the power of technology, social enterprises can now reach more people, improve efficiency, and drive meaningful change.
What is a Social Enterprise?
A social enterprise is an organization that uses business principles to achieve a social or environmental mission. Social enterprises can take many forms, from non-profits to for-profits, and can operate in a variety of sectors. The key characteristic of a social enterprise is its commitment to creating positive impact alongside financial returns.
Types of Social Enterprises
There are many types of social enterprises, each with its own unique approach to creating social impact. Some common types of social enterprises include non-profit organizations, social businesses, and hybrid models. Non-profit organizations rely on donations and grants to fund their activities, while social businesses generate revenue through sales and services. Hybrid models combine elements of both, using business principles to drive social impact.
The Role of Technology in Social Enterprise
Technology is playing an increasingly important role in the social enterprise sector, enabling organizations to reach more people, improve efficiency, and drive meaningful change. From digital platforms to mobile apps, technology is helping social enterprises to scale their impact and achieve their missions. With the power of technology, social enterprises can now collect and analyze data, communicate with stakeholders, and deliver services more effectively.
Digital Platforms for Social Impact
Digital platforms are being used by social enterprises to connect people, resources, and organizations. These platforms can take many forms, from online marketplaces to social networks, and can be used to address a wide range of social issues. For example, online platforms can be used to connect volunteers with non-profits, or to provide access to education and job training programs.
Mobile Technology for Social Change
Mobile technology is also being used by social enterprises to drive social change. Mobile apps can be used to provide access to healthcare services, financial inclusion, and education. Mobile technology can also be used to collect data, track progress, and evaluate impact. With the widespread adoption of mobile devices, social enterprises can now reach people in even the most remote and underserved communities.
Examples of Social Enterprises Using Technology
There are many examples of social enterprises using technology to drive social change. One example is the non-profit organization, Medic Mobile, which uses mobile technology to improve healthcare outcomes in developing countries. Another example is the social business, Warby Parker, which uses e-commerce and digital marketing to sell affordable eyeglasses and support social causes.
Medic Mobile: Using Mobile Technology to Improve Healthcare
Medic Mobile is a non-profit organization that uses mobile technology to improve healthcare outcomes in developing countries. The organization has developed a range of mobile apps and digital tools that enable healthcare workers to collect and analyze data, communicate with patients, and deliver services more effectively. With the support of Medic Mobile, healthcare workers can now provide better care to more people, and improve health outcomes in some of the world’s most underserved communities.
Warby Parker: Using E-commerce to Drive Social Impact
Warby Parker is a social business that uses e-commerce and digital marketing to sell affordable eyeglasses and support social causes. The company has developed a range of digital platforms and tools that enable customers to purchase eyeglasses online, and to support social causes through the “buy one, give one” model. With the support of Warby Parker, people in need can now access affordable eyeglasses, and the company can drive social impact alongside financial returns.
Challenges and Opportunities for Social Enterprises
While technology is creating many opportunities for social enterprises, there are also challenges to be addressed. One of the main challenges is the digital divide, which can limit access to technology and digital platforms. Another challenge is the need for digital literacy, which can enable social enterprises to effectively use technology to drive social change.
Addressing the Digital Divide
The digital divide is a significant challenge for social enterprises, as it can limit access to technology and digital platforms. To address this challenge, social enterprises can work to provide access to digital devices, internet connectivity, and digital literacy training. This can enable more people to access digital platforms and services, and to participate in the digital economy.
Developing Digital Literacy
Digital literacy is also essential for social enterprises, as it enables them to effectively use technology to drive social change. To develop digital literacy, social enterprises can provide training and capacity-building programs for staff and stakeholders. This can enable social enterprises to effectively use digital platforms and tools, and to drive meaningful social impact.
Conclusion
In conclusion, technology is playing an increasingly important role in the social enterprise sector, enabling organizations to reach more people, improve efficiency, and drive meaningful change. From digital platforms to mobile apps, technology is helping social enterprises to scale their impact and achieve their missions. While there are challenges to be addressed, the opportunities for social enterprises to use technology to drive social change are vast and exciting.
Frequently Asked Questions (FAQs)
What is a social enterprise?
A social enterprise is an organization that uses business principles to achieve a social or environmental mission.
How can technology be used to drive social change?
Technology can be used to drive social change by providing access to digital platforms, mobile apps, and other digital tools that enable social enterprises to reach more people, improve efficiency, and drive meaningful impact.
What are some examples of social enterprises using technology?
Examples of social enterprises using technology include Medic Mobile, which uses mobile technology to improve healthcare outcomes, and Warby Parker, which uses e-commerce and digital marketing to sell affordable eyeglasses and support social causes.
What are some challenges facing social enterprises in using technology?
Challenges facing social enterprises in using technology include the digital divide, which can limit access to technology and digital platforms, and the need for digital literacy, which can enable social enterprises to effectively use technology to drive social change.
How can social enterprises address the digital divide?
Social enterprises can address the digital divide by providing access to digital devices, internet connectivity, and digital literacy training, and by working to develop digital platforms and tools that are accessible and inclusive.
What is the future of technology in social enterprise?
The future of technology in social enterprise is exciting and rapidly evolving, with new digital platforms, tools, and innovations emerging all the time. As technology continues to advance and improve, social enterprises will have even more opportunities to drive social change and achieve their missions.
Innovation and Technology
Thrive Amidst Volatility

Introduction to Volatility
Five years after the start of the COVID-19 pandemic, the world in many ways feels even more tumultuous and unpredictable. And unlike five years ago, when the source of the disruption was a single, unknown pathogen, today’s volatility comes from myriad forces: global outages, AI, cyberthreats, new tariffs, trade wars, and, of course, economic concerns. For business leaders, the impulse may be to hit pause on planned initiatives and spending and wait to see how things play out. But here’s the thing: There’s no end in sight to this volatility.
Opportunity in Disruption
Yet there is opportunity in disruption — even in times as tumultuous as what we’re experiencing. Companies that thrive through volatility and come out ahead will master three critical domains: spending and resource optimization, change leadership, and risk management.
Key Areas to Master
To thrive amidst volatility, companies must focus on three key areas:
Ruthlessly Optimize Spend And Focus Resources
This is not the same as ruthless cost cutting, since reactive, brute-force budget cuts typically hurt more than they help. Instead, it’s about finding opportunities to streamline — by clearing out duplicative software, for instance, and renegotiating contracts when doing so would enable greater efficiency and savings. It’s also about reprioritizing (not pausing) modernization plans to help you be more nimble, secure, and prepared to make best use of AI and other game-changing technologies.
Take the same laser focus to understanding and serving your customers. Reevaluate your customer segments to determine who to prioritize, then double down on customer insights (leaning on zero-party data) to deliver stellar customer service.
Master Change Leadership
Adapting to perpetual change and volatility is exhausting. To be effective, leaders must act as stabilizing forces, providing confidence and clarity despite navigating terrain that most have never experienced. Recognize that leading in this climate takes balance — between keeping a steady hand on the future while meeting change at a moment’s notice and between people and processes. Employees may also be struggling with the effects of change, so develop bidirectional listening strategies and communicate transparently to maintain cohesion and engagement. Cultivate a culture of continuous learning and upskilling to foster adaptability and agility.
Embrace Risk Management
While you can’t control volatility from happening, you can manage it by taking a continuous, holistic approach to risk. Risks fall into three categories: 1) enterprise risks, or those connected to your strategy, business model, and other factors fully within your control; 2) ecosystem risks, or those arising from third-party relationships; and 3) external risks, which today encompass everything from tariffs and technology bans to pandemics and wars. During times of volatility, all business leaders must fully understand their specific risks, create scenario plans for addressing them, and have the best courses of action ready to ignite for whatever comes their way.
Conclusion
Mastering these three areas will equip you to not only thrive through volatility but also, potentially, to tap innovation and profit. By optimizing spend and resources, mastering change leadership, and embracing risk management, companies can turn challenges into opportunities for growth.
FAQs
Q: What are the three critical domains that companies must master to thrive through volatility?
A: The three critical domains are spending and resource optimization, change leadership, and risk management.
Q: How can companies optimize their spend and resources?
A: Companies can optimize their spend and resources by streamlining processes, renegotiating contracts, and reprioritizing modernization plans.
Q: What is the importance of change leadership in times of volatility?
A: Change leadership is crucial in times of volatility as it provides confidence and clarity to employees and helps companies adapt to perpetual change.
Q: How can companies manage risk in times of volatility?
A: Companies can manage risk by taking a continuous, holistic approach to risk, understanding their specific risks, creating scenario plans, and having the best courses of action ready to ignite for whatever comes their way.
Innovation and Technology
Accelerating AI with Co-Processors

Most chips today are built from a combination of customized logic blocks that deliver some special sauce, and off-the-shelf blocks for commonplace technologies such as I/O, memory controllers, etc. But there is one needed function that has been missing; an AI co-processor.
In AI, the special sauce has been the circuits that do the heavy-lifting of parallel matrix operations. However, other types of operations used in AI do not lend themselves well to such matrix and tensor operators and silicon. These scalar and vector operators for computing activations and averages are typically calculated on a CPU or a digital signal processor (DSP) to speed vector operations.
Designers of custom AI chips often use a network processor unit coupled with a DSP block from companies like Cadence or Synopsys to accelerate scalar and vector calculations. However, these DSPs also include many features that are irrelevant to AI. Consequently, designers are spending money and power on unneeded features.
Enter AI Co-Processors
Large companies that design custom chips address this by building in their own AI Co-Processor. Nvidia Orin Jetson uses a vector engine called PVA, Intel Gaudi uses its own vector processor within its TPCs, Qualcomm Snapdragon has its vector engine within the Hexagon accelerator, as does the Google TPU.
AI co-processors work alongside AI matrix engines in many accelerators today. But what if you are an automotive, TV, or edge infrastructure company designing your own AI ASIC for a specific application? Until now, you had to either design your own co-processor, or license a DSP block and only use part of it for your AI needs.
The New AI Co-Processor Building Block
Cadence Design has now introduced an AI co-processor, called the Tensilica NeuroEdge, which can deliver roughly the same performance of a DSP but consumes 30% less die area (cost) on an SoC. Since NeuroEdge was derived from the Cadence Vision DSP platform, it is fully supported by an existing robust software stack and development environment.
An AI SoC can have CPUs, AI block like GPUs, Vision processors, NPUs, and now AI co-processors to accelerate the entire AI workload. The new co-processor can be used with any NPU, is scalable, and helps circuit design teams get to market faster with a fully tested and configurable block. Designers will combine CPUs from Arm or RISC-V, NPUs from EDA firms like Synopsys and Cadence, and now the “AICP” from Cadence, all off-the-shelf designs and chiplets.
The AICP was born from the Vision DSP, and is configurable to meet a wide-range of compute needs. The NeuroEdge supports up to 512 8×8 MACs with FP16, 32, and BD16 support. It connects with the rest of the SoC using AXI, or using Cadence’s HBDO (High-Bandwidth Interface). Cadence has high hopes for NeuroEdge in the Automotive market, and is ready for ISO 26262 Fusa certification.
NeuroEdge fully supports the NeuroWeave AI compiler toolchain for fast development with a TVM-based front-end.
My Takeaway
With the rapid proliferation of AI processing in physical AI applications such as autonomous vehicles, robotics, drones, industrial automation and healthcare, NPUs are assuming a more critical role. Today, NPUs handle the bulk of the computationally intensive AI/ML workloads, but a large number of non-MAC layers include pre- and post-processing tasks that are better offloaded. Current CPU, GPU and DSP solutions required tradeoffs, and the industry needs a low-power, high-performance solution that is optimized for co-processing and allows future proofing for rapidly evolving AI processing needs. Cadence is the first to take that step.
Conclusion
In conclusion, the introduction of the Tensilica NeuroEdge AI co-processor by Cadence Design is a significant development in the field of AI processing. It addresses the need for a low-power, high-performance solution that is optimized for co-processing and allows future proofing for rapidly evolving AI processing needs. With its configurable design and support for a wide range of compute needs, NeuroEdge is poised to play a critical role in the development of AI applications in various industries.
FAQs
Q: What is an AI co-processor?
A: An AI co-processor is a specialized processor designed to work alongside AI matrix engines to accelerate scalar and vector calculations in AI applications.
Q: What is the Tensilica NeuroEdge AI co-processor?
A: The Tensilica NeuroEdge AI co-processor is a new AI co-processor introduced by Cadence Design, which delivers roughly the same performance as a DSP but consumes 30% less die area (cost) on an SoC.
Q: What are the benefits of using the NeuroEdge AI co-processor?
A: The benefits of using the NeuroEdge AI co-processor include low power consumption, high performance, and configurability to meet a wide range of compute needs.
Q: What industries can benefit from the NeuroEdge AI co-processor?
A: Various industries such as automotive, TV, edge infrastructure, autonomous vehicles, robotics, drones, industrial automation, and healthcare can benefit from the NeuroEdge AI co-processor.
Q: Is the NeuroEdge AI co-processor supported by a software stack and development environment?
A: Yes, the NeuroEdge AI co-processor is fully supported by an existing robust software stack and development environment, including the NeuroWeave AI compiler toolchain.
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