Generative Artificial Intelligence is a powerful new technology with the potential to boost innovation and reshape governance in many industries. Nevertheless, organisations face major challenges in scaling GenAI, including technology complexity, governance gaps and resource misalignments. This study explores how Enterprise Architecture Management can meet the complex requirements of GenAI adoption within large enterprises. Based on a systematic literature review and the qualitative analysis of 16 semi-structured interviews with experts, it examines the relationships between EAM, dynamic capabilities and GenAI adoption. The review identified key limitations in existing EA frameworks, particularly their inability to fully address the unique requirements of GenAI. The interviews, analysed using the Gioia methodology, revealed critical enablers and barriers to GenAI adoption across industries. The findings indicate that EAM, when theorised as sensing, seizing and transforming dynamic capabilities, can enhance GenAI adoption by improving strategic alignment, governance frameworks and organisational agility. However, the study also highlights the need to tailor EA frameworks to GenAI-specific challenges, including low data governance maturity and the balance between innovation and compliance. Several conceptual frameworks are proposed to guide EA leaders in aligning GenAI maturity with organisational readiness. The work contributes to academic understanding and industry practice by clarifying the role of EA in bridging innovation and governance in disruptive technology environments.

This post is a commentary on the MLCommons article "Perspective: Unlocking ML requires an ecosystem approach" by Peter Mattson, Aarush Selvan, David Kanter, Vijay Janapa Reddi, Roger Roberts, and Jacomo Corbo. The world of artificial intelligence (AI) and machine learning (ML) is undergoing a sea change from science to engineering at scale. Over the past decade, the volume of AI research has skyrocketed as the cost to train and deploy commercial models has decreased. Between 2015 and 2021, the cost to train an image classification system fell by 64 percent, while training times improved by 94 percent in the same period.1 The emergence of foundation models--large-scale, deep learning models trained on massive, broad, unstructured data sets--has enabled entrepreneurs and business executives to see the possibility of true scale.

When designing your cybersecurity defenses for the new normal, it's important to look beyond the technology. You'll need a true architecture-led approach, one that's driven by your business needs. The global pandemic has jolted many organizations into a new reality where virtual and remote become more important than physical and local. Security architectures deployed over decades have suddenly become irrelevant. Concurrent to this, organizations are looking inward and challenging themselves by asking, "How do I justify new investment in tight economic times? In fact, how do I justify an entirely new security architecture for this remote work reality?"

Ron is principal analyst, managing partner, and founder of the Artificial Intelligence-focused analyst and advisory firm Cognilytica, and is also the host of the AI Today podcast, SXSW Innovation Awards Judge, founder and operator of TechBreakfast demo format events, and an expert in AI, Machine Learning, Enterprise Architecture, venture capital, startup and entrepreneurial ecosystems, and more.Prior to founding Cognilytica, Ron founded and ran ZapThink, an industry analyst firm focused on Service-Oriented Architecture (SOA), Cloud Computing, Web Services, XML, & Enterprise Architecture, which was acquired by Dovel Technologies in August 2011.

Artificial intelligence (AI) is probably the most important new technology today. It has clear use cases, and the value that it's produced so far is indisputable – just think of the digital assistant on your phone, driverless cars, even Gmail uses it. With AI becoming more established, many organizations are starting to get access to and try their hand at running artificial intelligence initiatives. The business world is after all similar to an arms race, and having the latest'weapon' to help you get ahead of competitors is an irresistible prospect. The reason is that while successful, well-known AI projects may be capturing headlines (along with CIOs' dreams of digital transformation), the technology remains challenging.

The blogosphere is full of descriptions about how data science and "AI' is changing the world. In financial services, applications include personalized financial offers, fraud detection, risk assessment (e.g. These applications outlined are largely not new, nor are "AI" algorithms like neural networks. However, increasingly commoditized, flexible and cheaper hardware with readily available algorithms and APIs have lowered barriers to data-compute intensive approaches common to data science, making the use of "AI" algorithms much more straightforward. For practitioners, definitions are well understood. For those less familiar and curious, here are some quick definitions and introductions to baseline everyone. At their heart, data science workflows transform data, from heterogenous sources of information, through models and learning, to derive information from which "useful" decisions can be expedited. Decisions may be automated (e.g. an online search or a retail credit fraud check) or ...

The field of artificial intelligence has witnessed a huge renaissance in the past decade because of significant advancements in specialized computing, and better ways to collect, process and store large volumes of data. The continued diversity in AI projects, products and deployment models, combined with a requirement for rapid time to production, will require proper skills to meet these demands. A recent Gartner survey revealed that leading organizations expect to double the number of AI projects in place within the next year. At this rate, the lack of AI skills will continue to be the No. 1 challenge for enterprises looking to succeed in their AI initiatives through 2025. Herein lies the value of engaging an AI architect and other related roles to do so.

Abstract: The phenomenal growth of social media, mobile applications, sensor based technologies and the Internet of Things is generating a flood of "Big Data" and disrupting our world in many ways. Simultaneously, we are seeing many interesting developments in machine learning and Artificial Intelligence (AI) technologies and methods. In this talk I will examine the paradigm shift caused by recent developments in AI and Big Data and ways to harness their power to create a smarter enterprise computing environment. Using examples from health care, smart cities, education, and businesses in general, I will highlight challenges and research opportunities for developing an enterprise of the future. Bio: Sudha Ram is Anheuser-Busch Endowed Professor of MIS, Entrepreneurship & Innovation in the Eller College of Management at the University of Arizona.

Although a bank's business has basically stayed the same over the last couple of decades, how a bank operates has drastically shifted. The fundamental pillars of a bank, according to Mike Telang, executive vice president and head of enterprise architecture at Wells Fargo, now focus on mostly security, regulation, and innovation. When it comes to technology trends in the financial services industry, Telang said its impact on a bank depends heavily on where the bank is in its maturity lifecycle. "I think it depends on the lifecycle of the maturity of where you are as a bank at that point in time and you've got to find a solution for that ... you have a lot of trends like blockchain, you have a lot of trends like security, so we are applying the technology to the use case that suits us best," Telang said during a panel session at VMworld 2019 in San Francisco last week. Looking at emerging technologies, Telang described the challenges Wells Fargo has faced in adopting machine learning (ML) and artificial intelligence (AI).

If you think back to Greek mythology, an Oracle was a person who provides wise counsel, prophetic predictions, or precognition of the future (as summarized from Wikipedia). So, it is somehow fitting that Oracle, the company, is the first to drive Artificial Intelligence (AI) and Machine Learning (ML) so broadly and deeply into our services. This push allows you to become your own oracle to drive insights, predictions, and customer/employee/partner interactions in ways that will pull you ahead of your competition. AI/ML are game-changing technologies (and have finally come of age) that you need to plan and leverage your Enterprise Architecture for customer and competitive value. If you thought the tsunami of change brought by Cloud Computing was big, get ready. The move to Cloud Computing was merely an enabling step to AI which will come just as fast and be just as consequential to business, if not more so in the long run.