The IT world is grappling with a significant skills gap when it comes to both traditional and generative AI. A survey conducted earlier this year by Pluralsight of 1200 executives and IT professionals indicated that 81% of IT professionals think that they can use AI, but only 12% actually have the skills to do so. And 70% of workers likely need to upgrade their AI skills. In a December 2023 article Reuters reported there will be a 50% hiring gap for AI-related positions in 2024 and the existing personnel up-skilling demand could approach 70% of workers needing such a skills upgrade. A 2023 survey of nearly 13,000 employees by Boston Consulting Group found that while 86% needed AI training, only 14% were receiving it. Bottom line, we have a large AI IT skills gap that is likely going to get worse as more and more companies deploy AI enabled solutions this year.

Artificial Intelligence (AI) is revolutionizing industries and transforming the way companies operate. As organizations try to maintain competitive advantage and drive innovation, incorporating AI into their IT strategy has become a necessity. AI has the potential to streamline processes, enhance decision-making, and unlock new opportunities, making it a critical component of any forward-thinking IT strategy.

The Internet of Things (IoT) is a network of physical devices embedded with sensors, software, and other technologies, designed to connect and exchange data with other devices and systems over the internet. This interconnectedness allows for more direct integration of the physical world into computer-based systems, resulting in improved efficiency, accuracy, and economic benefits.

Building an effective IT strategy is essential for any organization looking to leverage technology to drive business growth and efficiency. Emerging technology like artificial intelligence (AI), generative AI and the Internet of things (IoT) offer many strategic business benefits if implemented and used properly in the context of the specific needs of a company. IoT in particular, significantly impacts a company's IT strategy, influencing various aspects of business operations, technology management, and strategic planning.

Generative AI can transform enterprise workflows by automating routine tasks, streamlining business processes, and enabling employees to focus on higher-level, strategic work. It excels in automating repetitive activities like data entry and customer service, optimizing workflows to reduce bottlenecks, and enhancing operational efficiency through predictive maintenance and demand prediction. By freeing employees from mundane tasks, generative AI empowers them to engage in creative, strategic, and problem-solving activities, ultimately fostering innovation and improving job satisfaction. Examples of its application include content creation, data analysis, and virtual assistance, all of which contribute to a more efficient and competitive business environment.

Companies continuously seek ways to enhance efficiency, reduce costs, and improve accuracy. Business automation is a key strategy for achieving these goals. By leveraging technology to perform repetitive tasks, streamline processes, and integrate complex workflows, businesses can focus on strategic initiatives and innovation. The three key facets of business automation are task automation, straight-through processing (STP), and orchestration.

High-quality data is the foundation for successful generative AI applications. As companies increasingly leverage AI to drive innovation and improve business outcomes, ensuring the accuracy, completeness, and consistency of data is more critical than ever. Poor quality data can lead to inaccurate AI model outputs, biased results, and ultimately, flawed decision-making.

Generative AI has the potential to have a major impact on business operations. From content creation and customer service to data analysis and decision-making, generative AI can streamline processes, reduce costs, and enhance efficiency. However, as with any powerful technology, it is crucial for companies to ensure the responsible use of generative AI to mitigate potential risks and maintain ethical standards.

The integration of technology into the education and training process has become a pivotal force in transforming traditional learning methodologies. One of the most impactful advancements in this domain is the application of Generative Artificial Intelligence (GenAI) to personalized education and training. This innovative approach leverages the power of AI to adapt learning materials, catering to the unique needs and preferences of individual learners.

The sales profession is undergoing a transformation. AI and generative AI, with capabilities for creating human-quality content, simulations and predictive analysis, can provide sales managers effective tools for sales coaching. For sales managers burdened with the task of nurturing and developing their teams, genAI offers many possibilities. By leveraging genAI, managers can personalize training, provide real-time feedback, and equip their reps with the tools they need to close more deals and achieve peak performance.

Traditional risk management often involves reacting to problems after they occur. AI offers a game-changing approach by enabling proactive risk mitigation and problem prediction. AI-enabled risk management is more effective than traditional risk management processes and methods due to its ability to process and analyze vast amounts of data quickly and accurately. Traditional methods often rely on manual data collection and analysis, which can be time-consuming and prone to human error. AI systems, on the other hand, use machine learning algorithms to detect patterns and anomalies in real-time, providing faster and more reliable insights. These systems can continuously learn and adapt from new data, improving their predictive capabilities and enabling organizations to identify and mitigate risks more proactively. AI can integrate data from diverse sources, such as social media, financial markets, and internal company data, to provide a comprehensive view of potential risks.

Generative AI is adding value to individuals both in their personal life and in their job, as well as to businesses in business processes and other use cases. There’s a bit of confusion though that has persisted in the generative AI “lore” that gets discussed regularly. It is easy to think of generative AI in business in the same light as the currently available public tools like OpenAI’s ChatGPT, Google Gemini, Perplexity, and Anthropic’s Claude. While those tools are definitely being used in a business setting, they are not the only way generative AI is showing up for enterprise use.

Businesses are swimming (or maybe that’s drowning) in a sea of data. Sales figures, customer interactions, marketing campaign results – the list goes on. But data alone isn't enough. The true power lies in transforming this raw material into actionable information and, ultimately, into valuable knowledge that can fuel informed decision-making, innovation, artificial intelligence (AI) and competitive advantage.

Generative AI has the potential to revolutionize the development of green technology and contribute significantly to our understanding and mitigation of climate change. By leveraging the power of machine learning algorithms, generative AI can help create, optimize, and discover new sustainable solutions, making it a valuable tool in the fight against climate change. We can accelerate the development of innovative green technologies and gain a deeper understanding of the complex processes driving climate change. This, in turn, will enable us to make more informed decisions and implement effective strategies to mitigate the impacts of climate change and build a more sustainable future.

Generative AI is revolutionizing the way we interact with the internet, offering a transformative leap from the traditional search engine experience. Traditional search engines, while effective in navigating the immense data available online, often present users with a list of links, requiring them to sift through multiple pages to find the information they need. This process can be time-consuming and sometimes ineffective, especially when dealing with complex queries or seeking comprehensive understanding on a topic (and of course not mentioning the amount of “sponsored” links that pollute search results). In contrast, generative AI, exemplified by tools like Perplexity, is redefining this experience by synthesizing data and delivering proactive, tailored feedback directly to users.

The past year, with its explosion of generative artificial intelligence (GenAI) based tools, has created a lot of buzz around the business potential of artificial intelligence (AI). The use of AI enabled technology is not new of course, but until the broad exposure created by GenAI, it remained mostly “behind the scenes”. Business functions related to analyzing data and automating tasks were in common use in business prior to the public release of GenAI in 2022.

Introducing an AI-ready organization involves more than just implementing new technologies; it's about cultivating an environment where AI can thrive and deliver substantial value. The keys to successful enterprise AI adoption tie directly to broad leadership buy-in, and the importance of having senior executives that champion AI initiatives. In addition, implementing strategies for fostering a culture that embraces change and innovation is crucial for integrating AI into daily operations.

The integration of Artificial Intelligence (AI) and generative AI into the Architecture, Engineering, and Construction (AEC) industry represents a transformative shift, leveraging advanced technologies to improve the efficiency, sustainability, and creativity of building processes. AI and generative AI are impacting every stage of a project from conceptual design to facilities management.

Over the past two weeks, rumors swirled about Salesforce being in advanced talks to acquire Informatica for over $11 billion. I’ve seen several articles on the potential acquisition and hadn’t planned to add to them, but as I read more and thought about the acquisition, I decided to jump in. This acquisition is seen as a strategic expansion into data management, particularly to enhance Salesforce's capabilities in generative artificial intelligence (GenAI) and predictive analytics. Informatica's Intelligent Data Management Cloud (IDMC) would be a key asset in this potential acquisition. IDMC's robust data ecosystem and metadata-rich platform, including Claire, its active metadata-driven machine learning engine, could significantly bolster Salesforce's existing AI functionalities and data management offerings. 

Large Action Models (LAMs) are a new advancement in AI that builds upon the capabilities of Large Language Models (LLMs). LAMs leverage a combination of existing AI technologies to bridge the gap between understanding language and taking action in the digital world.