The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements. Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads. This paper aims to present a novel ontology linking business transformation initiatives to occupations and an approach to automatically populating it by leveraging embeddings extracted from job ads and Wikipedia pages on business transformation and emerging technologies topics. To our knowledge, no previous research explicitly links business transformation initiatives, like the adoption of new technologies or the entry into new markets, to the roles needed. Our approach successfully matches occupations to transformation initiatives under ten different scenarios, five linked to technology adoption and five related to business. This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives.

One of the points I've been stressing for a long time now is that: It's not about data It's about business, the business outcomes, about the value that is generated for business. Business is the driver, data and what it produces is the enabler. As any other corporate asset, data's purpose is to generate business value. Organizations have apprehended the importance of data in their businesses and are looking deeper into data to gain a competitive advantage, implementing machine learning and artificial intelligence to achieve new business objectives and to move ahead of competitors in the industry. A data asset is every piece of data that organizations use to generate revenues, they are currently among its the most valuable assets, and organizations must invest seriously on managing these assets.

This is part 1 in a multi-part series on the value-realizing, collaborative power of decisions. My mom used to say, "If it was a snake, you'd be dead". And the reason that I say that, is that organizations are seeking a collaborative value driver that can 1) align the organization around the economic power of data and analytics while 2) providing that "clear line of sight from data to value". And that value driver is slithering right in front of us – Decisions. Yes, something as simple and prevalent as decisions can be that collaborative linkage point.

I recently conducted a 3-day, remote "Data Monetization: Thinking Like a Data Scientist" workshop for a transportation agency in the Middle East. Doing this training remotely is a personal challenge as I miss the face-to-face interaction in ideating, validating, and prioritizing the business areas that can benefit from data and analytics. However, conducting the workshop remotely did provide some valuable learnings for me. One learning was my "Thinking Like a Data Scientist" visual was outdated (Figure 1). Figure 1 portrayed the "Thinking Like a Data Scientist" (TLADS) process as a linear process, where you would complete one step and then cleanly move onto the next step. But in reality, the process is highly iterative where it is common for learnings from one step to impact an earlier step such as refining the KPIs against which the targeted business initiative's progress and success will be measured.

In a recent blog I stated that "Crossing the AI Chasm" is primarily an organizational and cultural challenge, not a technology challenge. That "Crossing the AI Chasm" not only requires organizational buy-in, but more importantly, necessitates creating a culture of adoption and continuous learning fueled at the front-lines of customer and/or operational engagement (see Figure 1). A recent Harvard Business Review (HBR) article "Building the AI-Powered Organization" agrees that despite the promise of AI, many organizations' efforts with it are falling short because of a failure by senior management to rewire the organization from the bottom up. The above points – interdisciplinary collaboration, data-driven at the front-line, and experimental and adaptive – are the hallmarks of an organization where everyone has been trained to Think Like a Data Scientist." So, how can your organization embrace the liberating and innovative process of getting everyone to "Think Like a Data Scientist"?

In a recent blog I stated that "Crossing the AI Chasm" is primarily an organizational and cultural challenge, not a technology challenge. That "Crossing the AI Chasm" not only requires organizational buy-in, but more importantly, necessitates creating a culture of adoption and continuous learning fueled at the front-lines of customer and/or operational engagement (see Figure 1). A recent Harvard Business Review (HBR) article "Building the AI-Powered Organization" agrees that despite the promise of AI, many organizations' efforts with it are falling short because of a failure by senior management to rewire the organization from the bottom up. The above points – interdisciplinary collaboration, data-driven at the front-line, and experimental and adaptive – are the hallmarks of an organization where everyone has been trained to "Think Like a Data Scientist." So, how can your organization embrace the liberating and innovative process of getting everyone to "Think Like a Data Scientist"?

This article was written by Sergii Gorpynich, co-Founder and CTO at Star, co-written by Perry Simpson, Managing Director of Star, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines. While it's easy to find healthcare AI use-cases online, it isn't clear which applications are viable, and which are hype. Healthcare leadership needs to consider the specific data assets, resources, and needs of their organizations before deciding on an AI initiative. In this article, we go into more detail about how executives need to adapt to the changes that AI might bring in businesses. Without a basic grounding in what AI can do and how it works, it can be impossible for executives to find and assess AI opportunity areas. Healthcare leaders should understand the basics of how technologies behind AI work, key concepts of machine learning and data science in general, the critical role of data, the importance of data infrastructure, and the kinds of problems that AI can and cannot solve.

Everyone is talking about the power of AI and it's slowly invading our lives--emphasis on slowly. While you might have a few AI assistants and connected devices in your house, the business world hasn't fully jumped on board yet with AI. Sure, the forward-thinking companies have and those are the headline we are seeing, but I'm talking about full adoption from the mom and pop shops all the way up to the enterprise level, spanning across all industries. We all understand the power and the potential of AI, but we don't seem to discuss the AI deployment challenges that many businesses are likely facing. We see statistics like 61 percent of companies with an innovation strategy are using AI to identify opportunities in data and think that a majority of companies must be adopting AI.

Everyone is talking about the power of AI and it's slowly invading our lives--emphasis on slowly. While you might have a few AI assistants and connected devices in your house, the business world hasn't fully jumped on board yet with AI. Sure, the forward-thinking companies have and those are the headline we are seeing, but I'm talking about full adoption from the mom and pop shops all the way up to the enterprise level, spanning across all industries. We all understand the power and the potential of AI, but we don't seem to discuss the AI deployment challenges that many businesses are likely facing. We see statistics like 61% of companies with an innovation strategy are using AI to identify opportunities in data and think that a majority of companies must be adopting AI.

Since the inception of artificial intelligence into the world of technology, a number of questions have been raised across minds. The Artificial intelligence saga has raised such debates as its future of business as well as the place of the human factor in making accurate decision. Among this AI saga is the challenge of business having enough resources to acquire this human assistants. Treat AI as a business initiative, not a technical specialty: Many organizations view AI's implementation as a task for the IT department. That mistake alone could give rise to most of your future challenges.