I have summarised for you the Key Takeaways of the MBBs: The latest summit in Davos brought attention to crucial topics for businesses today, with a focus on sustainability, digital transformation, and the future of work. Let's take a look at the key takeaways shared by McKinsey & Company, Boston Consulting Group, and Bain & Company. McKinsey & Company's key takeaways from the latest summit in Davos included the need to focus on sustainability, digital transformation, and the future of work, as well as addressing global inequality and the impact of the Covid-19 pandemic. Additionally, the importance of investing in new technologies, such as artificial intelligence, machine learning, and big data, was highlighted. Finally, the summit discussed the need for long-term strategic planning and the need for businesses to be agile and adaptive in order to remain competitive in the current global landscape.

Leaders from McKinsey Technology Council, academia and industry, highlight AI, metaverse, crypto, and quantum computing as some of the key business priorities in 2022, according to a recent report. Predicting the trends that will headline business agendas this year as well as the ones that fly under business' radars, but need more attention, the leaders claim that investments in digital are expected to continue as the world manages continued impacts of the Covid disruption. Artificial intelligence (AI) and Machine Learning (ML) Businesses are expected to continue to invest in AI and ML through a process known machine learning operations (MLOps), whereby they can create an operating system that streamlines the production and maintenance of machine learning models. According to Rodney Zemmel, senior partner at McKinsey & Company, "MLOps provides the opportunity to create an operating system of people and technology that can make the process of creating each new application dramatically easier, helping to enable the whole business." Michael Chui, partner at McKinsey Global Institute believes that MLOps will allow businesses to deploy AI more cost effectively and at scale, as a point of competitive advantage.

From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace. Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout.

Pre Covid-19, it was estimated that UK employers were spending more than £6 billion per year to address the issues associated with internal and external skills gaps. Recruitment costs topped this substantial bill; indicating that some employers were opting for short-term approaches to'buying' talent rather than'building' it within their own organisations. To address the escalating issue of employability skills in the UK, a number of pre-pandemic reports highlighted that more and more organisations were becoming acutely aware that there would have to be a shift in mindset towards adopting sustainable, long-term workplace learning provisions to produce a more agile, loyal, motivated, and productive workforce for the future. In this respect, there was a trend emerging; one that was based upon the premise of moving away from economically impactful marginal gains towards a more sustainable culture of investing in the capability development of existing employees. The answers to why some organisations still struggle to address the skills gaps issues within their business are complex and, of course, have many facets.

Hiroshige Seko, the minister of Economy Trade and Industry (METI) of Japan introduced a new concept for their roadmap to realize'Society 5.0' the future urbanism as the next big thing in industries. He mentioned that we require another industrial revolution using advanced technological innovations including, AI, IoT, and Big Data; this would be'Connected Industries.' This was the inception of'Connected Industries' as introduced by Hiroshige with the impact on future lives. Artificial Intelligence or AI will be on a next-level role in this development, with a more significant impact on each ecosystem entity. Before moving ahead to understand the role of AI in the'Connected Industries', let's first understand AI and its applications.

For global banking, artificial intelligence (AI) could potentially deliver up to US$1 trillion of additional value each year, boosting revenues through increased personalization of services, lowering costs through efficiencies, and uncovering new and previously unrealized opportunities through the use of data, says McKinsey & Company. In a post titled AI-bank of the future: Can banks meet the AI challenge?, McKinsey says that banks have continuously adapted to the latest technology innovations throughout the years, and as the industry heads towards the AI-powered digital age, incumbents must adopt AI at scale and become so-called "AI-first banks." Several trends are accelerating banks' transition towards becoming AI-first, it says, with the first one being customers' rapid adoption of digital banking. COVID-19 has further boosted the adoption of digital banking with use of online and mobile banking channels surging an estimated 20 to 50% in the first few months of the pandemic. The emergence of digital ecosystems and so-called "super apps" is also changing the way consumers discover, evaluate and purchase banking products and services, it says.

A study by McKinsey & Company found that AI-driven quality testing can increase productivity by up to 50% and defect detection rates by up to 90% compared to human inspection. Though machines with automated optical inspection (AOI), powered by machine vision, have replaced most of the manual processes in the modern assembly line, quality control still remains a huge and costly challenge. The European Commission claims that in some industries 50% of production can be abandoned due to defects, and the defect rate can reach up to 90% in complex production environments. The critical limitation with machine learning AOI systems is in disclosing surface defects where even a slight variant (often invisible to the human eye) can hamper the entire production run and render hundreds to thousands of products useless before the defect is discovered. The economic impact can be devastating.

AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing. What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value.

For the many companies that rely on pricing as a competitive advantage, they need to start evaluating AI and machine learning on their IT platform roadmaps now. Staying at competitive parity and turning AI- and machine learning-based expertise into a pricing and revenue management strength needs to be a priority. Data is a proven panacea for fear, and given the new market dynamics many companies are facing, it's the most reliable way to make decisions. Harnessing Pricing Power to Create Lasting Value, Bain & Company, February 24, 2020. Harnessing Pricing Power to Create Lasting Value, Bain & Company, February 24, 2020.

From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace. Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout.