The idea of robots picking items from warehouse shelves may still seem futuristic today. But the future may be closer than many people think. Of the almost 1 million robots we expect to be sold for enterprise use in 2020, we predict that just over half of them will be professional service robots, generating more than US$16 billion in revenue--30 percent more than in 2019. What's more, with regard to enterprise spending, the market for professional service robots is growing much faster than that for industrial robots (figure 1). If recent trends are any sign, professional service robots may pass industrial robots in terms of units in 2020 and revenue--in 2021. That's not to say that the industrial robotics market is hurting.

The work of the future is no longer about enhancing physical capabilities. Instead, it's based on enhancing intellectual work, complex cognitive decision-making and human insights. According to a 2019 Accenture study, 75% of global executives believe AI is critical to their company's future in the next five years. This is a phenomenal statement, yet effectively implementing AI is not an easy task. There are challenges to scaling an AI strategy, and I'll explore a few approaches to this in a future article.

Using AI successfully in the enterprise will increasingly require close collaboration between data scientists and tech-savvy business experts. Few advanced technologies conjure as much fear, uncertainty, and doubt as AI, which is alternately described by some as a destroyer of jobs and humanity's salvation. "Whereas most tech innovations have focused on solving a very defined problem, AI is continually learning and evolving even after we put it out into the field," said Beena Ammanath, a managing director and leader of the AI practice with Deloitte Consulting LLP. "Ethics comes to the forefront, as do transparency, accountability, and planning ahead for challenges that might arise if it is misused or goes rogue." According to Deloitte's latest "State of AI in the Enterprise" survey, respondents are increasingly spending on AI technologies and seeing positive returns; about half expect to increase their AI investments by 10% or more in the next fiscal year.

Accenture Completes Acquisition of Mudano, Enhancing Its Analytics and Data Transformation Services to Financial Services Firms in the UK LONDON; Feb. 28, 2020 – Accenture (NYSE: ACN) has completed its acquisition of Mudano, a strategic data consultancy to U.K. financial services firms. The acquisition enhances Accenture's analytics, data and artificial intelligence (AI) transformation capabilities. Terms of the transaction were not disclosed. Mudano's team of industry-focused data professionals will join Accenture Applied Intelligence. Mudano was founded in 2014 and is headquartered in London, with a presence in Edinburgh, Scotland. About Accenture Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations.

Over the past few years, artificial intelligence has matured into a collection of powerful technologies that are delivering competitive advantage to businesses across industries. Global AI adoption and investment are soaring. By one account, 37 percent of organizations have deployed AI solutions--up 270 percent from four years ago.1 Analysts forecast global AI spending will more than double over the next three years, topping US$79 billion by 2022.2 Get the Deloitte Insights app. Deloitte's State of AI in the Enterprise, 2nd Edition offers a global perspective of AI early adopters, based on surveying 1,900 IT and business executives from seven countries and a variety of industries.3 These adopters are increasing their spending on AI technologies and realizing positive returns. Almost two-thirds (65 percent) report that AI technologies are enabling their organizations to move ahead of the competition.

When setting out to improve your tax processes, the hardest part can be knowing where to begin. These steps can help point you in the right direction. Think big: Start by sizing up your opportunities. Talk to people in your organization to identify recurring instances across the business when significant time is spent inefficiently today--perhaps for scenario analysis or strategic planning. Be sure to include your finance colleagues in the brainstorming, as they often face similar challenges.

Hi! We are back again with a new episode. In our third episode, we were joined by Mr. Saurabh Chopra, currently serving as a project manager for Royal Enfield; having garnered extensive strategy consulting experience at Deloitte and Accenture in the past. In this episode, Shaurya and Saurabh dig deep into exploring the minutiae on how companies are utilizing experiential marketing & technology. Specifically, the importance of voice search, AI Chatbots and Customer Acquisition for Travel sector companies and we also discuss the merits of Rentals Vs. Key Takeaway: Successful startups and established companies operate on similar key ideas and how incorporating them early in your business model could successfully future-proof your business.

There's widespread consensus that we're in the throes of the fourth industrial revolution Artificial intelligence and its sister technologies is transforming virtually every business. Yet with AI's enormous potential comes great responsibility. The majority (77%) of CEOs say that AI threatens to increase vulnerability and disruption to the ways they do business. Unfortunately, the call for responsible AI has taken a backseat for many companies. Only 25% of companies say that they definitely prioritize considering the ethical implications of an AI solution before investing in it, according to research by PwC.

It is difficult to open an insurance industry newsletter these days without seeing some reference to machine learning or its cousin artificial intelligence and how they will revolutionize the industry. Yet according to Willis Towers Watson's recently released 2019/2020 P&C Insurance Advanced Analytics Survey results, fewer companies have adopted machine learning and artificial intelligence than had planned to do so just two years ago (see the graphic below). In the context of insurance, we're not talking about self-driving cars (though these may have important implications for insurance) or chess-playing computers. We're talking about predicting the outcome of comparatively simple future events: Who will buy what product, which clients are more likely to have what kind of claim, which claim will become complex according to some definition. Analytics have applications across the insurance value chain, from marketing, client acquisition and retention to underwriting, pricing and claims management, as insurers look to squeeze more signal out of their data.

The future of work: What does this term really mean? Much discussion has focused on artificial intelligence and whether or not robots will take our jobs, but cognitive technologies are only one aspect of the massive shift that is under way. To understand what's going on and, more importantly, what we can do about it, it's important to consider multiple converging trends and how they are already fundamentally changing all aspects of work--with implications for individuals, businesses, and society. We define the future of work as a result of many forces of change affecting three deeply connected dimensions of an organization: work (the what), the workforce (the who), and the workplace (the where) (figure 1). The new realities created by these forces of change present us with complex questions to consider--including ethics around human-machine collaboration, how we plan for 50–60-year careers,1 and how we unleash organizations through a continuum of talent sources.