Artificial intelligence and machine learning became popular buzzwords in 2018, thanks in large part to companies adopting the emerging technologies to expand their businesses and acquire new customers. AI-based technologies helped companies add new revenue streams and optimise current ones. According to an analysis by professional services firm PwC, technological disruption has also impacted the mergers and acquisitions (M&A) or investment strategies of large corporations. "With larger corporations looking to adapt to technological advancements and revamp their business models, 2018 has seen a number of acquisitions in the new technology space," said Sanjeev Krishan, partner and leader, private equity and deals, PwC India. "Relevance has become a key element for the survival of any business, and this has made technology expertise a requirement to achieve a competitive advantage in the current market," he added.

There are certainly of theories floating about, but consulting firm PwC predicts that organizations that focus on six key areas will become AI leaders. Pressure will increase to scale up AI in 2019 to enhance decision-making and provide forward-looking intelligence for users in every department and function. Having the right AI governance model enables companies to develop use cases that create quick wins. AI is being democratized, but is still so complex that even trained business specialists can make mistakes. Organizations need to develop the right mix of citizen users, citizen developers, and data scientists, and give them the tools, training, and incentives to help them work collaboratively. Customers, employees, boards, regulators, and corporate partners are wondering if they can trust AI.

As Kirk Borne of Booz Allen Hamilton explains, AI-as-a-service offerings can be transformative for both businesses and employees in serving customers. If you are inclined to think providing excellent service to your customers isn't rocket science, think again. In today's digital topography, big data reigns supreme, and the ability to intelligently deploy technologies that can make sense of the information out there is the key to competitive advantage. Data science as a discipline is moving beyond the classroom and into the boardroom, as more and more executives and directors are realizing the tremendous opportunities it opens to their organizations. To discuss some of the business applications for data science and artificial intelligence, we spoke with Dr. Kirk Borne, Principal Data Scientist and Executive Advisor at management and technology consulting firm Booz Allen Hamilton.

The manufacturing industry is experiencing a growing talent shortage. Data from Deloitte shows that by 2025 the manufacturing skills gap will widen, creating the need for 3.4 million skilled workers, with 2 million of those roles going unfulfilled. Driving this void will be the 2.7 million workers who will retire or leave the industry by 2025, in addition to the roughly 700,000 jobs that will be created by growth within the industry. There still exists a widespread misperception that automation and robots will put manufacturing jobs at risk. This is not the case.

Twenty percent of executives at U.S. companies with artificial intelligence initiatives report that they will roll out AI across their business this year, according to PwC's 2019 AI Predictions report. These companies expect their AI investment, which is often part of intelligent automation initiatives, to go beyond improving productivity and cutting costs. They see AI as a path to growing profits and revenue in 2019. However, the executives in the survey said that there are challenges, such as training employees to use AI systems, and security threats remain a concern. Success in leveraging AI will be built on strategies for the organization and the workforce, for creating responsible AI and AI-ready data, for reinventing the business, and for integrating AI with other technologies.

Most executives know that artificial intelligence (AI) has the power to change almost everything about the way they do business -- and could contribute up to $15.7 trillion to the global economy by 2030. But what many business leaders don't know is how to deploy AI, not just in a pilot here or there, but throughout the organization, where it can create maximum value. The "how" is the sticking point with any emerging technology, and AI is no exception. How do you define your AI strategy? How do you find AI-literate workers or train existing staff?

Headlines about automation and workforce disruption are good for thinking about the future, but what is today's reality? From our latest AI trends research, we know that business leaders aren't adopting technology for technology's sake. Instead, they're acknowledging the now: AI may be the future, but what can automation and AI do for me today? Does the organization want to automate billing, general accounting and budgeting, and compliance functions? Automation and AI will likely be a part of the solution in all of these areas, and workers should not fear it.

Slowly but inevitably machine learning is starting to influence our daily lives. Whether you ask your home virtual assistant to check the weather forecast or cede some of the planning (and even driving) of your daily commute to your "smart" automobile, it is machine learning that is making life easier. Yet while we seem to have embraced machine learning at home, understanding and embracing its potential in the enterprise remains challenging. Judging by the experience I've had with our clients, the desire is there, experiments are happening, but there is difficulty in getting real change into production. Many organizations are not yet making the transformational changes driven from machine learning that will be needed in order to succeed in the coming years.

In today's business landscape, the value of a diverse workforce has become indisputable. Hard numbers support its positive impact on businesses' top and bottom lines. A recent survey revealed that 62% of participating executives rated diversity and inclusion as a high or top priority. Diverse organizations are often more successful than others. For example, McKinsey & Company found that gender-diverse organization are 15% more likely to outperform their peers, while ethnically-diverse organizations are 34% more likely to do so. With many of the world's leading economies facing an increasingly aging population, it is encouraging to see that retaining older workers can increase business productivity.

Rather than automating existing processes to make them faster and cheaper, true process reimagination forges new alliances between people and machines, enabling them to achieve more together. That's particularly salient for organisations in the public sector needing to reduce the risks they face. These new relationships are what Accenture calls the'missing middle'. Where machines help humans, there are three key dimensions. The first is where machines amplify or augment humans with new insights.