Three years ago, a project carried out by Price Waterhouse Coopers (a multinational network of professional services companies that operates as companies under the PwC brand), estimated that "Artificial intelligence technologies could increase world GDP by US $ 15.7 trillion, an extensive 14%, by the year 2030 ". However, the socioeconomic benefits of this technology appear to be predestined to meet only the needs of the first world citizen. Or worse still, of the country that achieves supremacy in the development of'thinking' computing systems, which are already transforming our societies and economies. On the other hand, to ignore that there are only two alternatives to align the course of our nations in the era of COVID, is to abandon the interests of our people and selfishly condemn future generations. Because there are only two options: 1) develop; 2) use smart systems developed by third parties.

A financial services company had a problem. It faced increased risk exposure from its artificial intelligence (AI) due to inconsistent monitoring, risk identification, governance, and documentation of multiple applications across its business units. It had to be addressed. The issues potentially exposed the company to poor customer experiences; negative brand image; and legal, regulatory, and compliance violations. Their AI models and applications were generating results quickly, sometimes within a few hours.

Service robots hit double digit growth worldwide. The market for professional service robots reached a turnover of 6.7 billion U.S. dollars worldwide – up 12% in 2020. At the same time, turnover of new consumer service robots grew 16% to 4.4 billion U.S. dollars.

VP Data & AI at ECS, roles have included co-founder at a data analytics startup, VP AI at Booz Allen, and Global Analytics Lead at Accenture. As the influence of artificial intelligence grows, it is increasingly vital to design processes and systems to harness AI while counterbalancing risk. Our charge is to eliminate bias, codify objectives and represent values. Responsible AI ensures alignment to our standards spanning data, algorithms, operations, technology and Human Computer Interaction. I am examining the importance of each of these elements in a series of articles.

Omnia's Trustworthy AI Module, Deloitte's unique artificial intelligence evaluation technology, has been recognized as'Digital Innovation of the Year' at the Digital Accountancy Forum and Awards 2021 in London earlier this week. This marks the second consecutive year Deloitte has garnered top honors for delivering innovative and disruptive technologies by The Accountant and International Accounting Bulletin. It also marks the fourth time Deloitte has won the award overall. Omnia DNAV, a digital cloud-based solution that revolutionizes the audit of securities and investments, was honored with the award in 2020. Deloitte won the 2018 'Audit Innovation of the Year' for its audit-transforming Cortex data platform and in 2015 for functionality using artificial intelligence that quickly identifies, extracts, and analyzes information across an entire population of documents.

More enterprises have adopted RPA functions to automate rote, repetitive tasks, but sometimes they need more capabilities. Enter machine learning functions and the result is "intelligent automation" which, unlike RPA, can learn and adapt. The choice between the two should depend on the use case, but in today's AI-crazed world, there's a misconception that intelligent automation must be better when, in fact, robotic process automation (RPA) may be a more elegant solution. "We view AI/ML as knowing what to do, RPA is knowing how to do it," said Muthu Alagappan, chief medical officer at intelligent automation platform provider Notable Health. "For example, OCR can be used to extract information from insurance cards, photo IDs and clinical documents. From there, RPA [enters] the extracted data into existing systems of record." RPA simply executes its programming, so if requirements change, it needs to be reprogrammed.

Derek Snaidauf is a Principal with Deloitte Transactions and Business Analytics LLP. He is a recognized expert in Artificial Intelligence, Trustworthy AI, Cyber, and Cloud, and is a frequent author and speaker on these topics. Derek holds leadership roles in Deloitte's Strategic Growth Offerings, Analytics & Technology, and Regulatory & Legal Support practices. During his 20 year management consulting and professional services career, Derek has helped numerous clients across industries navigate complexity, boost performance, and anticipate change. He supports them end-to-end on strategy, organizational design, process transformation, data science, and technology.

The real big leap in automation comes with the combination of RPA and smart voice and chatbots. Deloitte is at the forefront of this and uses Cognigy.AI not only with its customers but also internally in the HR department. Peter Fach showed an example of how a department head can pay a bonus to selected employees. Previously, this required contact with an HR employee, who provided information on the bonus program, answered queries and then took over the handling (booking, payment, etc.). Now, the team lead is guided through the process in the HR portal by a Cognigy AI-based chatbot.

AI adoption and maturity levels are significantly lower at other types of technology companies, with many companies insisting on seeing sector-specific use cases and proven results before scaling up their AI programs and investments. Many existing AI efforts in the sector are limited to scattered experiments and small-scale pilots, without an overarching strategy for harnessing the full power of AI and digital data. As more organizations shift their AI workloads to a cloud environment, data integration challenges are intensifying. Some of the most common barriers to access third-party data sources include dealing with disparate data that exists on different systems and merging data from diverse sources. For all these efforts, the right talent and expertise can be critical.

Skepticism about how productive employees could be if they worked from home also eroded. An April 2021 study from the Becker Friedman Institute at the University of Chicago examined companies' post-covid remote work plans and found a predicted productivity boost of 5% for the US economy. And a December 2020 survey by PwC found that 34% of employees said they were more productive than before the pandemic, while over half of executives said average employee productivity had improved. Now, an equally widespread disruption is underway in the form of hybrid work: while some organizations are insisting workers return to the office full-time, many are prepping for a new normal where employees spend some days in the office and some days working remotely, with a combination of virtual and in-person meetings and collaboration. A CNBC survey of executives in human resources, finance, and technology found that just under half of companies will use a hybrid work model in the second half of 2021.