Deloitte in the news
Adopting AI is Natural Evolution for any Insight-Driven Organization
David Steier, managing director of Advanced Analytics and Modeling at Deloitte Consulting LLP
AI Business recently got the chance to speak to David Steier, managing director of Advanced Analytics and Modeling at Deloitte Consulting LLP. Using advanced analytic and visualization techniques, including predictive modeling, social network analysis, and text mining, David and his team of quantitative specialists at Deloitte help clients across a variety of industries to solve some of their most complex technical problems.
We start by discussing Deloitte’s position on AI–where are they up to as a ‘Big Four’ consultancy firm, and what are their key goals for AI in the short- and long-term? David shares this thoughts:
“In the short-term we plan to evangelize AI and its impact on the enterprise–the scope and breadth of these technologies, and how AI is going to transform many aspects of business from back end operations to customer interaction. In the long run, the goal is to provide prototypes and enterprise scale cognitive applications to clients across industries”.
But the scope for AI is not just client-focused, David continues:
As a firm with innovation at its heart, we plan on leveraging the breakthroughs in AI internally as well. This will help augment the intelligence and productivity of our workforce, enabling us to innovate our offerings and enhance the value we provide to clients.
David explains the advantages that alliances provide Deloitte within the marketplace:
“We’re always on the lookout for promising technologies that augment the scope of our automation, engagement, and insights offerings. Our alliances with firms like IBM around technologies like Watson enable us to provide well-rounded cognitive applications to our clients using best-in-class technologies and platforms. In conjunction with our proprietary algorithms and deep industry knowledge, we are able to provide industry-specific solutions to clients, efficiently and at scale”.
Like with all emerging technologies, the road to implementation of AI is not necessarily a smooth one. Interestingly David says that the “perception that AI is a technology of the future” is a challenge businesses face when looking to adopt the technology:
“Making AI real for clients and our own business leaders through working prototypes is going to be key to overcome this challenge”.
“And lastly, but probably the most important of all”, David points out, “are the concerns around data privacy and security”:
“While personal data is already being collected in different ways, the legal and ethical frameworks to leverage this data in AI-enhanced applications are almost non-existent. As the space matures and rates of AI adoption increases, there needs to be a multi-dimensional approach to develop these frameworks”.
From a consultancy standpoint, David is well placed to comment on what is important within an enterprise to allow it to embrace AI and enable its adoption. He shares his thoughts on this:
“Adopting AI technologies is a natural evolution for any company that seeks to be an insight-driven organization. Having a culture of experimentation and innovation embedded throughout the enterprise will help organizations to fully tap into the magnitude of impact that AI enables. A culture of design thinking is also necessary to fully recognize the transformative impact of AI across an organization”.
Looking out to the enterprise landscape, David says “industries with a lot of unstructured data (ex. medical notes, forms, and geo-data) have the greatest potential for transformation using AI”:
“As AI enables automation of knowledge intensive processes that are reliant on the interpretation of unstructured data, we’re seeing great demand for these technologies in the healthcare and financial services industries. Concurrently, retail and other customer facing industries are increasingly demanding AI technologies to deliver new models of engagement to consumers. As with the adoption of any new technology, we’re seeing a variance in rates of AI adoption, but in the medium-long term, AI will be embedded into enterprises across many industries”.
He goes further to describe several use cases for AI in key industry areas:
“Some of examples of AI in action include automating the labor intensive prior authorization process in the healthcare industries, preventing money laundering in the financial services industry by delivering contextualized deep insights in real-time, and intelligent agents that are enabling better health outcomes for patients by engaging providers, patients, and health plans at the right time through meaningful interactions”.
This article originally appeared on aibusiness.org as part of the AI Summit in San Francisco. David delivered his keynote on ‘Cognitive Computing: Reimagining the Enterprise’ and was joined at the event by fellow CxOs from the world’s leading enterprises and the most exciting AI software developers, gathering to explore the huge opportunity that AI presents all industry verticals.
And there’s another “perception” that David cites as a barrier to adoption: “the perception that AI is going to replace humans”:
“It’s critical for firms like ours to educate business leaders that AI can be used to augment the intelligence of our workforce, not necessarily to replace them”, he says.