Traci Gusher, Principal, Data & Analytics and AI @ KPMG
Traci Gusher, Principal, Data & Analytics and AI at KPMG

PHILADELPHIA – Across all industries, AI adoption is increasing. In the last year, AI adoption grew by over 60%, with almost two thirds of enterprises harnessing the power of AI technologies in 2017. However, understanding the value of the technology and actually finding a valuable use case are two different problems.

For Traci Gusher, Principal in Data & Analytics and AI with KPMG, AI is no longer simply ‘nice to have’; it is a business imperative. Business leaders must not only think about AI, but build a strategy for how AI will help their companies remain competitive. Ahead of The AI Summit San Francisco, we caught up with Traci to explore why AI is so important to businesses today.

Why should large enterprises consider AI? How can they start thinking about their own everyday business problems in relation to AI solutions?

Organizations that do not start incorporating AI in their business and customer processes could soon have trouble effectively managing costs. They may have difficulties penetrating new customers and markets and fail to innovate with speed and agility.

If businesses are looking at using AI to solve everyday business challenges, I say ‘stop’. Don’t look at everyday business processes. Leaders should start by looking at their enterprise risk management plan, last earnings announcement, and long-term strategic plans and then ask these questions:

  • What are our top challenges?
  • What are our top opportunities?
  • How can AI help?

Organizations that take a business- and outcome-focused approach can help focus time, capacity, and dollars on areas that can enhance performance, customer experience, and the bottom line. Companies can realize benefit from implementing AI for some small process areas, but these processes should be part of a focused, strategic plan that aligns to business imperatives.

These projects are far more likely to move from prototype to production and not suffer from failure-to-launch outcomes, which are becoming all too common.

What does KPMG Ignite mean in practice for enterprises looking to leverage AI?

KPMG Ignite provides a platform that brings together open source and partner-provided functionality with KPMG proprietary accelerators. By leveraging KPMG Ignite, we can help clients build prototypes, embrace experimentation, and bring the right models to production to enhance business benefits fast. KPMG Ignite also leverages the combined knowledge of the firm by utilizing subject matter professionals with deep industry and domain knowledge to make models that are fit for purpose.

“For AI to drive value, the business must be ready to change as a result.”

How can enterprises meet key digital challenges for 2019 – and where does AI fit?

Organizations want to move fast to solve ground-level process challenges and buy software to solve single issues. Sometimes this makes sense. Leaders who explore how one area resembles another can lower costs and see value from technology investments faster.

We are working with several companies to identify AI uses across channels and find synergies in AI approaches to solve different business challenges. Instead of buying five different software products for natural language processing, natural language understanding, and machine learning, organizations can build a sustainable machine learning architecture that enables training other NLP, NLU, and ML models for more uses.  This is a significant part of the premise of our KPMG Ignite Platform; build for scale, model for specifics.

What are the key obstacles to making AI work for global enterprises?

One of the most prevalent obstacles now is failure-to-launch. Organizations often get stuck in the lab and move very few solutions to production. This is as much a technology issue as it is a cultural one.

Firstly, data scientists and engineers work differently than most IT teams. Data scientists and engineers are creative, experimental, and focused on modeling to an outcome. IT teams are program- and project -focused with deadlines and policies driving their work. It is critical to build a cultural bridge between these teams and closely integrate throughout the lab and industrialization processes.

Secondly, for AI to drive value, the business must be ready to change as a result.  It is necessary for organizations to plan and work as hard at preparing to change processes, and even people as a result of deployed AI, as it is to build the capabilities.

What does competitive advantage look like in the context of AI? 

Competitive advantage means being able to not only make regulatory or compliance processes more efficient, but also help them add value. It means enabling digital assistance for your customers with the ability to seamlessly switch to human interaction when needed.

Businesses with the ability to understand customers at a new level and proactively address their needs can lead the competition. Those with efficient processes that need little human attention are able to free up employees to work on the long list of projects you wish they could do—if only there were more people to do the work.

As told to Ciarán Daly


You can find out more about how KPMG is delivering artificial intelligence to global enterprises by watching their keynotes or by meeting them in person at the upcoming AI Summit San Francisco. Find out more.

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