3 Keys To AI Success

AI is everywhere. Companies use it in myriad ways, and we all encounter it in some form every day. But are businesses wringing as much value out of AI as they could be? Some are. The most recent PwC AI Business Survey found that companies that stand out as leaders among those achieving AI results have one thing in common: Instead of focusing first on one goal, then moving to the next, they’re advancing with AI in three areas at once.

• Business transformation: Going big on digital transformation to streamline everything.
• Enhanced decision-making: Relying on data, AI and analytics to think and plan better, faster and longer term.
• Systems modernization: Employing an agile, iterative process of constant monitoring and making improvements that includes but isn’t limited to cloud transformation and widespread process improvement and automation.

A holistic approach is critical to achieving all three business outcomes, as opposed to focusing on one goal at a time, then moving to the next. At the heart of all three is data. AI can deliver more value when it’s embedded in application systems that work nonstop, analyzing and acting on data. These systems, in turn, need cloud-based computing power that can scale as needed.

When PwC and Amazon Web Services (AWS) team up to serve clients, we focus on transformation to accelerate business outcomes as part of their move to the cloud. Through this collaboration, companies are then able to better analyze and capitalize on data with AI and machine learning. That makes it easier to automate processes and speed up everything from routine reconciliation to more complex decision-making in a variety of industries and use cases.

• Financial services firms build their reputation on staying ahead of market trends, so they should offer innovative products and services while remaining compliant with increasingly complex data regulations. AI can be particularly powerful in fraud detection.
• Healthcare organizations need collaborative ecosystems that allow secure access to patient data and help them comply with a host of regulations. Researchers can use AI platforms to process large volumes of data.
• Retailers need to monitor product line performance, footfall in physical stores and user behavior across digital channels.
• Manufacturers require real-time information on supply chains, production machinery performance and distribution issues.

A unified approach to AI aligns well with a unified approach to data. Our survey indicates that 36% of companies with a holistic approach to AI are planning to use AI this year to help create a data fabric — an action-ready, 360-degree view of all data that touches their organizations.

Compared to companies that approach AI in a piecemeal manner, the leaders (just over a third in our survey) are far more likely (36% versus 20%) to report widespread AI adoption. They’re roughly twice as likely to report substantial value from AI initiatives to improve productivity, decision-making, customer and employee experience, product and service innovation, and more. By bringing leaders together from across the organization, a holistic approach facilitates scale and data sharing. The idea is to bring together AI specialists with analytics teams, software engineers and data scientists. Including business experience as well helps align outcomes with business priorities, helping with organizational buy-in and resulting in projects that deliver a measurable impact at a reasonable cost.

This holistic approach also allows companies to take fuller advantage of cloud technology to enable a data-driven transformation.

1. Business transformation to drive agility and trust

Business transformation is about agility and customer trust. At the heart of both agility and trust is the way your organization handles customer data. Today that often means using AI and machine learning to do more with data in near real time.

PwC and AWS are collaborating to help clients better leverage the power of cloud combined with PwC’s strategic frameworks to accelerate these transformations. Together, we help businesses address challenges around managing and using data, breaking down data silos, determining whether clients have the right analytical tools in place and implementing frameworks for security and privacy that allow for agility, speed and long-term sustainability.

The ability to respond quickly to market changes and customer feedback is another benefit of cloud-powered business transformation.

For example, PwC and AWS helped a financial institution identify customers who had been unhappy with their experience by analyzing more than 11,000 audio recordings of call center interactions. Using two AI/ML services from AWS, PwC built a solution for the client in just two weeks. Amazon Textract was used to convert the audio recordings to text files, which were then fed into Amazon Comprehend. Amazon Comprehend’s AI model categorized the semantics/expressions of the customer during the call and it pulled out key phrases related to the expressions conveyed. This significantly narrowed down the output, which was then displayed on an Amazon Quicksight dashboard for end users at the financial institution to explore. The AI analysis ultimately helped the company improve processes to avoid unhappy customers in the future or reach out to specific customers to address the issues they had raised.

The speed with which the financial institution was able to identify the unhappy customers exemplifies the agility cloud solutions can provide. The ability to quickly improve processes customers had complained about showed its commitment to providing a measurably improved customer experience — a cornerstone of trust.

2. Better decisions through analytics and AI

AI-supported decision-making is so powerful for a simple reason: it can enable you to incorporate and analyze exponentially more information than any human being could do on their own. Data-driven organizations strive to base strategic business decisions on the evidence provided by data. To achieve this at scale does require a certain rigor, however, along with an ability to innovate by identifying opportunities — with data, naturally — that can give rise to new products that enhance customer experience.

With the right data strategy, companies can get a handle on their data, find insights from diverse data types and make the data available to the appropriate people and systems. To help confirm they’re developing greater agility while achieving transformation, we recommend that you:

• Start with outcomes. As you consider new AI models in decision-making, don’t focus solely on the data you have. Start with the business outcome you seek and then look for the data and analytics to back it up. Consider which decision-makers will use the model to achieve this outcome, where the model fits within the decision-making process, how it can integrate with the cloud, and how you’ll monitor, test, scale, improve and eventually retire it.
• Let AI make your data actionable. Once you’ve identified the data you need, let AI help you find and use it. AI can wade through an ocean of data and extract exactly what decision-makers need when they need it.
• Focus, then scale. A holistic approach doesn’t mean doing everything simultaneously without considering how it’s all connected. An effective way to use AI in highly complex decisions is to start with a specific element, then add on from there.

3. Systems modernization makes more possible

Data sits at the heart of systems modernization. Modernizing their data architecture has helped many companies create a better customer experience and make running the business that much easier. Modern data ecosystems require a foundation of systems that allow flexibility and scalability, which cloud infrastructures are uniquely suited for.

Wyndham Hotels, for example, redesigned its data architecture to glean more useful insights and better connect with guests. Before PwC signed on to help the hotel chain more effectively utilize AWS tools and services, a single piece of data would travel an average of nine system steps taking up to two-and-a-half days. Now the journey is only four steps and takes as little as five minutes. The move to a more robust, scalable cloud environment with improved quality checks and better error detection resulted in better overall accuracy of guest data. The company can also build on its data capabilities as customer needs and the hospitality market evolve.

When looking at long-term systems modernization it’s important to consider these questions.

• What systems do we use to acquire and store our data, both on-premises and in cloud-based systems?
• How do we make this data available, and to whom?
• What tools do we use or allow teams outside of traditional IT roles to use?
• How are we working across the business to upskill our teams to work with data?
• How do we confirm data security?

Meanwhile, it’s important to emphasize that systems modernization is about people as much as IT. PwC’s commitment to human-led, technology-powered innovation extends to our own digital transformation, too.

Learn more about how PwC and AWS are helping companies leverage cloud capabilities, including AI and machine learning, and dig into the results of our AI Business Survey.