How AI Innovation Can Speed Up Post-Pandemic Business Recoveries

CDAO Fall 2020 highlighted AI strategies and technologies that industry leaders are using to accelerate their recoveries from COVID-19

AI looks set to play a key role in America’s post-pandemic business recovery. As enterprises refocus on their core objectives, advanced AI technologies will enhance their efforts by improving processes, providing real-time insight and meeting rapidly transformed customer expectations.

However, many organizations still struggle to scale AI initiatives. This is often due to technical and organizational barriers that may not have been fully understood at the start of the journey. How to overcome these barriers and reap the rewards of being a first mover in AI was a central theme at Corinium’s CDAO Fall 2020 virtual conference.

“The intelligence revolution is going to dwarf everything that has gone before,” said DataRobot Director of AI Success John Sturdivant. “It is going to affect every industry. It is going to touch every business. And it is going to fundamentally change the competitive landscape and the way that value gets created.”

Here, we’ve collected the some of the key insights from this year’s conference about how AI can accelerate innovation and post-COVID-19 business recoveries.

Creating Unions Between Human Talent and AI

The goal of AI solutions in business contexts is generally not to create ‘general intelligence’ or replace human talent entirely. It is to achieve greater levels of machine and human collaboration and to automate low value tasks.

“We are really looking at augmenting human activity with machine prediction,” says Todd James, Senior Vice President at investment firm Fidelity. “This is where humans have the ability to make AI work better and where AI gives humans superpowers.”

These unions between human-only and machine-only work are becoming popular in the business community. They can achieve greater process efficiency and free employees up to focus on more challenging or rewarding tasks.

“These types of capabilities did not exist a few years ago,” James continues. “They are available through AI and they have a huge ability to dampen demand in our operations space as well as to focus our talent on higher order problems.”

Robotic process automation (RPA) is another tool enterprises are using to handle time and labor-intensive tasks and free staff members up to focus on innovation.

For example, bots with integrated RPA and machine learning (ML) functionality can be used to create revenue reports, provide insights on what is driving change and even to predict likely financial outcomes.

“[RPA] can be deployed really fast and it does not require a lot of system integrations,” says former Publishers Clearing House CAO Ash Dhupar. “We can create more time for our employees so they can really think through what they are going to do to create change.”

Organizations are Injecting AI into their DNA

Translating the potential of AI into tangible business value requires putting AI systems at the heart of decision-making processes. Businesses that have been able to do this successfully are already reaping the benefits. 

“I know there is a conversation about whether AI is still hype. For us, it no longer is,” says BMO Financial Chief Data Scientist Ren Zhang. “We are generating true value and [AI systems] being embedded into the business’ day-to-day function and working closely with them is the key.”

Of course, any change in business processes to incorporate AI should be carefully managed to make sure that everyone is brought along on the journey. Being AI-driven means embedding AI as a competency in an organization.

“It can’t be siloed off into a center of excellence. It can’t be a shared service group that is just taking orders from business units,” stresses John Sturdivant, Director of AI Success at software company DataRobot. “It has to be a skill that is embedded in your frontline in your business units.”

One way that Amarnath Lingam, Head of Enterprise AI and Advanced Analytics at technology products provider CDW, has managed to do this is by including staff from core business functions in the AI product development process.

“Make them part of your product team and then ideate with them,” he recommends. “They will be the voice to their teams and that is how the deployment will be eased.”

Shore Up the Data Fundamentals to Enable AI Success

The pandemic has upended years of historical customer trends and greatly increased the demand for new data and insights which more accurately represent the new business reality.

Considering the increased volume of and demand for new data, industry leaders should focus on the fundamentals of good data governance and ensuring data quality. Here too, human talent has an important role to play.

As Blackrock VP of Research, Analytics and Data Hamdan Azhar notes, the curation of incoming data is a crucial to generating accurate and useful insights from AI and ML models.

“[There will be issues] if the data in the back-end has just been allowed to come in and had not been curated, cleaned or processed,” he says. “If we want a human to be able to make use of this data then we really need to pay greater attention to the data quality.”

This focus on data quality is also a key element of success for the AI initiatives Zhang has championed at BMO Financial.

“Data is the key ingredient,” she concludes. “Without good data, no matter how wonderful your algorithm, there is nothing you can do.”

By refocusing on data fundamentals and scaling up the use of technologies like those we have discussed, industry leaders can enhance their organizations’ responses to the pandemic, better serve their customers and tap into the vast potential of AI to generate business value.

Discover the detailed sessions and case studies about the application of AI, ML and RPA in market-leading organizations by registering for CDAO Fall On-Demand here.