Many companies today are rapidly adopting new technologies and tools to improve overall efficiencies, improve customer and client experiences, and support key initiatives that are related to business transformation. However, these efforts, while necessary, bring with them growing pains for the workforce.

As our global technologies transform, so must our teams. What we have discovered in implementing emerging technology at U.S. Bank over the years is that effectively deploying and making use of new tools requires a skilled and diverse workforce and a technology team with a strong engineering culture to support it.

Banking on technology and people

The largest technology investment for U.S. Bank came in 2022 when we announced Microsoft Azure as our primary cloud service provider. This move accelerated our ongoing technology transformation, part of which includes migrating more than two-thirds of our application footprint to the cloud by 2025. Harnessing the power of cloud is just one of many ways that technology is enabling our organization to bring products and services to our clients faster, while enhancing our operations’ scalability, resiliency, stability, and security.

The technology transformation at U.S. Bank is also focused on adopting a more holistic approach to both external and internal talent pipelines. Diversity is a key component of our team building because true innovation and problem-solving comes from people with different perspectives. To attract new, diverse talent to join our team, we supplement traditional recruitment methods with proactive techniques that help build our company’s reputation as a leader in technology and to give back to our community.

For example, we’re positioning some of our top subject matter experts at relevant conferences and councils to share lessons learned from our transformation journey and we’re engaging with educational programs, like Girls Who Code, Summit Academy, and Minneapolis Community and Technical College to both develop and recruit diverse talent.

Our top workforce priority, however, is retaining our current team and equipping them with the skills they’ll need today and in the future. Because technology changes so quickly, we have adopted a continuous learning mindset where our teams embed learning into their everyday responsibilities and see it as an investment in themselves. To do that, we created a strategy that focuses on four key areas: an employee’s time, establishing a personal plan, providing effective learning tools, and offering ways to apply what is learned. 

1. Time: Establishing a flexible learning environment

We created an environment and performance goals that encourage our technology teams to regularly dedicate time to continuous learning. Each member of my leadership team operates a different type of technology team with different priorities, work schedules, and deadlines, so they are empowered to decide how to create the time and space for their employees to achieve their learning goals. Some have opted to block all employees’ calendars during certain times of the month, and others leave it to their individual manager-employee relationships to determine what works best. We’ve found that, by empowering each team to make these decisions, our teammates are more likely to complete their learning goals.

2. Plan: Growing skillsets and knowledge

Just investing the time doesn’t necessarily mean our teams will develop the right skills. So, we created a program we call “Grow Your Knowledge,” where managers and employees have ongoing skills-related discussions to better understand employees’ current skills, skill interests, and potential skill gaps. This helps them collaboratively create a personalized development plan. We’re also able to use the information to help us measure impact and provide insights on new trainings we may need to meet a common skill gap.

3. Tools: Learning paths and programs

We assembled a cross-functional team of external consultants, HR representatives, learning and development experts, and technical professionals to develop the Tech Academy — a well-curated, one-stop shop for modern tech learning at U.S. Bank. This resource designed to support our teams to learn specific technical, functional, leadership, and power skills that are needed to drive current initiatives. Employees can take advantage of persona-aligned learning paths, targeted skill development programs, and experiential learning. We even developed a Modern Technology Leadership Development Program for managers to help them better understand how to support their teams through this transformation.

4. Application: Putting experiential learning into practice

Providing experiential opportunities where employees can further build their skills by practicing them is an essential part of our strategy. Right now, we offer programs such as certification festivals, hackathons, code-a-thons, bootcamps, and other communities of practice for our teammates to hone their newly acquired skills in psychologically and technologically safe, yet productive settings.

Our certification festival, called CERT-FEST, is our most successful experiential learning program so far. We leverage our own teammates to train others in a cohort-learning environment for eight weeks. To date, our employees have obtained several thousand Microsoft Azure certifications. Hackathons and code-a-thons take that certification to the next level by allowing our technology teammates to partner with the business in a friendly, competitive environment. The winning teams at this event build solutions for new products or services that meet a real business or client need.

Learn today for the needs of tomorrow

Since we’ve started this continuous learning journey with our teams, we’re seeing higher employee engagement, an increase in our team’s reported skills and certifications, and a stronger technology-to-business connection across U.S. Bank. These efforts have also shifted our employee culture to acknowledge that working in technology means you will always be learning and growing.

Finding new, more effective ways to address the ever-shifting needs of our customers will always be a priority. But in a continuous learning environment the question we now always ask is, “What do I need to know today, to learn today, to do my job better tomorrow?” This has been the driving force behind our success in growing, retaining, and motivating our technology workforce.

Financial Services Industry, IT Training 

Pentagon Credit Union (PenFed), the second-largest credit union in the US, is looking to generative AI to transform how it interacts with its customers. Its vision? To create a new, cost-effective channel that helps meet members needs — and learns as it does so, to the benefit of members and the credit union itself.

“What’s happened in our business over the years is every channel is expensive and it doesn’t ever replace another channel. It’s just additive,” says Joseph Thomas, PenFed EVP and CIO, who notes that today 80% of PenFed’s interactions are digital, 15% are via call center, and 5% still rely on physical branches. “But we realized that with AI, we could add another channel of engagement but very cost effectively. We could add chat with a bot-enabled interaction to solve the early, simpler questions.”

Even with more than 2.9 million members, as a credit union PenFed doesn’t have the resources of a traditional bank. It doesn’t have an innovation lab or center of excellence to help it develop new technologies. But it does have more than eight years of experience leveraging supervised ML to support credit risk modeling and decision making. And in that time, it also adopted Salesforce.

“Salesforce is not just a CRM for us,” Thomas explains. “Salesforce is a digital platform, and it already had capabilities with Einstein as part of the platform, so we could cheaply and efficiently get into AI-enabled chatbots.”

The AI journey

The credit union started its new service strategy by deploying an Einstein-powered chatbot internally to support its IT service desk. The bot, which leveraged PenFed’s body of knowledge articles to assist end-users with tasks such as password resets, proved its effectiveness immediately and now handles about 25% of common internal service requests, freeing up service desk staff to focus on more complex tasks.

Once Thomas’s team developed experience with the platform, it began rolling out bots externally to the credit union’s members. Today, bots handle nearly 40,000 sessions per month, providing loan application status, product and servicing information, and technical support.

“We wanted to use AI internally before we unleashed it on the members,” Thomas says, adding that, with Einstein packaged with Salesforce, PenFed was able to conduct those internal experiments and later offer the new channel to its members at no extra cost.

PenFed now resolves 20% of cases on first contact with Einstein bots, with a 223% increase in chat and chatbot activity over the past year, Thomas says. The chat channel has also taken pressure off PenFed’s call center, which has reduced its average speed to answer by a minute, to less than 60 seconds, even as PenFed’s membership has increased by 31%.

But it is phase three of PenFed’s AI journey that Thomas is particularly excited about: Using generative AI for an assistant that can interact more naturally than a traditional chatbot while gathering data for insights that can lead to more personalized interactions.

“I don’t normally get hyped up on technology; I’m much more practical,” Thomas says, adding that his primary focus is always delivering value. “But what I’m seeing with generative AI is the missing ingredient to the world of digital, to the world of data.”

For years, CIOs have invested in data initiatives — data science, business intelligence, analytics — and they’ve also investing in digital channels, Thomas explains. But generative AI offers the potential to “snap data and digital together” to help institutions like PenFed go “from the digital credit union to the cognitive credit union,” he says.

Thomas offers up an example to illustrate his point. Today PenFed members can use the credit union’s digital channel to, say, change a CD from automatic to manual renewal. With gen AI in the mix, even as the bot helps a member perform this task, it can seek to understand the meaning behind it. In this case, the member may be shifting to manual renewal in order to facilitate moving their investments to a new account with another financial institution once the current CD matures.

“They’re going to take their money to [the other institution] because [the other institution] has got a better rate,” Thomas says. “Let’s say ours is 4.5% and theirs is 4.75%. In today’s world, we’re missing the digital forensics that members leave behind with the digital transaction.”

With generative AI, that insight could trigger the system to deliver the member a personalized offer of, say, 4.7% via the member’s channel of preference. The member gets a personalized experience, and the business could target members likely to churn rather than creating a marketing campaign that offers a 4.75% rate to 500,000 members.

“Now you get this hyper-personalized business transaction that benefits both parties,” Thomas says. “That’s just a small example. I think the combinations are endless.”

The copilot approach

As with its previous phase, PenFed is starting to use gen AI as a “copilot” for the credit union’s internal employee support line before the team extends the technology to its members. The next step will likely be a copilot for call center representatives dealing with member calls.

The credit union is using Einstein GPT on the Salesforce Financial Services Cloud because that’s where its knowledge articles sit. It is in the process of standing up Salesforce Data Cloud, which will act as the connection to other data sources.

“Data Cloud is going to be the zero ETL capability,” Thomas says. “It will get real-time data from Salesforce clouds and from our Snowflake environment.”

As Thomas sees it, that combination of real-time data and AI insights will further transform PenFed’s customer experience to an intelligent, mutually beneficial one for both the credit union and its members.

Digital Transformation, Financial Services Industry, Generative AI,

When Arvest, a regional bank operating in Arkansas, Kansas, Missouri and Oklahoma, hired Laura Merling as chief transformation and operations officer in 2021, one of the first things she changed was its digital transformation plan.

The 60-year-old bank, formed from the successive mergers of 14 regional banks, was planning to launch a neobank, an online-only service with national ambitions, as a way to ensure its future growth.

When Merling arrived in October 2021, Arvest had already begun the transformation process: conducting the first in a series of annual “Driving Change” surveys of staff attitudes and experimenting with the new core banking software around which it planned to build the new bank.

But there were challenges.

“Everybody was creating a retail neobank,” says Merling. That made for a competitive market in which the cost of acquiring a new customer was around $1,000. “You’d spend all your money on customer acquisition and not on building infrastructure,” she says.

On top of that, there was a degree of resistance — or at least indifference — to change within the company. Merling summarized the internal survey findings about staff readiness to change as one-third “Sure, I’m in,” one-third on the border, and one-third “I’m not really ready.”

It’s hard for staff to support change when it’s not clear what that change will be, she says.

Study, study, study

To get a clearer picture of where Arvest was, and where it wanted to go, Merling’s first moves were to commission one study of the company’s entire tech stack, and another of its data landscape. “We looked at them in parallel,” she says. “They’re related but also different: how easy is it to get to the data, and what data do we have?”

At the same time, she says, Arvest also conducted studies of its vulnerability to customer defection, and of the strengths it could capitalize on to build its new strategy.

“We did a lot in the first few months I was here,” she says.

The upshot of all that corporate introspection was a change in direction for the bank — or, rather, a return to what it had done best before. The plan for a neobank was dropped, and instead, says Merling, “We set forth a new mission. We wanted to be the leading community-focused bank serving commercial and small businesses.”

That didn’t mean the bank was turning its back on retail customers. By supporting local employers, “If you’re successful there, you’ll get the retail as well,” she says.

That meant building new applications and processes around its commercial loans — and making some changes to its core IT infrastructure.

A move to the cloud

A few months after Merling’s appointment, the bank announced a five-year partnership with Google Cloud as it prepared to digitize its contact center and move out of its two data centers.

“We can’t scale and be innovative if we’re just all waiting for on-prem,” she says.

Around this time, the results of the second Driving Change survey rolled in. Some of the transformation skeptics had drifted toward the “on the border” middle ground, but in the IT department — one of the first to see the clear direction as infrastructure changes began to take effect — resistance actually increased.

“It was all fear factor,” she says. “‘I’m going to lose my job,’ or ‘I don’t know this technology and I’m not going to get a chance to learn it.’”

Staff comments on the survey showed they didn’t feel their skills were valued, or even known, by management.

That prompted Arvest to create a program to help staff upskill or reskill. “We actually borrowed it from one of our partners,” she says. “They created it for their company internally.”

The upskilling program, called me@arvest, began in February 2022 with training for the IT team on Google Cloud as the company prepared to move its on-prem workloads there. “We needed people to know those skills,” says Merling. But creating the next wave of learning journeys took longer than planned. By the time it eventually happened in July, people were getting nervous they weren’t going to get the education, she adds.

The turning point was a full day of training around Google Cloud in November, with 500 people in the room and another 500 online. “We had our executives there, the bank presidents, the whole technology team plus,” she says.

Initially offered to the IT team, and later to operations staff, me@arvest will soon open to the marketing department.

Along the way, Merling decided to hire someone within the IT organization to run the program, which previously ran through HR. This plan enables her to meet the IT team’s skills needs up to ten years out.

A new purpose

One part of the original strategy Merling kept was the new core banking software from Thought Machine that Arvest had already experimented with — but now instead of redefining retail banking, it’s underpinning the modernization of the bank’s commercial lending processes.

“We’re basically rebuilding the entire technology stack for the bank by assuming a banking-as-a-service construct, whether we choose to use it or not,” she says.

By that, she means drawing on Thought Machine’s cloud-native, microservices-based approach, building new products and services that can be accessed through its APIs.

With the move to Thought Machine just beginning, the old core banking software won’t be going away just yet, so Arvest is using Google Cloud to deliver a single view of its customers. “It’s a key part of being able to serve customers going forward,” she says.

Merling brought in external help to stand up Google Cloud, training her own staff in parallel to ensure ongoing maintenance. “The Thought Machine work we did ourselves,” she says.

Under her transformation and operations umbrella are the CIO responsible for the existing core software, and the CTO building the new software. The development team was initially small, but is expanding through a mix of new hires and, as people learn new skills, internal transfers.

“My commitment to them was to make sure to bring everybody along for the ride,” she says. “I didn’t want to create an ‘us versus them’ situation. That’s super important to be able to grow the bank for the long term.”

Money talks

It’s a truism, but “90% of change management is communication,” she says.

To that end, each month she holds skip-level meetings with the second-level managers in her team, as well as “transformation talks” where the changes are presented to staff. Alongside the slow burn of rebuilding the banking core, these talks are also a chance to discuss “quick wins”: smaller technology changes that make a difference, such as the recent introduction of pre-authentication for customers calling the contact center. “That saved a minimum of 200 to 300 hours a month in call time,” says Merling.

Business Process Management, Cloud Management, Digital Transformation, Employee Experience, IT Leadership

Ivneet Kaur, Chief Technology Officer at Silicon Valley Bank, joins host Maryfran Johnson for this CIO Leadership Live interview, jointly produced by and the CIO Executive Council. They discuss the evolving digital customer experience, secure cloud migrations, agile-first, API-first strategies, competing for tech talent and more.

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