Digital transformation

Enterprise modernization initiatives are too often threatened by aging infrastructure and systems that have run out of technical support, according to a Kyndryl report. The IT services firm surveyed 3,200 C-suite executives and collected anonymized customer data from its Kyndryl Bridge platform.

While 9 in 10 executives said their company’s technology is best-in-class, nearly two-thirds acknowledged that outdated systems present a major concern. Data indicating 44% of mission-critical enterprise IT infrastructure is approaching or at end-of-life confirmed the apparent paradox.

If a company lacks comprehensive IT asset and configuration management, locating tech debt is a challenge, according to Michael Bradshaw, Kyndryl’s SVP and global practice leader for applications, data and AI. “It’s almost like an archeological dig,” he said. “You don’t know where the problems are unless you stub your toe on something that’s reached end-of-support.”

Executives are bullish on additive digital transformations that enable smarter chatbots, sleeker dashboards and more user-friendly interfaces. But drumming up enthusiasm for the everyday gruntwork that keeps the IT machine humming can be a challenge.

“It's hard for companies to strike that balance between investment in the new and making sure that you're tending to and you're being a good steward for those things that have been around for a period of time,” Bradshaw said.

Headlines, regulations and business disruptions can help bring IT security and privacy risks into focus. When companies absorb the financial and reputational hit of a data breach, it keeps cyber resilience on the corporate radar.

Nearly two-thirds of executives worry about cyberattacks and less than one-third feel their organization is prepared to manage those risks, according to the survey.

CIOs can reframe conversations around tech debt to emphasize the business value of remediation investments on data flow and AI adoption, two related enterprise priorities.

Three-quarters of respondents are investing in traditional AI and machine learning but fewer than half see a positive return on investment, the report found. While data privacy and compliance concerns are two of the most visible hurdles, a lack of investment in core infrastructure can consume ROI.

“When you’re talking about refreshing technology to eliminate technical debt, you’re rarely in an ROI conversation,” Bradshaw said. “The only time you get recognized is when a project delivers on time or if something goes wrong.”

The global IT outage triggered by a faulty CrowdStrike security patch on July 19 may have helped remind enterprises of the potential cost of poor asset management. In the aviation industry, for example, IT asset management helped dictate how long it took individual airlines to recover from the event.

“CrowdStrike brought to light topics that executives wouldn't otherwise be thinking about on a regular basis,” Bradshaw said. “If you didn’t have good asset and configuration management, you were in a world of hurt.”

Preventative solutions can still be a hard sell.

“It’s almost like the Maytag repairman in reverse,” Bradshaw said. “You’re doing the work to prevent something from happening and, because of that, nobody’s ever going to know.”

As tech leaders look ahead to next year, most anticipate IT budgets to increase, according to Forrester. The analyst firm surveyed 2,200 business and technology decision-makers for two reports.

Nine in 10 respondents expect a bump in IT spending in 2025. Three-quarters of executives anticipate larger budgets for software and technical personnel, driven by growing enterprise appetite for AI capabilities and broader modernization initiatives, the reports found.

Forrester projects tech spend to increase 5.3% year over year in 2024, up from a 4% bump in 2023. But for many organizations, the budget increases will merely keep pace with inflation. “Global inflation is averaging 5.9% and will force tech executives to strategically reallocate IT budgets,” Forrester VP and Research Director Matthew Guarini said in a recent blog post.

Business leaders want more and better technology to streamline operations, improve efficiency and drive the innovation that grows revenue. But in an election year marked by global economic volatility, they can’t risk runaway budgets.

CIOs have grown accustomed to the push-and-pull dynamic underlying digital transformation aspirations. More cloud use led many companies to feel the weight of growing costs. Generative AI’s hunger for high-capacity compute resources is having a similar impact as investments in the technology fail to produce measurable returns.

Cloud and AI will nevertheless be major areas of investment in the coming year, Forrester VP and Research Director Paul McKay said in a blog accompanying the reports. “Organizations are investing even more heavily in cloud, both for AI infrastructure and services support, along with the migration of more of their workloads to the cloud,” McKay noted.

Looking to generative AI for immediate gains is a short-sighted mistake, according to Forrester. While nearly 9 in 10 respondents expect to increase spending on AI-related data infrastructure, talent and tools in the next 12 months, that money will be wasted in the absence of a broad and flexible long-term strategy.

“You’ll need the ability to run AI workloads wherever needed, from the PC to the data center to the cloud, and you’ll need a modern data platform,” Forrester said.

McKay recommends tech leaders increase or defend current levels of enterprise investment in cloud usage optimization practices, trimming spend in targeted areas in order to attenuate the cost of AI workloads.

To offset growing spend on AI, cloud and tech talent, CIOs can reach for a number of other potential cost-cutting solutions.

As platform-based strategies mature, enterprises should focus on eliminating technical debt tied to siloed, single-task applications.

“Years of teams building and maintaining their own applications or insisting on idiosyncratic ‘ideal’ architectures has resulted in massive technology sprawl,” Forrester Chief Research Officer Sharyn Leaver said in a blog post.

“It’s time to inventory your bespoke applications and isolated infrastructure that serves just one or a few applications and replace them,” said Leaver.

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By Lumen

The 2024 Formula One (F1) season has transformed into must-see TV, becoming one of the most competitive seasons in recent memory and captivating millions of fans worldwide. As a fan of the sport, I reflected on what makes F1 so enthralling.

The speed at an F1 racing event exemplifies precision and high-performance engineering. Drivers and crews synchronize perfectly, fine-tuning their cars with remarkable speed to achieve optimal performance. F1 cars, representing the pinnacle of automotive engineering, utilize cutting-edge technology, including telemetry systems that gather real-time data on car performance. This data is crucial for optimizing race strategies, pit stop timings and overall car efficiency. High-speed cameras and sensors capture every detail of the car’s movement, enabling engineers to refine every aspect of the car’s setup and the driver’s technique.

There’s no question that technology has played a pivotal role in transforming racing. By integrating these technologies into their strategies, F1 teams gain a competitive edge, refining performance and pushing boundaries of what is possible in a sport where milliseconds count.

Similarly, Lumen is also pushing the boundaries of what’s possible with our own digital transformation driven by the desire to improve the customer experience, enhance operational efficiency and reduce costs.

The race to operational transformation

In this race, teamwork, innovation and precision count. Each technology we implement helps propel our operations forward with increased speed and efficiency.

• Targeted processes and goals: Previously, our order management teams were operating with static date-setting calculators that did not have the intelligence to set proper expectations for our customers based on actual availability. By evolving our date-setting capabilities to ping the network for live capacity, Lumen is able to provide service dates based on actual real-time availability.
• Implementation of innovative solutions: Lumen is working to significantly improve its operational efficiency and the customer experience by implementing ServiceNow Streamlined Order Management, Interval Quoting and Service Orchestrator and combining incident and change management. Lumen is also enabling AI/ML integration to enhance operational intelligence.
• Overcoming challenges: As in every race, you must rise above challenges. Large investments like this take a significant amount of time, capital and resources. We stay nimble by using agile development methodologies and AI to effectively develop and launch these tools that are critical to our customers’ success.  

Fine-tuning for optimal performance

In F1, teams constantly seek small, incremental improvements in car performance, pit stop efficiency and race strategies including learning from mistakes, such as Ferrari’s pit stop errors in 2019.

Similarly, Lumen encourages employees to view challenges and failures as opportunities for growth and to maintain an open mind and commitment to continuous improvement. One example is the Kaizen program, which encourages employees to leverage their unique perspectives to help achieve continuous improvement through small, incremental changes. Both examples emphasize teamwork and collective problem-solving to identify areas of improvement and implement solutions.

Setting the pace for tomorrow

Looking ahead, Lumen is setting the pace in innovation, accelerating into a promising future with AI capabilities and groundbreaking advancements. Lumen has teamed up with Microsoft to boost its enterprise-wide digital transformation leveraging Microsoft’s cloud and AI technologies. Just as F1 drivers leverage wearable devices to gather vital statistics, Lumen uses AI to monitor the digital pulse of systems and user interactions. This data is pivotal for machine learning algorithms to analyze and enhance operational efficiency, user engagement and system responsiveness.

At Lumen, we have one primary goal in mind as we incorporate these changes into our systems and processes: to improve the customer experience and connect people, data and applications—quickly, securely, effortlessly.

Citigroup saw returns from its ongoing modernization efforts during the three-month period ending Sept. 30, bank executives said.

“While we continue making substantial investments in our number one priority — our transformation — the efficiencies gained from our simplification and other efforts drove a 2% reduction in expenses,” CEO Jane Fraser said in the bank’s Q3 2024 earnings announcement.

The bank reported $13.3 billion in expenses, down from $13.4 billion in Q2 and from $13.5 billion a year ago. “Although a lot more work remains, we've started to see benefits from our prior investments play through,” CFO Mark Mason said during a Tuesday earnings call. “We continue to simplify our technology infrastructure, retiring over 450 applications year to date and now over 1,250 since our 2022 investor day.”

Citi’s digital transformation was spurred by compliance lapses stemming from poor data quality.

The Federal Reserve Board and the Office of the Comptroller of the Currency each fined the bank $60.6 million and $75 million, respectively, in July, stating Citi had failed to make sufficient remediation progress following a 2020 enforcement action.

Data overload coupled with technical debt continues to plague the banking industry, according to a recent Publicis Sapient report surveying 1,000 senior bank executives.

While satisfying regulatory requirements was Citi’s initial modernization motivation, simplifying processes and platforms became a broader goal under Fraser, who rose to her current position as CEO in 2021.

“The transformation reverses historic underinvestment in Citi’s infrastructure,” Fraser said Tuesday. “It enhances our risk and control environment and it's a strategic overhaul, as we've talked about, that goes well beyond the consent order to simplify and to strengthen Citi to the benefit of all of the stakeholders we have.”

Among the wins cited by Citi in Q3 were a reduction in data center costs achieved by migrating workloads to private cloud. The company also streamlined public cloud onboarding processes, reducing application migration time “from over seven weeks to two weeks,” Mason said.

Citi saw revenues increase 1% year over year to $20.3 billion in Q3. The company spent $12 billion on technology and retired 390 legacy applications in 2023 as part of the modernization push.

Banks are grappling with data overload and technical debt on the road to digital transformation, according to Publicis Sapient’s third Global Banking Benchmark study. The technology consulting firm surveyed 1,000 senior banking executives.

Despite eagerness to scale AI capabilities, nearly one-third of respondents cited budget constraints as a barrier to modernization. Banks have also been held back by legacy tech, regulatory challenges and a lack of operational agility in the last year, the report found.

“Whether the focus is on traditional AI or generative AI, banks’ success stories have one thing in common: the right data,” the report’s authors said. “The challenge for banks isn’t a lack of customer data — they have plenty of it. The challenge is that banks’ data is typically siloed across lines of business.”

Digital transformation in banking boils down to a data problem. The quest for scalable generative AI business solutions underscores the challenges as banks struggle to harness mountains of business and customer data.

“Over the last couple of years, we’ve seen a proliferation of data,” David Donovan, EVP and head of financial services for North America at Publicis Sapient, said. “It doesn't just come in spreadsheets and numbers; it comes in tweets, pictures, videos and chats. All those inputs need to be fed into an architecture that can consume it.”

Unstructured data has the potential to change the industry over the next decade, Accenture Senior Managing Director and Global Banking Lead Michael Abbott said during a CIO Dive live virtual event in March.

While organizations are modernizing to reach AI goals, leaders are also using the technology to ease the process. Generative AI coding tools trained to modernize mainframe COBOL applications can help banks wipe away technical debt, Abbott said.

AI and the data that powers it are the most critical areas for modernization in banking over the next three years, according to Publicis Sapient. But transformation reaches deep into the core of the enterprise and across its many business units.

A traditional investment bank has an average of 75,000 individual databases, according to Donovan. “A lot of those databases have duplicate data, inefficient taxonomies and, for lack of a better term, fragmentation,” he said.

Pulling data assets together onto a unified platform requires major investments in cloud and other technologies.

JPMorgan Chase, a leader in AI research and adoption according to research firm Evident, expects to pour $17 billion into technology this year, a 10% increase from $15.5 billion in 2023. As part of its AI strategy, the bank rolled out its LLM Suite AI assistant to 140,000 employees last month.

“Banks have more information on their clients than anyone,” Donovan said. “They know how much you make, they know your mortgage, they know your car payment, they map out your credit cards — they can pretty much figure out where you are on a day-to-day basis if they wanted to.”

While an unsettled regulatory environment around AI data usage has put the brakes on some plans, banking executives are directing an average of 29% of their digital customer experience investments into machine learning and AI, according to Publicis Sapient.

Banks are also laying the groundwork for scaling AI in the cloud, the report found. Nearly two in five respondents said their company had prioritized cloud infrastructure investments over the next three years.

“The legacy technology is at its limit,” Donovan said. “It's not going to be able to absorb the amount of information that these enterprises are going to need to access to be competitive.”

Nearly all — 97% — top business chiefs expect generative AI to materially impact their operations as pressure mounts to demonstrate tangible results from AI investments, according to an NTT Data report.

Nearly two-thirds of the C-suite expect their investments in the technology to drive significant industry transformation next year, the survey of more than 2,300 business leaders found. Yet, almost half of CISOs said they have felt pressured and overwhelmed by generative AI, significantly higher than their C-suite peers. 

Despite the enthusiasm overall, most organizations aren’t yet set up for success. Just over 2 in 5 respondents strongly agree existing generative AI tools meet their expectations, and most organizations lack the necessary capabilities to integrate the technology. 

All enterprises want the money and resources they poured into generative AI plans to pay off, but some are closer than others. 

There are signs of enterprise progress on generative AI outcomes. Nearly three-quarters of business leaders say they are now very satisfied with existing generative AI efforts, compared to just 41% a year ago, according to the NTT Data report. 

Businesses are most often using the technology to power personalized service recommendations, knowledge management tools, design and development processes and quality control, the report said. 

But pain points persist.

Nearly 90% of C-suite members are concerned about security risks associated with generative AI, according to NTT Data. Other worries include AI’s accuracy, costs, evolving regulatory provisions and ethical implications. 

Still, most IT leaders believe their teams can mitigate the technology’s risks. To help combat the issues, enterprises have upped employee training, focusing on responsible and secure use of the technology.

AI adoption and a shift toward hybrid-by-design IT strategies have raised the enterprise profile of the mainframe, according to an IBM Institute for Business Value report. The company collaborated with Oxford Economics to survey more than 2,500 global technology executives.

More than three-quarters of respondents said mainframe-based applications are integral to their digital transformation plans, an increase from less than two-thirds in the survey conducted last year. Nearly 4 in 5 respondents agreed that mainframes are “essential for enabling AI-driven innovation and value creation,” the report found.

“We're coming back from a cloud-first swing,” Tina Tarquinio, VP of product management for IBM Z and LinuxONE, said. “Our customers are looking at how they can use all the investments they’ve made to get to better outcomes.”

The cornerstone of legacy IT has found an ally in an up-and-coming technology. Generative AI adoption triggered a resurgent appreciation for mainframe capabilities as models gravitate toward a goldmine of on-prem enterprise data.

Mainframes are repositories for some of the most valuable business data, according to Brian Klingbeil, chief strategy officer at IT services company Ensono. “You can’t do machine learning and cool AI stuff without good data,” he said.

Even after a sustained wave of cloud migrations, mainframes still handle 70% of global transactions by value, according to IBM. “It would be hard to book a train, plane or hotel, or even swipe a credit card without interacting with a mainframe,” Tarquinio said in April, on the occasion of the platform’s 60th birthday.

As enterprises modernize mainframe applications to enable flexible hybrid cloud ecosystems, IBM has doubled down on AI capabilities. The company’s next generation mainframe will carry the high-capacity Telum II chip and Spyre AI accelerator when it hits the market early next year, according to Tarquinio.

While enterprises commit significant resources to mainframe application modernization, the process remains a challenge, according to a Forrester report commissioned by Rocket Software. Full rewrites commonly fail on the first try, the survey found.

IBM trained the watsonx Code Assistant for Z to translate COBOL applications to Java, but restructuring the original programming is often all that’s needed.

“So far, most of our clients have been so happy just to get healthy COBOL,” said Tarquinio. “You can modernize your COBOL and keep those high-performance characteristics that you want for an application.”

Nearly three-quarters of enterprises use generative AI tools to help modernize mainframe applications, the IBM report found.

As veteran mainframe engineers retire, companies are grappling with potential talent shortages, too. Nearly two-thirds of respondents are actively recruiting mainframe personnel and just over half rely on third-party support for mainframe services.