Digital transformation

Cloud-based data output overwhelms human management capacity, according to a Dynatrace survey of 1,300 CIOs and other senior IT leaders.

More than 4 in 5 respondents said the technology stack has become more complex in the past 12 months. Half expect the complexity to worsen.

Multicloud technology estates include a dozen different platforms and services, on average. The rising complexity hinders security and customer experience, according to the majority of respondents.

Data powers enterprise decisions, but managing the influx of information has long been a challenge in IT management.

The problem is one of missed opportunities, according to Dynatrace CTO Bernd Greifeneder.

Without the ability to turn data into insights, teams are slow to spot IT and operations issues, delaying responses, Greifeneder said in a release accompanying the study.

Manual approaches to log management and analytics won't keep up with the volume of data modern multicloud implementations pump out, according to 4 in 5 respondents. 

Running a data- and analytics-driven business is one of seven major components to high-performing IT operations, according to a Forrester report published in February. An effective data strategy depends on strategy, people, process, data and technology, according to the firm. 

"It all starts with C-level commitment to invest in these capabilities and clearly communicate the mandates to the rest of the organization," Forrester said. "High performance also requires balancing governance and agility via proper organizational role alignment and responsibilities and leveraging democratized low-code (but governed) development of analytical applications."

Spring clean outs aren’t just for the hallway closet or the garage. Tech stacks can use a good decluttering too, especially if it’s not already part of a regular cycle of software review. 

It can be a major undertaking — and come with major savings too. 

In 2023, Citigroup retired nearly 400 legacy apps as part of a process to simplify its technology infrastructure. “The modernization of our tech infrastructure is proceeding at pace, allowing us to deliver new capabilities to our clients," Citi CEO Jane Fraser, said during a January earnings call. 

“We consolidated trading and reporting platforms and retired 6% of our legacy applications for the second year in a row,” she said. 

While not every company is as big as Citi, a “hairball” of a tech stack can create a world of problems, said Karl Threadgold, managing director at Threadgold Consulting. Having “a lot of disjointed systems that aren’t integrated” can make work harder, and be a waste of employee time and financial resources. 

“The key thing is not having that single source of truth,” he said.

When to do a clean out

Rather than determining a one-time software clean out, organizations can take on a continuous cycle of analyzing what software applications are used and how. 

That’s not just reserved for major corporations, said Akshara Naik Lopez, senior analyst at Forrester. She’s seen that regularly happening for mid-market and smaller companies, too.

Different events can also trigger a reexamination of software applications, including:

• When applications are nearing support end dates. 
• When a software vendor is offering new functions or capabilities. 
• When SaaS contracts, which typically run three to five years, are up for renewal.

Clients are looking at software contracts 12-18 months ahead of renewal to decide if they should renew or purchase more functions, according to Naik Lopez. That lead time can also help allocate budget should a major change need to happen, she added.

Companies can make maintaining information and inventories of software part of employee job functions. Businesses could, for example, require employees to review software inventories once a quarter. 

“If information is accurate, that will help you see where you’re trying to go,” Naik Lopez said. “If it’s not maintained, none of this effort is going to help you. The decision points are just going to be wrong.” 

Creating a stay or go criteria

A clean out may be a necessary part of growth. Fortified is currently working with a financial services company that was acquiring a large number of organizations through mergers and acquisitions. 

They were faced with a bundle of systems, and had to figure out “what we have and what we invest in and not invest in,” said Ben Debow, founder and CEO of Fortified.

Some of the platforms were legacy systems over 20 years old. “They are reaching the end of their life, and having so many challenges adding new features and capabilities,” he said.

Fortified and its client started figuring out what functions are dependent on those legacy platforms, and what they would need to change if they were replaced. They also had conversations across business units to better understand their critical goals and objectives, which helped them create an evaluation criteria around what stays, what’s updated, and what goes out the door. 

This organization ended up deciding to rewrite its mission critical application, which it is in the process of doing right now.

Working with end users

Successful spring cleanings require partnerships with end users — usually employees. Dumping a new way of doing work onto their desktops could spur resentment and set the project up to fail.

Instead of presenting end users with a new solution, Threadgold said to involve them early on, "particularly with software that touches so many different departments.” 

Finding out where end users are encountering pain points such as where errors emerge or what’s causing work delays, can help companies decide what software will work best for their organization. 

Bringing staff into the planning process can also make them excited, rather than resentful, about change. It's “building a vision with the team of what the future could look like,” Threadgold said.

Budgeting for new or upgraded software should include resources to work with staff to help with the transition, said Naik Lopez.

“There needs to be time allocated for change management and working with the right service providers who help you handle change management,” she said. 

Sponsored content

By General Assembly

It happened seemingly overnight. Generative AI, exemplified by ChatGPT, skyrocketed into prominence in late 2022, amassing 100 million monthly users within two months as the fastest-growing consumer app in history. Now, having settled into orbit with 100 million weekly users and adoption by over 90% of Fortune 500 companies, AI's democratization continues to reshape talent demands, compelling employers to seek new skill sets and roles.

What does this mean for tech leaders? 

General Assembly's State of Tech Talent 2024 explores the transformative impact of on-demand, generative AI on the global workforce. Drawing insights from a survey of over 1,000 HR professionals globally, the research provides perspectives on talent acquisition and development in rapidly evolving AI-driven workplaces.

With the stage set, let’s examine the report’s main findings:

AI skills are in extremely high demand. 

Of the companies surveyed in the United States, Canada, the United Kingdom, and seven other countries across the globe, 97% use AI in their processes or products. And within the last year, 95% of managers have added AI skills to job requirements for non-AI-focused roles.

Yet, 69% of HR leaders are having trouble finding candidates with AI skills — which are even scarcer than data analytics, data science, software engineering, or UX design skills.

Companies are paying more to acquire the tech talent.

Faced with a low supply of AI-skilled candidates, 91% of HR leaders report that qualified applicants are demanding higher salaries, with 66% agreeing to these higher offers. Furthermore, job seekers in Europe and Asia, or at AI-heavy organizations, are more likely to secure higher pay.

Companies are spending significant sums to fill AI positions, with over half spending at least $10,000. In North America, the average recruitment cost for AI roles is $23,247, with 11% of companies worldwide spending over $50,000 in their recruitment efforts. 

It’s critical — and difficult — to get tech hiring right. 

Despite the high stakes, 93% of HR leaders find that sought-after job description skills are evolving more rapidly than ever, and 29% admit they don’t fully understand the roles they’re hiring for. Once employees are in the door, the hurdles continue: almost all HR leaders (97%) have experienced the need for different skills in new hires after onboarding, emphasizing the necessity of upskilling programs.

AI talent shortages lead to various challenges, including delayed technology implementation (51%), employee burnout (51%), overreliance on outsourcing (47%), lost business opportunities (46%), and missed deadlines (41%). Nearly a third of companies (32%) say they’ve lost revenue due to inadequate or misaligned staffing.

The good news? Outside of traditional approaches, companies have great alternatives to find and hire talent.

Companies that are solving the AI talent gap are adopting holistic approach to address their hiring needs in 2024, including:

• Building new talent pipelines (53%)
• Upskilling current employees (50%)
• Reducing educational requirements for open roles (53%)
• Increasing their hiring budgets (54%)
• Hiring additional HR staff (52%)

Since 2011, General Assembly has empowered more than 100,000 online learners to launch successful careers in tech. Through strategic partnerships with companies worldwide, the leading AI bootcamp provider has developed scalable talent pipelines and internal upskilling/reskilling programs that effectively address the ongoing supply and demand challenges within the tech sector.

General Assembly recommends the following strategies for business leaders to cope with the compounding changes in the tech landscape:

• Start now. Evaluate your organization's current and future needs to determine necessary headcount, roles, and skill profiles.
• Solve a problem. Begin with a meaningful pilot program that blends external expertise with a staff upskilling initiative to address a specific business challenge.
• Emphasize learning culture. Next, scale your skill sets by fostering continuous education and exploration within the organization, which attracts and retains top talent.
• Embrace nontraditional talent. Adopt skills-first hiring approaches that prioritize competencies over traditional credentials. In America alone, you can build teams from a vast pool of 70 million adults.

A Bold New Approach To Hiring in a Brave New AI Powered World

AI burst onto the international stage like an indie filmmaker whose unexpected masterpiece sweeps the Oscars, captivating audiences worldwide. As organizations across diverse sectors strive to harness the full potential of GenAI, the key lies in adopting a skills-first approach that democratizes access to emerging tools, empowering every employee to streamline processes, uncover insights, and tackle challenges effectively.

For further insights, explore General Assembly's full State of Tech Talent 2024, or reach out directly to the General Assembly team for tailored tech training and talent solutions to propel your business forward.

Gartner dialed back its global IT spending forecast for the year, citing CIO change fatigue as a contributing factor.

The analyst firm trimmed roughly $100 billion from its prior forecast but still expects tech spending to crest at $5 trillion for the first time in 2024, marking a 6.8% increase over 2023’s $4.7 trillion, the report said.

The change fatigue first diagnosed by Gartner in October could metastasize into change resistance this year, the report said, as CIOs contend with the ongoing proliferation of LLM-based tools and generative AI add-ons to existing product suites.

Enterprises trimmed budgets last year, hedging against macroeconomic uncertainty and depressing IT spending growth to a rate of just 3.3%, a smidge higher than 2022’s growth rate.

Weariness played a part, too, according to the report, with CIOs exhibiting lower levels of risk tolerance and demanding greater certainty of technology projects, the report said.

While executives may be resistant to signing new contracts, committing to long-term initiatives and partnering with additional vendors, enterprise modernization marches forward. Of the $12.2 billion Citigroup invested in technology last year, more than half went toward “change-the-bank” initiatives.

“Enterprises continue to find more uses for technology,” John-David Lovelock, distinguished VP analyst at Gartner, said in the report. “Until there is a plateau for how and where technology can be used in an enterprise, there cannot be a plateau in enterprise IT spending.”

As organizations continue to prioritize efficiency gains and optimization projects, investments in IT services will drive overall spending growth, according to Gartner. The category will represent the largest segment of IT spend for the first time by the end of the year, accounting for $1.5 trillion.

The software category, which saw a sharp decline in spending growth last year, will recover, Gartner expects, increasing by 4.6%.

Generative AI, however, won’t have a major impact on IT spending, Lovelock said.

“2024 will be the year when organizations actually invest in planning for how to use GenAI,” said Lovelock. “IT spending will be driven by more traditional forces, such as profitability, labor, and dragged down by a continued wave of change fatigue.”

A dearth of technical skills is preventing businesses from infusing operations with new technologies, according to an Infosys report. The company commissioned a survey of 1,000 senior business leaders for the study.

Nearly three-quarters of surveyed executives said the pace of technological change surpassed their company's capacity to incorporate it into operations.

Pressed to find skills in a tight tech labor market, around 4 in 5 respondents said they prefer employees who are knowledgeable across multiple technology solutions over single-specialty experts.

Organizations must quickly deploy novel technologies to respond to market pressures and customer demands. Despite devoting resources and time to bring projects to fruition, companies often struggle to hire the talent that can bring them to life.

Specific categories are a clear and ongoing struggle for the enterprise. Infosys found a gap of eight percentage points between current skills and availability in roles related to advanced statistical analysis. Respondents reported similar shortfalls in machine and deep learning and cloud computing expertise.

Though recent official data points suggest an uptick in unemployment among IT roles, indicators of demand persist. Organizations added 185,000 new tech job postings last month, bringing the total of open jobs to 436,000, according to CompTIA.

Generative AI represents a top staffing challenge. Training in this specific technology category will be a priority as companies shape their talent strategies, according to Infosys.  

"To close these gaps, leading companies are increasing budgets for upskilling initiatives," the report said. "Hands-on training in AI, ML, advanced analytics and cloud technologies will be critical to ongoing success, and this highlights the need to balance skills required for basic infrastructure, such as the cloud, with those needed for emerging technologies such as AI."

Businesses are eager to reap the efficiency gains, user-experience enhancements and innovation capacity of generative AI technologies. 

But large language models are data-hungry beasts, and their performance correlates directly to the quality of the information they ingest. Achieving enterprise generative AI adoption aspirations now hinges on IT’s ability to rationalize vast data estates, unlock reams of unstructured data and stand up risk-mitigating governance.

In the day-to-day churn of managing IT operations, tech leaders have to take the time to get the data foundation ready for growing AI use — and it's no easy task.

In March, CIO Dive brought together IT executives from the automotive, real estate, fintech and banking industries to explore generative AI implementation and the data operations the technology relies upon. Here are some key takeaways from our conversations.

Cushman & Wakefield's data standardization at work

Global commercial real estate services firm Cushman & Wakefield is working to implement generative AI in marketing and content creation, according to Shawna Cartwright, business information officer and SVP of enterprise technology. But in the last year, Cartwright has focused more broadly on bolstering data operations across finance, legal and other core functions. 

“We've spent a lot of time trying to standardize data across many of these key spaces,” Cartwright said. “But we also have a lot more work to do when it comes to ensuring that our data is clean and ready to go so that we are able to use it as we continue to build on our AI strategies.”

The company is eying previously overlooked unstructured data sources and merging proprietary and external data to create value for customers, she said.

“We’re doing quite a bit of work in the contract space, leveraging AI capabilities from contract solutions to go through our contracts and be able to understand the legal terms and commitments, ensuring that we are on track from a legal perspective to meet all of our legal needs from a contract perspective.”

To manage a geographically dispersed and functionally diverse enterprise, Cushman & Wakefield created several data teams, Cartwright explained.

“I have an enterprise data team, which is actually newly created in the last 12 months, and then we have an advisory and a services data team, as well as a product team that is supporting our deliveries within both data and AI.”

The challenge for Cartwright has been managing the complexities of a sizeable and growing data estate. The business is “working through some of the complexities of having multiple towers and ensuring that we don't silo ourselves and making sure that each of us are educating the other organizations on what is available,” she said.

Intuit pushes access to 'fresh, clean, well understood, correct' data

On Intuit’s path to provide meaningful data insights, it leaned on talent brought in via mergers and acquisitions to promote progress. Alon Amit, VP of product, analytics, AI and data at Intuit, joined the global fintech company five years ago as part of a deal to level up the company’s use of data and AI.

Amit heads a team dedicated to ensuring the data employees need is clean, helpful and ready to use. 

“The whole point is for us to build those mechanisms for data producers and data consumers so that everyone around the company can take charge of their data,” Amit said.

It’s a task that became even more critical as the company worked to make its AI products smarter over the past year with the increased enterprise interest in generative AI.

“This was a seismic shift in our attention to AI, also an acceleration of our journey to empower developers across the company to use AI,” Amit said. “Accessing data — fresh, clean, well understood, correct [data] — all of those things literally expanded by a factor of three to five, almost overnight.”

Two years ago, Amit said the company felt like the overall state of its data was in good shape, but infusing generative AI shifted some of its priorities. 

“Of course, some data is more urgent than other data … [but] our own blog posts were not at the top of that list,” Amit said. “Frankly, now they are to some extent because as we are training language models to speak our language, understand our domain and know the vernacular, our own blog posts are actually a mission critical component of that.”

Banks find potential in unlocking unstructured data

Professional services firm Accenture is assisting organizations on AI initiatives across industries while undertaking its own related transformation. Michael Abbott, senior managing director and global banking lead at Accenture, monitors around 350 different AI projects from banks around the world, which he categorizes in three groups: embedded tools, changed processes and product differentiation. 

Most banks are leveraging generative AI by simply turning to already existing solutions, such as generative AI capabilities in Salesforce or ServiceNow. Others are trying to tackle decades of COBOL.

“Unfortunately many of the banks are still working on code from the 1970s,” Abbott said. “We're now able to put that back into original specifications and … it may very well be the most disruptive thing to banking overall, is when you can get it to the original specifications and you get it built right, generative AI can then forward engineer that code into any language.”

The power to untangle decades of legacy code is an immense draw for banking institutions. But Abbot emphasized the importance of leaning into unstructured data. 

“This is not helping banks where they have structured information because of its risk and underwriting, most likely they’ve taken advantage of that information,” Abbot said. “It’s really all the unstructured problems out there that are being unlocked for the first time.”

American Honda speaks the data language

Data is the common language across American Honda Motor Company, Bob Brizendine, VP of IT at American Honda, said.

But preparing data for AI use cases and keeping a watchful eye on data quality and security has taken on new urgency in the last year.

“We have definitely increased the investment and the pace of our data quality initiatives,” said Brizendine. “We haven't defined a metric for this, but in essence, AI-ready data is what we're striving for.”

The company implemented a five-point adoption plan and, as he put it, gave data a promotion within the company.

Brizendine detailed several focus areas in his group’s data strategy: curation, discovery and literacy.

“When we talk about how to curate data and what are the target areas, our approach is pretty straightforward,” he said.

Unlocking previously untapped sources, what Brizendine noted as the “easiest area,” is a priority.

“These are the areas that deal with new business ventures that we're getting into within the company, or the growing emphasis that we have on sustainability initiatives — areas for which we have few existing sources of data,” Brizendine said.

One key area of data discovery lies in the unstructured realm.

“What used to be the biggest challenge was unstructured data when we were talking about data provisioning and things along that line,” Brizendine said. “But as many are realizing now, some of the best sources of high-quality data for generative AI and specifically the large language models are not found necessarily in your structured data, but they’re in what we would have historically considered to be our unstructured datasets.”

Much of that data, Brizendine noted, is already highly curated and resting in product manuals and other documentation, easing the quality assurance process.

For structured data, Brizendine said there’s no secret sauce. “There's really no magic here,” he said. “We're really prioritizing the most critical areas where we do lack high-quality data as we move toward this AI-ready level for the future.”

The notoriously risk-averse banking sector, now a hive of AI innovation, is better poised than any other industry to reap the benefits of the technology this year, according to Accenture.

Nearly three-quarters of banking work is well-suited to automation, the IT services and consulting firm found, based on an analysis of almost 20,000 tasks across 900 job categories in 19 industries.

Opportunities abound for the sector, according to the report. The firm expects technology to drive gains across functions, including contract generation, software development and risk and compliance management.

As executives work to translate boardroom enthusiasm into scalable business applications, outcomes will hinge on aligning tools to tasks. ROI, a business imperative, will largely depend not just on how well the technology organization prepares for implementation but also on how seamlessly LLMs integrate into existing workflows.

Accenture expects AI tools to generate revenue gains of 6% or more in the next three years through enhancements to customer service functions alone, including wealth advisory, commercial relationships and contact center interactions.

“AI will hit every single part of the bank,” Michael Abbott, senior managing director and global banking lead at Accenture, told CIO Dive. “It’s going to hit legal, HR, enterprise applications, the back office, the front-end user experience, marketing — it changes every single aspect of what you do in banking.”

The banking sector’s proclivity for automation and ML integrations is already well established. Bank of America launched its Erica virtual assistant in 2018, and Wells Fargo’s Google Cloud-based Fargo chatbot began rolling out in 2022.

Banks are ahead of the game, Ryan Martin, partner and AI, automation, data and analytics practice lead at Infosys Consulting, told CIO Dive. “They're early adopters and they're not scared of technology at all,” said Martin.

Financial services companies have also been at the forefront of modeling and predictive analytics but are understandably hesitant to feed sensitive data into a black box, particularly without humans in the loop, according to Martin.

“There's a huge interest in going after new services, but nobody wants to be the guinea pig or the first one to mess it up,” Martin said.

Accelerating modernization

In banking, AI implementation won’t escape human oversight. 

High-value use cases in the near-term will focus on augmentation rather than replacement and take place under the watchful eye of trained professionals, according to Accenture.

“This technology opens up a whole new class of problems to be solved now in a different way that you couldn't solve three years ago,” said Abbott.

Cracking legacy COBOL code at the core of banking systems represents one of the more transformative and scalable solutions.

IBM unveiled the watsonx LLM’s capability to translate COBOL into Java last summer and coding assistants are one of the more prevalent additions to the enterprise IT toolbox.

With IT talent in short supply, the pool of mainframe experts and COBOL programmers has dried up, Abbott said. Banks, a repository for some of the largest stores of legacy code, stand to gain the most from coding assistance, second only to insurance companies, in Accenture’s estimation.

As AI eases the strain of servicing the core, engineers can turn their attention to projects that add value.

“If you free up that time spent servicing technical debt, testing requirements, debugging and just basically keeping the lights on, IT can spend more time architecting the product solution that you want while the technology does the programming testing and delivery,” Abbott said.

Goldman Sachs started piloting an assistant capable of writing 40% of its code in some cases using generative AI, the report noted.

Accenture leveraged GPT-4 and a vector database to develop a similar tool that provides developers with the COBOL technical and functional documentation needed to accelerate modernization. The solution reverse-engineers the legacy code for engineers, who can then use the checked results to reconfigure the application to the same specifications.

As generative AI tools untether the core from outdated code, IT can turn its attention to engineering commercial banking products and services to drive revenue rather than maintenance.

“Let the machine do the work here,” said Abbott. Banks will still need capable engineers, he stressed, but those engineers will no longer be stuck in the cycle time of COBOL.

As last year’s economic pall lifted, enterprises loosened budget constraints — just a bit.

Executives were more optimistic heading into 2024 but still wary, according to a Boston Consulting Group survey of more than 600 global business leaders. Even with elevated hopes, companies remained eager to expand on cost optimization gains, particularly in spending-intensive areas like cloud, where data hungry generative AI capabilities are likely to drive heavier usage.

“Generative AI brought a lot of excitement but also a lot of worry about what it’s going to do to cloud bills, and rightly so,” Rohit Nalgirkar, managing director and partner in BCG’s technology and digital advantage practice, told CIO Dive.

While budget scrutiny cuts across the enterprise, CIOs play a strategic role in optimizing spending, or what BCG refers to as cost transformation. Enterprise technology connects a company’s business processes to its products, Nalgirkar said, creating three primary opportunities for CIOs to reap the benefits of cost transformation:

• Providing the business with the tools to analyze financial and operations data, identify spending pain points and mapping out IT solutions.
• Implementing plans to pare back unnecessary costs, such as overprovisioned cloud resources, and deploy tools, such as generative AI, that can boost efficiency. 
• Leveraging vendor relationships to bring SaaS and other service-level agreements in line with business realities and improving license utilization.

“Technology is so fundamental to business that not involving IT leaders in an organization’s cost transformation from day one is like driving a car that’s low on fuel,” Nalgirkar said in a recent blog post. “You won’t get far.”

Cloud controls

CIOs feel the heat. As enterprise cloud estates grew in size and complexity, controlling spend was the top cloud challenge cited by IT executives in Flexera’s 13th annual State of the Cloud Report, published Monday.

The potential for wins is significant. Companies can reduce tech spend alone by up to 40% through cost management, based on BCG’s analysis of more than 3,000 companies over a five-year period

“Improving IT cost management can reduce technology spending anywhere from 15% to 40%, depending on the situation,” Nalgirkar said.

Cloud’s usage-based pricing model can be an ally or an enemy in the battle to control IT spend.

The hyperscalers provide customers with usage optimization tools, tiered storage options and built-in discount opportunities, but many organizations fail to take advantage of these perks.

More than half of the largest cloud provider’s customers paid on-demand rates for compute last year, according to ProsperOps’ analysis of $1.5 billion in AWS bills. The FinOps solution provider estimated customers could save an average of 30 cents of every dollar spent on cloud compute.

The move toward FinOps, a cloud cost optimization framework adopted by more than half of respondents to the Flexera survey, is a gamechanger, but only with sustained effort.

“The real value of FinOps is in continued governance,” Nalgirkar said. “The key is, it’s not a one-and-done thing.”