In finance, portfolio management involves the strategic selection of a collection of investments that align with an investor’s financial goals and risk tolerance. 

This approach can also apply to IT’s portfolio of systems, with one addition: IT must also assess each asset in that portfolio for operational performance.

Today’s IT is a mix of legacy, cloud-based, and emerging or leading-edge systems, such as AI. Each category contains mission-critical assets, but not every system performs equally well when it comes to delivering business, financial, and risk avoidance value to the enterprise. How can CIOs optimize their IT portfolio performance?

Here are five evaluative criteria for maximizing the value of your IT portfolio.

Mission-critical assets

The enterprise’s most critical systems for conducting day-to-day business are a category unto themselves. These systems may be readily apparent, or hidden deep in a technical stack. So all assets should be evaluated as to how mission-critical they are.

For example, it might be that your ERP solution is a 24/7 “must have” system because it interfaces with a global supply chain that operates around the clock and drives most company business. On the other hand, an HR application or a marketing analytics system could probably be down for a day with work-arounds by staff.

More granularly, the same type of analysis needs to be performed on IT servers, networks and storage. Which resources do you absolutely have to have, and which can you do without, if only temporarily?

As IT identifies these mission-critical assets, it should also review the list with end-users and management to assure mutual agreement.

Asset utilization

Zylo, which manages SaaS inventory, licenses, and renewals, estimates that “53% of SaaS licenses go unused or underused on average, so finding dormant software should be a priority.” This “shelfware” problem isn’t only with SaaS; it can be found in underutilized legacy and modern systems, in obsolete servers and disk drives, and in network technologies that aren’t being used but are still being paid for.

Shelfware in all forms exists because IT is too busy with projects to stop for inventory and obsolescence checks. Consequently, old stuff gets set on the shelf and auto-renews.

The shelfware issue should be solved if IT portfolios are to be maximized for performance and profitability. If IT can’t spare the time for a shelfware evaluation, it can bring in a consultant to perform an assessment of asset use and to flag never-used or seldom-used assets for repurposing or elimination.

Asset risk

The goal of an IT portfolio is to contain assets that are presently relevant and will continue to be relevant well into the future. Consequently, asset risk should be evaluated for each IT resource.

Is the resource at risk for vendor sunsetting or obsolescence? Is the vendor itself unstable? Does IT have the on-staff resources to continue running a given system, no matter how good it is (a custom legacy system written in COBOL and Assembler, for example)? Is a particular system or piece of hardware becoming too expense to run? Do existing IT resources have a clear path to integration with the new technologies that will populate IT in the future?

For IT assets that are found to be at risk, strategies should be enacted to either get them out of “risk” mode, or to replace them.

Asset IP value

There is a CIO I know in the hospitality industry who boasts that his hotel reservation program, and the mainframe it runs on, have not gone down in 30 years. He attributes much of this success to custom code and a specialized operating system that the company uses, and he and his management view it as a strategic advantage over the competition.

He is not the only CIO who feels this way. There are many companies that operate with their “own IT special sauce” that makes their businesses better. This special sauce could be a legacy system or an AI algorithm. Assets like these that become IT intellectual property (IP) present a case for preservation in the IT portfolio.

Asset TCO and ROI

Is every IT asset pulling its weight? Like monetary and stock investments, technologies under management must show they are continuing to produce measurable and sustainable value. The primary indicators of asset value that IT uses are total cost of ownership (TCO) and return on investment (ROI).

TCO is what gauges the value of an asset over time. For instance, investments in new servers for the data center might have paid off four years ago, but now the data center has an aging bay of servers with obsolete technology and it is cheaper to relocate compute to the cloud.

ROI is used when new technology is acquired. Metrics are set that define at what point the initial investment into the technology will be recouped. Once the breakeven point has been reached, ROI continues to be measured because the company wants to see new profitability and/or savings materialize from the investment. Unfortunately, not all technology investments go as planned. Sometimes the initial business case that called for the technology changes or unforeseen complications arise that turn the investment into a loss leader.

In both cases, whether the issue is TCO or ROI, the IT portfolio must be maintained in a way such that losing or wasted assets are removed.

Summing it up

IT portfolio management is an important part of what CIOs should be doing on an ongoing basis, but all too often, it is approached in a reactionary mode — for example, with a system being replaced only when users ask for it to be replaced, or a server needing to be removed from the data center because it fails.

The CEO, the CFO, and other key stakeholders whom the CIO deals with during technology budgeting time don’t help, either. While they will be interested in how long it will take for a new technology acquisition to “pay for itself,” no one ever asks the CIO about the big picture of IT portfolio management: how the overall assets in the IT portfolio are performing, and which assets will require replacement for the portfolio to sustain or improve company value.

To improve their own IT management, CIOs should seize the portfolio management opportunity. They can do this by establishing a portfolio for their company’s IT assets and reviewing these assets periodically with those in the enterprise who have direct say over IT budgets.

IT portfolio management will resonate with the CFO and CEO because both continually work with financial and risk portfolios for the business. Broader visibility of the IT portfolio will also make it easier for CIOs to present new technology recommendations and to obtain approvals for replacing or upgrading existing assets when these actions are called for.

See also:

• Should CIOs rethink the IT roadmap?
• Innovative financing techniques can boost your IT budget
• Digital transformation works best when co-owned — but only if you do it right

기업의 AI 프로젝트가 기대에 못 미쳤다는 평가가 잇따르고 있다. 지금까지 나온 연구 대부분이 같은 결론을 내린다. MIT는 기업의 생성형 AI 프로젝트 중 95%가 실패했다고 분석하기도 했다. 여기서 ‘실패’는 6개월 안에 측정 가능한 재무적 수익을 보여주지 못한 경우를 뜻한다.

문제는 이제 ‘성과 부진’을 눈감아주는 시기가 지났다는 점이다. CEO와 이사회, 투자자들은 AI 투자에서 눈에 보이는 ROI를 요구하고 있다. IT 서비스 기업 킨드릴(Kyndryl)의 보고서에 따르면, 설문에 참여한 3,700명의 고위 경영진·의사결정자 중 61%는 “1년 전보다 지금 AI 투자 ROI를 입증해야 한다는 압박이 더 커졌다”고 답했다. 글로벌 CEO 자문사 테네오(Teneo)의 설문 조사에서도 “AI가 과대광고에서 실행으로 무게 중심이 이동하면서, AI 지출의 ROI를 보여달라는 압박이 커지고 있다”며, 비슷한 흐름을 확인했다. 보고서는 투자자 53%가 ‘6개월 이내의 흑자 ROI’를 기대한다고 밝혔다.

IBM 컨설팅의 글로벌 매니징 파트너 닐 다르는 “CEO와 CIO에게 수익을 내라는 압박이 지속되고, 결국 ‘AI로 회사를 어떻게 더 나아지게 만들 것인가’라는 질문으로 수렴한다”고 설명했다.

성공의 초석은 가치 중심 AI

보험사 뉴욕라이프(New York Life) 산하 뉴욕라이프 그룹 베네핏 솔루션즈의 CIO 맷 마즈는 2026년에도 AI ROI를 낼 수 있다고 자신했다. 핵심은 ‘예상 가치’를 기준으로 배포 우선순위를 정하는 방식이다. 마즈는 “2023년 12월 CEO의 촉구로 AI 여정을 시작했고, 출발점부터 고객·파트너·직원 경험을 끌어올릴 기술·데이터·AI 기업이 되자는 목표를 세웠다. 그래서 처음부터 가치와 ROI가 최우선 기준이었다”라고 밝혔다.

AI 투자 판단도 다른 투자와 동일한 잣대를 적용한다. 운영비 절감, 마진 개선, 매출 성장, 고객 만족, 고객 유지 등 다양한 지표를 보되, 최종적으로는 ‘이익 기여’로 귀결된다는 설명이다. 마즈는 “우리는 손익을 기준으로 투자를 집행하기 때문에 지출에 민감하다”라며, “민첩하게 움직이되, 돈을 낭비하지 않는 것이 중요하다”라고 강조했다.

ROI 중점 전략을 추진하기 위한 실무 방식도 소개했다. 데이터·시스템·역량 측면에서 AI에 대한 준비가 된, 이른바 ‘AI Ready’  영역을 우선 공략하고, 여기서 나온 수익으로 다음 프로젝트를 재투자한다. 또한 AI 시스템을 재사용 가능하게 설계해 이후 프로젝트가 더 빨리 출발하도록 만든다. 마즈는 “막연한 개선을 기대하는 것이 아니라, 현실적인 ROI를 낼 수 있는 과제를 고르기 위한 전략적 접근”이라고 설명했다.

여기에 지속적인 IT 현대화도 중요한 기반으로 꼽았다. 마즈는 “기반을 먼저 구축했기 때문에 AI를 활용할 좋은 위치에 섰다”라며, “AI로 ROI를 내려면 전략적 데이터 관리, 컴퓨팅·애플리케이션 현대화, 클라우드 네이티브 솔루션이 필요하다”라고 말했다. 향후에는 AI로 업무를 재구성하고 핵심 프로세스에 ‘에이전틱 솔루션’을 본격적으로 도입하겠다는 구상도 내놨다. 다만 투자안에 따라 회수 기간은 수년이 걸릴 수 있다고 덧붙였다.

“ROI가 필요하다” 성과 측정 기준도 재정의

하지만, 많은 기업이 ROI가 빨리 나오지 않을 가능성이 더 크다고 생각한다. 테네오의 보고서에 따르면, CEO 84%는 “새 AI 이니셔티브의 수익이 나오는 데 6개월보다 더 오래 걸릴 것”이라고 예상했다.

컨설팅 기업 웨스트몬로(West Monroe)의 CAIO 브렛 그린스타인은 “초기 AI 프로젝트 상당수가 비즈니스와 무관한 실험·학습에 가까웠고, 조직의 목표를 해결하지 못해 없어졌다”라고 진단했다. 실제 과제를 겨냥했더라도 확장에 필요한 데이터·기술 기반이 없거나, 현대화 비용이 예상 ROI보다 커서 무산되는 경우가 많았다는 분석이다. 성과가 있어도 정량화가 어렵거나 ‘조직의 판도를 바꾸기엔’ 너무 작은 개선에 그치기도 했다.

그린스타인은 “웹과 모바일 초기에도 비슷한 일이 있었다. 중요한 지표가 무엇인지 새로 찾아내는 데는 시간이 걸린다”라고 말했다. 다만 챗GPT 등장 이후 3년이 지나며 기업의 이해도가 성숙해졌다고 평가했다. 그린스타인은 “지금은 고객들이 AI의 혁신 가치를 이해하고 ‘새로운 업무 방식’으로 보는 3번째 변화의 물결 속에 있다”라며, “ROI가 나오는 곳은 AI를 혁신으로 보고, 현업과 함께 업무 방식을 다시 설계한다. ROI를 얻으려면 변혁 작업이 필요하다는 걸 아는 조직”이라고 분석했다.

팔로알토 네트웍스의 CIO 미라 라자벨은 AI 과제를 고를 때 ‘속도’, ‘효율’, ‘경험’ 세 가지를 기준으로 삼는다고 밝혔다. 라자벨은 “속도가 핵심”이라며, “더 적은 자원으로 더 많은 일을 할 수 있는가, 경험을 얼마나 개선하는가가 판단 기준”이라고 말했다.

라자벨은 이를 토대로 IT 운영의 90%를 AI로 자동화하는 프로젝트를 추진 중이라고 소개했다. 해당 프로젝트는 2024년 초 자동화 비율 12%에서 2025년 말 기준 75%로 높아졌고, 그 과정에서 IT 운영 비용을 절반으로 줄였다고 전했다.

지표와 목표 수립이 먼저

다르는 AI ROI가 잡히지 않았던 이유로 ‘전략 부재’를 지목했다. 다르는 “어디에 AI를 적용할지 체계적으로 묻기보다, ‘일단 해보자’며 무작정 확산시키는 경우가 있었다”고 지적했다.

하지만 점점 더 많은 최고 경영진이 AI를 ‘대대적 혁신’ 수단이자 자사 비즈니스를 극적으로 전환할 기술로 보기 시작했다. 다르는 “이런 기업은 기능별로 재창조하고, 더 좋고 강하고 저렴하게 혁신하고, 경우에 따라 매출도 성장하고 있다. 2년 전에는 실험과 PoC가 많았지만, 지금은 혁신의 단계로 넘어왔고 성숙한 조직은 12개월 내에 수익을 낼 것을 요구한다”라고 설명했다.

애플 기기 관리 소프트웨어 기업 잼프(Jamf)의 CIO 린 램도 관점 변화를 언급했다. 램은 “그동안은 문제점을 해결하기 위해 AI를 적용했지만, 이제는 일하는 방식 자체를 다시 생각할 목적으로 AI를 사용한다”라며, “진짜 큰 가치는 그 지점에서 나온다”라고 강조했다.

현재 잼프가 우선시하는 AI 이니셔티브도 분명 이렇다. 램은 “2년 전에는 ‘일단 해보자’가 통했지만, 지금은 그 단계를 넘었다. 누군가 잠재 가치의 근거가 없는 제안을 하면, 반대할 것이다. 지금은 이해관계자의 목표를 확인하고, 성과를 측정할 지표를 먼저 세운다”라고 밝혔다. 이어 “AI와 AI 에이전트로 할 수 있는 일의 가능성은 거의 무한해 보이지만, 결국 우리는 비즈니스를 운영한다. 논리적이고 영리하게 의사결정을 해야 한다”라고 덧붙였다.

IT 과제를 AI 혁신의 선순환 자원으로 전환

물론 ROI에 초점을 맞춘 혁신에도 현실적 장애물은 남아 있다. IT 서비스 기업 타타 컨설팅 서비스(Tata Consultancy Services, TCS) 북미 AI·기술 혁신 사업부를 이끄는 제니퍼 페르난데스는 “레거시 기술, 프로세스 부채, 데이터 부채가 AI 확장을 막고 있다”라고 지적하며, “이 부채를 갚지 않으면 AI 야심을 키우기도, 의미 있는 성과를 내기도 어렵다”라고 강조했다.

시스코의 ‘AI 준비도 지표’도 같은 문제를 보여준다. 조사 대상 중 IT 인프라가 ‘AI 대한 준비가 되어 있다”라고 답한 조직은 32%에 그쳤고, 데이터 준비도는 34%, 거버넌스 프로세스 준비도는 23%로 더 낮았다.

페르난데스는 ‘부채를 전략적으로 처리하되, AI로 그 부채를 갚는 방식’을 권했다. AI로 IT를 현대화하면 운영 효율이 올라가고, 더 많은 AI 사용례를 지원할 역량도 함께 커지며, 데이터 계층의 결함도 보완할 수 있다는 것. 이런 개선을 통해 절감한 비용과 역량을 다시 다른 AI 프로젝트에 재투자하면, 현대화 효과 덕분에 후속 프로젝트의 ROI 가능성도 높아진다는 설명이다.

페르난데스는 “최적화를 통해 부채를 줄이고, 단계적으로 구축하면서 변혁을 이어갈 수 있다. CIO가 ‘고쳐야 하니 돈을 달라’고 말하는 대신 ‘AI를 도입해 수익을 만들고, 그 수익으로 다른 혁신에 재투자하겠다’고 말할 수 있다. CIO가 AI를 위한 재원을 만들어주는 셈”이라고 말했다.
dl-ciokorea@foundryco.com

Everything is changing, from data pipelines and technology platforms, to vendor selection and employee training — even core business processes — and CIOs are in the middle of it to guide their companies into the future.

In 2024, tech leaders asked themselves if this AI thing even works and how do you do it. Last year, the big question was what the best use cases are for the new technology. This year will be all about scaling up and starting to use AI to fundamentally transform how employees, business units, or even entire companies actually function.

So whatever IT was thought of before, it’s now a driver of restructuring. Here are seven ways the CIO role will change in the next 12 months.

Enough experimenting

The role of the CIO will change for the better in 2026, says Eric Johnson, CIO at incident management company PagerDuty, with a lot of business benefit and opportunity in AI.

“It’s like having a mine of very valuable minerals and gold, and you’re not quite sure how to extract it and get full value out of it,” he says. Now, he and his peers are being asked to do just that: move out of experimentation and into extraction.

“We’re being asked to take everything we’ve learned over the past couple of years and find meaningful value with AI,” he says.

What makes this extra challenging is the pace of change is so much faster now than before.

“What generative AI was 12 months ago is completely different to what it is today,” he says. “And the business folks watching that transformation occur are starting to hear of use cases they never heard of months ago.”

From IT manager to business strategist

The traditional role of a company’s IT department has been to provide technology support to other business units.

“You tell me what the requirements are, and I’ll build you your thing,” says Marcus Murph, partner and head of technology consulting at KPMG US.

But the role is changing from back-office order taker to full business partner working alongside business leaders to leverage innovation.

“My instincts tell me that for at least the next decade, we’ll see such drastic change in technology that they won’t go back to the back office,” he says. “We’re probably in the most rapid hyper cycle of change at least since the internet or mobile phones, but almost certainly more than that.”

Change management

As AI transforms how people do their jobs, CIOs will be expected to step up and help lead the effort.

“A lot of the conversations are about implementing AI solutions, how to make solutions work, and how they add value,” says Ryan Downing, VP and CIO of enterprise business solutions at Principal Financial Group. “But the reality is with the transformation AI is bringing into the workplace right now, there’s a fundamental change in how everyone will be working.”

This transformation will challenge everyone, he says, in terms of roles, value proposition of what’s been done for years, and expertise.

“The technology we’re starting to bring into the workplace is really shaping the future of work, and we need to be agents of change beyond the tech,” he says.

That change management starts within the IT organization itself, adds Matt Kropp, MD and senior partner and CTO at Boston Consulting Group.

“There’s quite a lot of focus on AI for software development because it’s maybe the most advanced, and the tools have been around for a while,” he says. “There’s a very clear impact using AI agents for software developers.”

The lessons that CIOs learn from managing this transformation can be applied in other business units, too, he says.

“What we see happening with AI for software development is a canary in the coal mine,” he adds. And it’s an opportunity to ensure the company is getting the productivity gains it’s looking for, but also to create change management systems that can be used in other parts of the enterprise. And it starts with the CIO.

“You want the top of the organization saying they expect everyone to use AI because they use it, and can demonstrate how they use it as part of their work,” he says. Leaders need to lead by example that the use of AI is allowed, accepted, and expected.

CIOs and other executives can use AI to create first drafts of memos, organize meeting notes, and help them think through strategy. And any major technology initiative will include a change management component, yet few technologies have had as dramatic an impact on work as AI is having, and is expected to have.

Deploying AI at scale in an enterprise, however, is a very contentious issue, says Ari Lightman, a professor at Carnegie Mellon University. Companies have spent a lot of time focusing on understanding the customer experience, he says, but few focus on the employee experience.

“When you roll out enterprise-wide AI systems, you’re going to have people who are supportive and interested, and people who just want to blow it up,” he says. Without addressing the issues that employees have, AI projects can grind to a halt.

Cleaning up the data

As AI projects scale up, so will their data requirements. Instead of limited, curated data sets, enterprises will need to modernize their data stacks if they haven’t already, and make the data ready and accessible for AI systems while ensuring security and compliance.

“We’re thinking about data foundations and making sure we have the infrastructure in place so AI is something we can leverage and get value out of,” says Aaron Rucker, VP of data at Warner Music.

The security aspect is particularly important as AI agents gain the ability to autonomously seek out and query data sources. This was much less of a concern with small pilot projects or RAG embedding, where developers carefully curated the data that was used to augment AI prompts. And before gen AI, data scientists, analysts, and data engineers were the ones accessing data, which offered a layer of human control that might diminish or completely vanish in the agentic age. That means the controls will need to move closer to the data itself.

“With AI, sometimes you want to move fast, but you still want to make sure you’re setting up data sources with proper permissions so someone can’t just type in a chatbot and get all the family jewels,” says Rucker.

Make build vs buy decisions

This year, the build or buy decisions for AI will have dramatically bigger impacts than they did before. In many cases, vendors can build AI systems better, quicker, and cheaper than a company can do it themselves. And if a better option comes along, switching is a lot easier than when you’ve built something internally from scratch. On the other hand, some business processes represent core business value and competitive advantage, says Rucker.

“HR isn’t a competitive advantage for us because Workday is going to be better positioned to build something that’s compliant” he says. “It wouldn’t make sense for us to build that.”

But then there are areas where Warner Music can gain a strategic advantage, he says, and it’s going to be important to figure out what this advantage is going to be when it comes to AI.

“We shouldn’t be doing AI for AI’s sake,” says Rucker. “We should attach it to some business value as a reflection of our company strategy.”

If a company uses outside vendors for important business processes, there’s a risk the vendor will come to understand an industry better than the existing players.

Digitizing a business process creates behavioral capital, network capital, and cognitive capital, says John Sviokla, executive fellow at the Harvard Business School and co-founder of GAI Insights. It unlocks something that used to be exclusively inside the minds of employees.

Companies have already traded their behavioral capital to Google and Facebook, and network capital to Facebook and LinkedIn.

“Trading your cognitive capital for cheap inference or cheap access to technology is a very bad idea,” says Sviokla. Even if the AI company or hyperscaler isn’t currently in a particular line of business, this gives them the starter kit to understand that business. “Once they see a massive opportunity, they can put billions of dollars behind it,” he says.

Platform selection

As AI moves from one-off POCs and pilot projects to deployments at scale, companies will have to come to grips with choosing an AI platform, or platforms.

“With things changing so fast, we still don’t know who’s going to be the leaders in the long term,” says Principal’s Downing. “We’re going to start making some meaningful bets, but I don’t think the industry is at the point where we pick one and say that’s going to be it.”

The key is to pick platforms that have the ability to scale, but are decoupled, he says, so enterprises can pivot quickly, but still get business value. “Right now, I’m prioritizing flexibility,” he says.

Bret Greenstein, chief AI officer at management consulting firm West Monroe Partners, recommends CIOs identify aspects of AI that are stable, and those that change rapidly, and make their platform selections accordingly.

“Keep your AI close to the cloud because the cloud is going to be stable,” he says. “But the AI agent frameworks will change in six months, so build to be agnostic in order to integrate with any agent frameworks.”

Progressive CIOs are building the enterprise infrastructure of tomorrow and have to be thoughtful and deliberate, he adds, especially around building governance models.

Revenue generation

AI is poised to massively transform business models across every industry. This is a threat to many companies, but also an opportunity for others. By helping to create new AI-powered products and services, CIOs can make IT a revenue generator instead of just a cost center.

“You’re going to see this notion of most IT organizations directly building tech products that enable value in the marketplace, and change how you do manufacturing, provide services, and how you sell a product in a store,” says KPMG’s Murph.

That puts IT much closer to the customer than it had been before, raising its profile and significance in the organization, he says.

“In the past, IT was one level away from the customer,” he says. “They enabled the technology to help business functions sell products and services. Now with AI, CIOs and IT build the products, because everything is enabled by technology. They go from the notion of being services-oriented to product-oriented.”

One CIO already doing this is Amith Nair at Vituity, a national physician group serving 13.8 million patients.

“We’re building products internally and providing them back to the hospital system, and to external customers,” he says.

For example, doctors spend hours a day transcribing conversations with patients, which is something AI can help with. “When a patient comes in, they can just have a conversation,” he says. “Instead of looking at the computer and typing, they look at and listen to the patient. Then all of their charting, medical decision processes, and discharge summaries are developed using a multi-agent AI platform.”

The tool was developed in-house, custom-built on top of the Microsoft Azure platform, and is now a startup running on its own, he says.

“We’ve become a revenue generator,” he says.