For technology leaders, the biggest challenge in AI adoption is deciding where to invest. As vendors roll out new tools and boards push for rapid adoption, CIOs often end up deciding to invest in AI without a clear picture of the long-term benefits.
According to Gartner, global AI spending is expected to reach: $2.5 trillion in 2026highlighting the scale of capital flowing into AI efforts. But despite this surge, Gartner found at least the following: 50% of generative AI projects then abandoned proof of conceptOften due to unclear business value, data preparation issues, or rising costs. The result is wasted spending, fragmented systems, and increased pressure on CIOs to demonstrate measurable impact from their AI investments.
Given these risks, the most successful CIOs with AI are not the ones who rush to deploy tools first. Instead, it’s up to them to establish a clear decision framework upfront, before vendor pitches even begin. To help build that framework, here are five strategic questions that should guide every CIO’s AI investment decisions.
1. What The problem is that business is challenging How to solve it?
most Successful AI initiatives Start with clearly defined business outcomes, not technology mandates. However, many organizations reverse this process and deploy AI first, then look for use cases.
Brandon Sammut, chief human resources and AI transformation officer at Zapier, a software company that provides business and workflow automation, says organizations often make the mistake of starting with technology rather than the business problem they’re trying to solve. “When we took the lead on ‘how do we leverage AI?’ the result was an impressive demo that no one was actually using in a production environment,” he added.
The first question is: “What problem are you trying to solve?”
Gabriela Cubeiro, Senior Vice President of Products, 8 a.m.
Gabriela Cubeiro, senior vice president of products at 8am, a software company that provides integrated workflow, payment, and management tools, shared a similar view, stressing that AI efforts need to start with clear business intent, not experimentation. She argued that instead of focusing on what AI can do, leaders must first define the outcome. “The first question is: What problem are we trying to solve?” she said.
Therefore, CIOs should encourage their teams to clearly define the operational or financial problem they are trying to address. For example, a statement like “Using AI to improve customer service” is too vague to guide investment decisions. A more specific goal, such as reducing average call resolution time by 20%, gives organizations a measurable goal and a more realistic way to evaluate whether AI is the right option.
In some cases, simpler process changes or traditional automation can achieve the same results with less complexity and risk, reinforcing the need to consider whether AI is really needed. Defining your business problem upfront ensures that your AI investments are tied to real value, rather than being an experiment for its own sake.
2. It is data Is your foundation ready to support AI?
The reliability of an AI system is determined by the data used to train it, but many organizations overestimate the readiness of their data. When data is incomplete, inconsistent, or poorly managed, the output can be inaccurate, biased, or difficult to explain.
Before moving forward with AI initiatives, CIOs need to start with the basics. You should ask whether relevant data is accessible across systems or locked in silos, is it properly structured and labeled to support the intended use cases, and whether clear governance policies define how the data is used. Privacy requirements, regulatory obligations, and data ownership must also be clearly understood.
strong data Governance is especially important When AI impacts business decisions. Sumit Johar, CIO of cloud-based fintech company BlackLine, explained that if AI systems are to influence decisions that impact revenue, compliance, and customer trust, the data fed to them must be accurate, timely, and auditable. “Without disciplined data management, AI will only amplify the noise,” he added.
Making AI work across CRMs, dashboards, support systems, and data layers is a real challenge.
Brandon Sammut, Head of HR and AI Transformation, Zapier
Beyond governance, organizations also need to consider how AI tools connect to the broader technology environment. Integration challenges often reveal additional data gaps. Zapier’s Sammut pointed out that while building or purchasing an AI tool may seem easy, the real complexity lies in integrating it across existing enterprise systems, which many teams underestimate until implementation begins.
“Making AI work across CRMs, dashboards, support systems and data layers is a real challenge,” he said. “Most teams don’t plan until it’s too late.”
Organizations don’t need perfect data to start experimenting with AI, but they do need an honest assessment of gaps in data quality and governance. Many AI efforts stall not because the models fail, but because the underlying data environment is not strong enough to support it at scale.
3. Can organizations sustain this system in the long term?
AI is not something that can be implemented once and done. Models must be monitored, retrained, and adjusted as conditions change. This is a concept known as a “model.” model drift. Without continuous monitoring, a system that initially works well can lose accuracy and relevance over time.
CIOs need to think beyond the implementation date and consider whether their organization is equipped to support the system over the long term. The main aspects to consider are: Who will monitor the performance of the model? Who will retrain it if data patterns change? What happens if the relationship with the vendor ends or the project’s internal champions leave?
Another important aspect of sustainability is architectural discipline. As AI tools proliferate, organizations run the risk of fragmented adoption. BlackLine’s Johar explained this pattern: AI sprawlcompanies accumulate multiple AI tools without a clear long-term strategy.
“CIOs are constantly under pressure to introduce new AI capabilities, but they need to evaluate which tools really fit the bill over the long term,” he said. Without intentional oversight, organizations can end up with duplicate investments, redundant tools and systems that are difficult to manage at scale.
To avoid that outcome, intentionally Deciding whether to build or buy. Johar emphasized that organizations should develop AI systems in-house only if it strengthens the company’s core competitive advantage. For everything else, he said, it often makes more sense to purchase tools and keep them flexible so that organizations can adapt as the AI environment evolves.
This disciplined approach allows AI efforts to remain adaptable rather than locked into a rigid architecture. Organizations without an ongoing management plan often find themselves overly dependent on vendors and management systems they don’t fully understand.
Long-term success depends not only on strong governance and performance monitoring, but also on clear ownership, flexibility, and alignment with business priorities. Without that foundation, even well-designed AI systems can become difficult to maintain over time.
4. How will success be measured?
AI efforts often run into difficulties because success is defined too loosely. Goals like “increase efficiency” and “drive innovation” may sound appealing, but it can be difficult to determine whether your investments are paying off. Instead, CIOs need to establish clear metrics and timelines before implementation. These may include reducing operating costs, shortening decision-making cycles, increasing customer satisfaction, or measurably increasing productivity.
8am’s Cubeiro emphasized that it’s not just financial gain that measures value. He pointed to adoption and responsible use as key indicators of early success, explaining that strong engagement with AI tools shows teams understand how to properly apply them.
CIOs must directly tie results to business priorities such as profitability, efficiency, and customer experience.
Sumit Johar, CIO, BlackLine
BlackLine’s Johar also cautioned against relying solely on usage metrics, as high adoption rates don’t necessarily translate to business impact. Instead, clear alignment with corporate strategy is essential. “CIOs need to tie results directly to business priorities such as profitability, efficiency and customer experience,” he said.
For example, if improving the customer experience is your top goal, success may be reflected in stronger service level agreements or higher customer satisfaction scores. When profitability is a priority, value can come in the form of cost savings and operational efficiencies.
It is equally important to consider how the value created by AI will be used. For example, when automation frees up hundreds of employee hours each month, that time must be redirected to higher-value work such as innovation, analytics, and customer engagement to produce meaningful results. ROI.
Clear metrics and regular review points help organizations track progress, adjust strategy, and decide whether to expand, pivot, or eliminate efforts.
5. What risks does AI pose? Who is responsible for those risks?
AI projects come with a different risk profile than most traditional IT initiatives, impacting everything from regulatory compliance and operational reliability to algorithmic bias and reputational impact.
As AI regulations evolve, organizations may need to: Meet requirements for transparency and explainabilityBias testing and automated decision-making documentation. Therefore, CIOs should engage legal, compliance, and risk leaders early in the process, rather than after implementation.
These risks are becoming increasingly visible in practice. Cubeiro at 8 a.m. noted growing concerns. AI illusionEspecially in the legal field, fabricated citations and unsubstantiated claims lead to sanctions, reputational, and financial damage. “When AI generates content that is counterfactual or unsupported, it poses significant compliance and operational risks,” he said, stressing the need for validation and governance controls before widespread adoption.
To manage these risks more effectively, BlackLine’s Johar proposed a structured monitoring mechanism. “Organizations can benefit from establishing two governance councils,” he explained. “One focused on risk by bringing together legal, security, and privacy leaders, and the other focused on transformation to assess whether new AI capabilities align with business goals or overlap with existing tools.” Such oversight can help prevent piecemeal efforts and duplicative investments, especially as new AI tools enter the market rapidly.
Beyond structure, clear accountability is essential. If an AI system produces harmful or inaccurate results, organizations need to know who is responsible for monitoring and escalating. Many companies are addressing this issue through formal governance structures such as cross-functional AI review boards and ethics committees.
Sammut from Zapier emphasized the need to build governance into AI programs from the beginning. “You can delegate some of the work to AI, but you can’t delegate responsibility,” he said.
Without clear ownership and oversight, even well-intentioned AI projects can create risks that are difficult to contain once the system is in production.
Kinza Yasar is a technical writer in Informa TechTarget’s AI and Emerging Technologies group with experience in computer networking.
