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Artificial Intelligence
From blind spots to business value: How executives can build AI initiatives that succeed
Amid widespread predictions of the bursting of the AI bubble, José Parra Moyano demonstrates how leaders can focus on optimizing initiatives with strong value, data readiness, and employee endorsement.
Depending on who you believe, artificial intelligence (AI) is either the most significant business growth accelerator of our lifetimes or a bubble about to burst. The stock market broke multiple records in 2025, with share prices of some AI companies rising dramatically in a short span of time. Nvidia’s value jumped to more than $5tn in the face of investor enthusiasm for the AI boom.
A survey published in Harvard Business Review in January 2026 revealed that 99% of executives see investments in data and AI as a top organizational priority. But while most organizations are using AI, the majority remain in the very early stages of scaling and of capturing business value, as demonstrated in McKinsey’s The State of AI: Global Survey 2025.
Despite enthusiasm and interest, skepticism of the value of AI initiatives is widespread. This is because as many as four in five AI projects fail.
Building for success
To benefit from the potential of AI, leaders must ensure that any projects align with overarching business goals. To ensure this, organizations should focus on three fundamental dimensions of AI adoption: business value, data, and people. This three-part prioritization forms a framework for addressing challenges.
Keep these three questions top of mind:
- What value do we aim to create with AI?
- Do we have access to the necessary data?
- How will people – employees and other stakeholders perceive the changes, and how can we help them to adapt?
Guided by these questions, leaders can strategically prioritize resources, mitigate risks, and increase the likelihood of long-term adoption and ROI.
Successful implementations focus on solving specific, measurable challenges.
1 – Value: Define the business case
Start by clearly articulating the value AI will bring to the organization. While this may seem obvious, many struggle to provide a straightforward definition of the problem they intend to solve with an AI-based system.
Successful implementations focus on solving specific, measurable challenges. Try to answer: What is giving me/my customers/my team a headache? And define how much you would pay to solve this problem. For example, a CEO frustrated by high customer churn has implemented AI: a combination of predictive random forests (a machine learning algorithm that combines the output of multiple decision trees to reach a single result) and large language models (LLMs) to analyze unstructured textual data. This predicts at-risk accounts 60 days before cancellation, enabling proactive retention efforts that reduce churn by half in her company.
Companies that thrive with AI adoption tend to take a focused, pragmatic approach. However, in solving specific, tangible problems, the successful initiative will also generate broader knowledge within the organization. This may encourage the cultural evolution needed to engender a broader organizational transformation, seeing the group use AI to address larger, more complex challenges.
“Data collaboration platforms enable organizations to train AI models while safeguarding privacy.”
2 – Data: Design for access, quality, and effectiveness
The second dimension of successful AI adoption focuses on data. Commentators often warn of the pitfalls here with the mantra “garbage in, garbage out.” AI’s effectiveness depends on the quality and accessibility of data, yet organizations often lack the volume, diversity, or structure required for effective AI training. The key is to look at data in terms of access rather than just ownership. You may “own” data but cannot use it because of a lack of consent. But there is also data you do not own but can access.
Data collaboration platforms enable organizations to train AI models while safeguarding privacy. These systems work by sending algorithms to where the data is stored, rather than moving data into the organization to train the tool. This ensures personal information remains securely stored at its source without impeding analysis.
Such platforms range from proprietary services offered by private companies to open-source solutions used by organizations or consortia. The widespread use of such platforms underlines the growing recognition of the value of securely tapping into shared or sensitive data. Importantly, these tools can address a critical challenge for AI development: the lack of high-quality training data. For instance, hospitals and pharmaceutical companies can collectively train algorithms to support enhanced diagnostics or treatments without sharing raw data.
In more complex B2B environments, where regulations or privacy concerns prevent companies from using customer data to train AI models, these platforms allow firms to train algorithms while being impeccable in respecting privacy and facilitating compliance and innovation.
By maintaining privacy while enabling insights, data collaboration platforms are unlocking new possibilities across industries, from healthcare to autonomous vehicles, while navigating the growing complexities of data regulation.
Be aware, though, that just because you can measure it doesn’t mean that you need to. As the management expert Peter Drucker observed: “What gets measured gets managed… even if it’s pointless to measure and manage it.”
3 – People: Managing perceptions and building trust
AI can be seen as a threat by many employees, who wonder whether AI will replace them, augment their abilities, or both. Jensen Huang, CEO of Nvidia, has proclaimed: “The IT department of every company is going to be the HR department of AI agents in the future.”
Organizations must address widespread human fears over AI by demonstrating how AI can enhance human capabilities and empower people to do more. The key message is that even with AI in place, human expertise will remain essential, especially as new challenges emerge. It is important to manage how employees perceive this transition – if they see AI as a threat, they may resist or even undermine initiatives.
Successful AI initiatives focus on communication and change management, recognizing that the wrong perception of AI can significantly increase the risk of failure. If employees do not support the initiative, the organization must be prepared for a complex and costly change management process.
Don’t use AI just because it’s the latest thing.
People learning from data to create value: a roadmap
A value-data-people framework offers guidance for leaders tasked with navigating AI’s complexities. Before approving an AI initiative, ask three key questions:
- What is the value we want to create?
- Do we have access to the right data?
- How will our people perceive this change?
If you cannot answer these three questions, it’s time to rethink the initiative. Beware these common pitfalls:
- Don’t use AI just because it’s the latest thing
- Use AI to solve real problems and add true value
- For resilience and success, focus on value, data, and people
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