As we have stepped into 2025, we face heightened global economic uncertainty. The pressures on the C-suite in enterprises have increased to deliver “success from AI.” To deliver AI success, the C-suite must sharpen its focus on increasing the Enterprise Value by leveraging AI. To illustrate this, let us consider a typical example of leveraging AI.
An enterprise leverages “Customer Support AI agents” to complement and augment human agents to improve the productivity of its human customer support team. The enterprise tracks inward-looking KPIs such as the total number of customer calls handled by the team and the number of customer calls per human support agent. However, from the customers’ perspective, they are interested in reducing the time taken to resolve their issues and improving the quality of the resolutions. When the enterprise can deliver such outcomes to the customers, it would improve their experience, turn them into “happy customers”, increase word-of-mouth recommendations, and positively impact revenues. An increase in revenues can increase the Enterprise Value. Hence, the C-suite involved in the customer support function must shift its goal of leveraging AI from improving productivity to improving customer experience. It must track customer-facing KPIs like Net Promoter Score (NPS) improvement.
Let’s double-click on “how” the customer support Human and AI agents (“HAI agents”) can improve customer experience, by reducing the time to resolve customers’ issues and improving the quality of the resolutions. When a customer contacts the enterprise’s customer support either via chat/call, the HAI agents must have access to the following example “in-scope” data, “immediately”, and it must be “accurate” as well:
- the products/services the customer is using.
- health of the product/service the customer is using, through sensor data from the devices, or the operating status of the service.
- summary and action items from the recent interactions between the customer and all business functions of the enterprise, like the invoicing, marketing, sales, customer support, etc.
Important to note, the HAI agents must not have access to “unauthorized” customer information, that the customer has not provided their consent for the purpose of receiving support, such as the customer’s:
- billing address,
- date of birth,
- bank account information, etc.
Thus, the “immediate”, “accurate”, and “authorized” data access would be the key to improving the NPS and positively impacting Enterprise Value by leveraging AI.
The C-suite must be driven by the inputs that lead to AI success. While the AI models are an essential input, they are not the differentiating factor, as all enterprises can access open-source or proprietary AI models. The key input and differentiating factor, which impacts success or failure with AI, is the “Data.” The data must be accurate, accessible immediately, and authorized to access, as required in different business use cases, such as improving customer experience, as mentioned above. The data and the ability to meet these data requirements would vary for each enterprise. Enterprises that can successfully deliver the required data as input would be able to deliver the output, i.e., AI success. Thus, the C-suite must have the urgency to deliver AI success and must be “data-driven.”
This brief guide explores how the C-suite can achieve Data-driven AI Success by aligning AI initiatives with enterprise business strategy, treating data as a strategic asset, and building business capabilities to achieve the goal.
- Aligning AI initiatives with Enterprise Business Strategy.AI initiatives must enable the achievement of expected business outcomes in the enterprise business strategy. For it to happen, AI capabilities must be embedded in every business process to improve business outcomes from the business use cases delivered through the processes, such as improving customer experience through the customer support process.
Avoiding Common Pitfalls
Many stakeholders in enterprises, in a rush to become the “internal AI champion” and leveraging the “fail-fast mindset,” waste enterprises’ time and resources by executing fragmented PoCs (proofs-of-concept) that only become “cool showcases” and fail to deliver measurable enterprise value. As a guardian of enterprises’ resources, the C-suite must avoid such wastages by developing an AI strategy aligned with the enterprise’s business strategy.
- Treating Data as a Strategic Asset. As becoming “data-driven” is key to AI success, the C-suite must treat data as a strategic asset. They must provide their sponsorship and commitment towards ensuring that the AI initiatives and the business use cases receive the data they require – when they need it i.e., immediately, or every hour or every day, how they need it i.e. through API access or streams of data or analytics reports etc., while ensuring governed access to the data. Such data could be structured data from their enterprise systems, like the ERP and CRM applications, or semi-structured/unstructured data from the sensors in the devices. The C-suite must also commit to developing a data-driven mindset in the people across the enterprise.
Avoiding Common Pitfalls
To leverage data as a strategic asset, it must be shared with the stakeholders of AI initiatives and business use cases, per their requirements. For that, a robust and scalable data foundation must be developed by unlocking data from the IT applications that execute the business processes and/or store the data. It is a challenging and complex change to execute. The “Head of Data” hired to make data a strategic asset must be included in the C-suite to empower it to execute such a change.
- Building Business Capabilities for Data-driven AI Success. The C-suite must appreciate that delivering data-driven AI success is a journey, with multiple milestones of success. It must take a strategic view of building the business capabilities required throughout the journey. For example, data management capabilities must be classified as the highest-level business capability, which must be enhanced based on the needs of the AI initiatives and the business use case throughout the journey.
Avoiding Common Pitfalls
Many enterprises that have not considered data as a strategic asset may classify the data management capability as a sub-capability under the IT management capability. Such an approach would hinder unlocking data from the technology applications and sharing of data across the enterprise in a governed manner.
Conclusion: It is a “now or never” moment for the C-suite!
The C-suite must bring in the urgency of enhancing enterprise value by leveraging AI at scale. They must realize that AI success will not come only from adopting the latest AI technologies, but also by leveraging data as a strategic asset, through a robust and scalable data foundation that enables AI to scale. Data-driven AI success is a journey, and enterprises would face multiple hurdles along the way. The C-suite must be actively involved in resolving the hurdles faced. The future belongs to the enterprises that are successful with data-driven AI. The question is: Are you ready?
Note: The above content is the author’s personal point of view.
Copyright © 2025 Sujay Dutta.
Author’s Bio:
Sujay Dutta is a seasoned technology and business leader with 25+ years of global experience. He believes the future is being shaped at the intersection of AI, Business outcomes, Culture, and Data (“A.B.C.D.”).
More about the author: https://www.amazon.com/author/sujaydutta
