With artificial intelligence (AI) proving its worth, many companies have jumped on board, investing in data experts and technologies to play the AI game. Yet despite these investments, only 8% of companies surveyed use artificial intelligence (technologies capable of performing cognitive functions associated with the human mind) and other advanced analytics at the level required to do so. the highest possible value. These practices include adopting a unified vision of AI among leaders, using standard methodologies and agile development teams, adapting talent management strategies for artificial intelligence, and integrating AI into decision-making processes. . Clearly, IT's vast experience in mass technology deployments enables it to undertake these and other efforts required to evolve AI, such as workflow redesign, communication with all employees, and value tracking. But with so much to do, where do I start? We found CIOs who succeed in adapting AI to three main activities: building business analytics and partnerships, prioritizing network-based initiatives, and building technologies and tools that are critical to adaptability.
Forge partnerships with analysts and business leaders.
Close collaboration, shared responsibility, and joint efforts among business, IT, and analytics managers are critical to building AI capabilities across the enterprise and eliminating data silos, functional silos, and silos. They often weigh on your initiatives. At a major European bank, the IOC facilitated the formation of this vital coalition. As part of his job, he explained to business leaders that key capabilities, such as the ability to retrieve and analyze customer data in real time, would be economically feasible and continue as a business. 2025. This generated enthusiasm for artificial intelligence, which stimulated business. leaders to fill in the boards with ideas on how they could take advantage of these capabilities. This clear vision of the possible has helped the organization create a cohesive roadmap for AI and build momentum for early use cases. As the bank launches new use cases, the IOC sends IT SWAT teams along with business experts, data specialists, and designers as part of agile delivery teams to avoid technology bottlenecks that could impact implementations or adoption by the end user. When issues arise, such as a data access issue, IT is there to quickly get you back on track.
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Prioritize artificial intelligence initiatives to achieve a network effect.
For many companies, the question is not whether to look for artificial intelligence, but where to use it. Use case value, feasibility (eg, difficulty of implementation), and time horizon are critical factors in use case selection. Beyond these factors, CIOs must also consider the potential network effect they can create based on their deployment choices. The CIO of a major telecommunications provider worked with business leaders to prioritize three use cases closely related to sales and marketing: accurately segment customers, identify the best product to buy, and predict customer churn. By factoring in all the data cleansing work required for these use cases, they could create a 360-degree view of the customer in a year, laying the foundation for even more advanced use cases that can generate hundreds of millions of customers. extra value dollars for users. company. One of the biggest mistakes IT managers have made is using traditional technologies to maximize the capabilities of AI. They are simply not flexible or cheap enough for data- and power-hungry AI models. While the CIOs at the European bank and the European telecommunications company were investing in different technologies, their selection criteria were very similar and included:- Open architectures for data lakes, data management tools and artificial intelligence software, based on or compatible with open source technology. This enables them to support a growing number of use cases across geographic areas and business units and easily integrate growing volumes of data, new types of data (such as voice or data). image), additional features (such as streaming or streaming), and various artificial intelligence techniques. (from machine learning to deep learning) - all at a fraction of the cost of existing systems.
- System independence so that artificial intelligence systems and supporting technologies such as security, management, storage, and data environments can run as needed. A cloud, on-site or in a hybrid environment. This enables organizations to expand (or shrink) environments as data processing needs change and move AI solutions more seamlessly from development to production.
- Standard artificial intelligence technologies, methodologies (for example, code reuse), and partnerships with third-party data brokers, IoT solution providers, and others to ensure interoperability of artificial intelligence systems and enable artificial intelligence talent to be Deploy quickly, when appropriate, with a common set of reusable tools. Such standardization can, for example, allow AI teams to easily integrate a supply chain forecasting tool with a new AI-managed inventory management system for a data processing process. More transparent controls.