Each of these disciplines can be significantly enhanced by AI, but it is crucial that organizations follow a practical approach to ensure that the integration of AI is both purposeful and effective, maximizing its value across different technology domains:
- Establish a robust data strategy
- Pinpoint specific use cases
- Rigorously test potential AI solutions
Data is the bedrock of any AI strategy
Data is the fuel that powers AI systems. Without a comprehensive data strategy, AI solutions cannot function optimally. A robust data strategy involves identifying and aligning business and technology objectives to achieve specific outcomes. This strategy should optimize the organization's ability to deliver reliable data at scale, wherever and whenever it is needed across the business.
Importantly, a sound data strategy also involves ensuring data quality. The effectiveness of AI solutions, such as large language models (LLMs), is largely dependent on the quality of the dataset used to train the model. Therefore, IT leaders must understand how data is produced, collected, utilized and analyzed. They need to ensure the data being fed into AI systems is trustworthy and unbiased or else they risk generating unreliable outcomes.
Identify use cases for targeted AI implementation
Once a solid data strategy is in place, organizations need to identify the specific use cases and outcomes they want AI to help them achieve.
Leaders should focus on developing and driving a top-down strategy that aligns with business goals, clarifies where AI is already being used, scales grassroots efforts where appropriate and follows a people-first approach.
This involves identifying the areas of business where AI can advance the organization's strategy, from improving operational efficiency and customer experience to enabling smoother regulatory compliance and driving innovation.
Incorporating generative AI into a digital assistant can benefit both end users, be they employees or customers, and the team responsible for the business process in question. Another example would be incorporating AI into security operations as a way to increase awareness and response to threats.
Test AI solutions for effective development
Once use cases have been identified, organizations need a way to compare, contrast, validate and test the integration of AI solutions before they commit to purchasing and implementation. Testing AI solutions involves assessing their performance against defined objectives and use cases. This is true whether the solution is comprised completely of off-the-shelf products, is custom built or contains a combination of both.
This process can also involve assessing the maturity of the organization's IT infrastructure, reference architectures and application development lifecycles to determine if they are capable of enabling data-intensive, AI-powered solutions. It's also crucial to consider factors such as data integration capabilities, scalability, interoperability, security, compliance, and the user experience for employees and staff.
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