An Overarching AI Strategy
Generative AI has superstar potential, but most projects fail. “Some estimates place the failure rate as high as 80 percent — almost double the rate of corporate IT project failures a decade ago,” according to Harvard Business Review.
The key to increasing the odds of success is to start an overarching strategy for how AI will be used across the business. Here are five components of such a strategy.
- Assess objectives to determine which technologies align best. Every retailer has unique objectives that relate to their specific needs, strengths and weaknesses. Examining these objectives and the methods through which they may be achieved is a must when identifying which AI tools are most applicable.
- Prioritize data quality and accessibility for extracting core insights. Data is only as useful as the insights it leads to. Yet quality insights stem from accessible, quality data. So, to get the most out of their AI analytics tools, retailers must prioritize the accessibility and enhancement of the data to be analyzed.
- Consider scalability and interoperability. Not every AI tool will match perfectly with a retailer's current setup. Similarly, some tools may work well for certain levels of operation, but not for others. These factors should be taken into account when determining which tools to select. This will help make AI implementation more seamless and ensure investments are made only in tools that are built to last.
- Make sure data privacy and security checks are in place. This is especially important when collecting customer data. Compliance with local privacy laws and regulations does more than just help businesses avoid a fine. It also helps retailers better protect their customers' data, further building and maintaining their trust.
- Run continuous AI algorithm trainings to reduce cyber risk. AI shouldn’t be a “set it and forget it” tool. Just like any technology, AI has its own security risks. Continuous monitoring can help mitigate these risks, however, assuaging warranted concerns. Further, constant assessment can help inform AI algorithm training, bolstering the effectiveness of the technology across retail use cases.
Together, these factors are pivotal in producing a robust, overarching AI strategy, making them essential considerations for the 98 percent of retailers planning to invest in generative AI.
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A Timeline to Help Secure Strategic Alignment
An overarching strategy should be exactly that: overarching, not beholden to the set end date of a specific objective. But that doesn’t diminish the value of a timeline. Retailers still need something to measure against. Just like basketball: It’s not about how many points are scored over the course of the season, but whether enough points are scored by the time the clock hits zero; that’s how you rack up the wins needed to advance.
To create a timeline, retailers should determine the time by which they’d like AI to drive specific results. Work backward from there, identifying what needs to be done to achieve those results and the time each task will take. Then, coordinate with all relevant teams so the timeline and goals are made clear, ensuring strategic alignment toward a measurable objective.
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Accurate Technology Selection
IT leaders need to pick the technologies that pair best with their desired outcomes. One aspect of this, stemming from the overarching AI strategy, is the assessment of business objectives. Another aspect is learning the technologies.
Some tools may be better suited to supply chain optimization over omnichannel efficiency. For instance, edge computing brings computation and data storage closer to the sources of data generation, enabling faster processing and reduced latency. Federated learning allows AI models to be trained across decentralized edge devices, helping retailers implement advanced personalization, including tailored product recommendations. In addition, predictive analytics can integrate with existing enterprise resource planning systems and supply chain management tools en route to optimizing inventory levels, reducing fulfillment cycles and saving money overall.
Alternatively, different tools may better suit omnichannel efficiency or other desired outcomes. Generative AI technologies are as varied as their use cases, after all. But retailers don’t have to research on their own. CDW and partners such as Google and NVIDIA can help IT decision makers identify the right technologies as well as outfit their AI toolkit from top to bottom, unlocking generative AI’s superstar potential.
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