Finding the low-hanging fruit of AI | VentureBeat

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Delivering AI solutions from the testbed to production environments is likely to be the main focus for the enterprise in the coming year or more. But organizations must be careful not to push AI too far too soon, despite the pressure to keep up with the competition.

This often leads to two main problems. First, it pushes inadequate solutions into environments where they are quickly overwhelmed and this leads to failure, disillusionment and mistrust among the users, ultimately hindering adoption. The AI ​​industry is not helping with its flood of promises that their solutions offer complete digital autonomy and transformative experiences.

Small wins are still wins

In some circles, the idea of ​​going smaller with AI is catching on. Instead of a complete forklift upgrade for the entire business process, it’s better to do the easy stuff first. That is, putting AI to work in limited, non-critical areas and seeing how it performs before promoting it to bigger and better things. In this way, successes are more frequent, trust is more easily earned and AI can learn how to integrate with the world as it is before trying to improve it.

For many organizations, however, the question is where to find this low-hanging fruit.

According to Joe Bush, editor of The manufacturer, it’s all around us. For example, resource consumption can be monitored much more easily and effectively with an intelligent platform than with teams of operators. Speaking to an industrial audience, the same need exists to minimize the use of electricity, water and other commodities in the enterprise. With the right sensor-driven data, AI can also assess and even move the workload in the digital environment to ensure that the work-machine balance remains optimal. And AI can also respond to changing conditions much faster than manual operators and streamline key processes such as reporting, maintenance planning and delivery.

Of course, it doesn’t hurt to have a plan in mind when deploying AI in production environments, as it is much more valuable to collaborate than isolation. Accenture’s Bhaskar Ghosh, Rajendra Prasad and Gayatri Pallail recently entered The Harvard Business Review that rather than aiming for quick victories or grand strategic transformations, the wisest course right now is to focus on building capabilities that address problems that will arise in the future. This requires careful analysis of current capabilities and identification of any gaps leading to failures. You can then develop a step-by-step approach to deploying AI so that it achieves the small victories that ultimately lead to the big transformation.

Small and broad data

Some organizations are also starting to realize that throwing AI at big data and hoping for something magical to happen isn’t the right choice either. According to Rohan Sheth, associate vice president of Infrastructure Solutions at colocation provider Yotta, AI will likely be less effective at processing massive amounts of data and more effective at using smaller amounts of more accurate data — what some may already consider small and broad data. To get there, however, the company will need to improve its capabilities to analyze and condition data before feeding it into AI models, which happens to be another area where AI could be of great use.

The extent to which AI can support the enterprise strongly depends on an organization’s ‘data maturity’, said Sumit Kumar Sharma, enterprise architect at In2IT technologies† In a recent interview with ITWeb, he explained that there is no “one-size-fits-all” approach to AI, as each organization’s needs and legacy environments are different. Depending on how data is generated, consumed, and stored, different flavors of AI will provide a unique set of services, and these services will be better for some business models than others. For example, a business-to-business (B2B) supplier would make more use of chatbots and natural language processing than a large analytics company, which in turn would likely be more drawn to machine learning and neural networks.

At this point, it might sound like AI is just another technology looking for a solution, and in a way it is. But there is one big difference between AI and previous generations of technology: it can adapt and respond to new data and changing circumstances. This gives the enterprise a lot of leeway to try and fail with AI, as long as each failure leads to a greater understanding of how they can succeed in the future.

It might be tempting to push AI right into key aspects of the business to reap the benefits of a completely transformed business model, but it’s not ready for that yet. Like any other employee, he must start small and prove himself before he can be promoted to bigger responsibilities.

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Shreya Christina
Shreya has been with for 3 years, writing copy for client websites, blog posts, EDMs and other mediums to engage readers and encourage action. By collaborating with clients, our SEO manager and the wider team, Shreya seeks to understand an audience before creating memorable, persuasive copy.

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