Despite the fact that organizations are rapidly implementing ML and AI initiatives, many tools and projects lack adequate resources, are significantly less effective than they should be, lag in implementation, and fail or are abandoned far too frequently.
According to PwC’s Global Artificial Intelligence study, business interest in AI has exploded in recent years, with spending expected to exceed USD15.7 trillion by 2030.
Businesses can gain a better understanding of AI/ML methodologies by learning more about them. However, a comprehensive understanding of how business dynamics interact with emerging technology capabilities can accomplish much more.
A new approach to such projects, one that focuses on breaking out of the hype cycle, challenging set best practises and asking different questions of their data and capabilities, can help business leaders realise AI’s full potential.
Here are three strategies that can help companies of all sizes rethink AI/ML, generate higher-value use cases, and achieve better results.
Start with a clear business challenge
One of the most common mistakes companies make with AI/ML projects is starting with their data or accessible capabilities rather than a specific problem they want to solve and working backwards to the capabilities needed.
That seems to make sense on the surface. The largest and most complete training datasets are generally used to build the most complex, high-impact ML models. However, in those circumstances, it’s usually about massive, curated, purpose-built datasets designed for particularly data-intensive use cases. The logic of deploying machine learning where there is the most data to obtain the best outcomes doesn’t hold up at smaller scales for the vast majority of enterprises.
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When AI/ML teams start with data and dive in where they have the most data, they frequently end up with models and capabilities that, while theoretically sophisticated, don’t help them solve any urgent business challenges. And if they aren’t solving actual problems, they aren’t creating value, which is one of the main reasons why so many companies that experiment with Machine Learning abandon their projects before seeing a clear return.
Leverage AI to augment human intelligence
The headline results of AI aren’t solely driven by technology; they’re also driven by the people who are empowered by it.
Despite the fact that both technologies are associated with autonomy, they are most valuable when they are used in human teams and those teams are given the skills to get the most out of them.
It’s critical to remember that Artificial Intelligence isn’t a replacement for human intelligence; rather, it augments it.
Machines and humans excel at things that are vastly different. Humans are consistently better at contextualizing the insights offered by AI models while machines are at sifting through and extracting meaning from enormous amounts of data.
They work better together, and businesses must reflect this in their implementation approach if they want to get the most out of their AI and ML investments. This entails offering relevant training for teams, preparing them for how new AI capabilities may alter their work patterns, and changing processes in ways that are beneficial to team members.
Rather than dumping new capabilities on teams and forcing them to alter their working patterns, the focus should be on enabling individuals to use AI capabilities in ways that support their working patterns and how they operate.
Consider AI as augmenters of the team
AI models are functional extensions of the team since they increase human capabilities and intelligence, and they must be developed as such. If models and use cases are created in isolation rather than in accordance with the unique needs of the teams who will use them, such teams are more likely to avoid and reject them.
Businesses often fail to prioritize crucial success elements such as transparency, ease of use, and how easy it is for humans to interpret the outputs of an AI model into useful actions during the design process. When certain elements aren’t taken into account, the models are unable to produce the expected value.
HR can teach the teams that create and deploy ML use cases some significant lessons in this area. High-value ML models or AI use cases are similar to the ideal new recruit in many aspects. Both should fit in well with the rest of the team and be easy to communicate with. They should bring new ideas and capabilities to the table without disrupting current goals, processes, or strategic priorities. They should also support and enhance the abilities that a team already has, maximizing their efficacy.