Dr. TJ Jiang, Chief Executive Officer and Co-Founder of AvePoint.
A few years ago, moving to the cloud was considered non-negotiable for business continuity during the pandemic and a prerequisite to fostering innovation. But with only 60% of corporate data hosted on the cloud, it’s clear that the shift still continues.
Some organizations are stunted because of their reluctance to fully adopt the digital workplace and the potential of safe, compliant and productive cloud-based collaboration. The same thing is happening now with AI and machine learning. Every organization should be using these technologies to enhance products and improve digital employee experience, but only 55% of organizations have adopted AI, according to McKinsey.
Like the initial shift to the cloud, organizations are also beginning their journeys without having the proper data foundation in place, which can make fully maximizing the potential of AI and machine learning difficult down the road.
Organizations need a clear strategy for how and when they will incorporate AI and machine learning, and they need quality, well-governed data to serve as the bedrock for impactful automation, greater efficiencies and innovation.
Ensure a strong data foundation.
Over 15 years ago, I got my PhD in data mining where we used early machine learning and AI techniques on older, slower and much larger computers. Even though technology is more sophisticated today, one thing remains the same: You need a strong data foundation before anything else. In fact, I would say that it’s 80% of the total effort to ensure that you have quality and clean data to feed your data models.
It’s the same reason why if you talk to any architect, they’ll agree that while we’ve improved construction—and many interior design trends have come and gone—having a strong foundation is still critical to building any dream home.
For AI to truly work, organizations need to ensure their proprietary, enterprise-owned data is high quality. Instead of jumping ahead to the end of the AI story, it’s worth the upfront cost to start at chapter one: Apply an advanced information management strategy that accounts for data from the various sources across your enterprise, making sure it’s properly aggregated, classified, intact and has the right information lifecycle to unlock the business value from otherwise disorganized data sets.
To avoid an oft-made mistake of garbage-in-garbage-out problem with proprietary overlay models, organizations must have high-quality, well-governed data to ensure any usage of AI can truly fuel powerful insights, deliver impactful recommendations and provide comprehensive data protection.
Tap into specialized AI solutions.
Once your data is in order, tapping into AI and machine learning to optimize operations and unleash innovation will be much more fruitful.
For example, AI and machine learning solutions can help advance internal communications by analyzing data within your digital workplace environment to identify patterns and personalize communication strategies. According to IDC, OECD and Axios, the cost of ineffective internal communication adds up to over $15,000 per employee every year.
Looking beyond generative AI solutions that have garnered a lot of press, AI can also help boost personal productivity. Imagine the time saved and increase in engagement by aligning necessary documents, meetings and people automatically. Today, nearly half of digital workers struggle to find the information needed to do their jobs effectively. AI and machine learning solutions can reduce the need for context switching and minimize time spent looking for information, transforming the digital workplace experience.
These types of solutions, however, are only possible with proper data hygiene from the start.
Empower your employees.
Finally, your employees should not be afraid to use AI and machine learning to uplevel their careers. According to PwC, 31% of employees think AI will help increase their productivity and efficiency at work. On top of that, 27% believe they’ll learn new skills as a result of using AI, which leads to better employee engagement and more positive workplace experiences.
This is key when paired with the knowledge that productivity will be a top five CEO strategic business priority by 2025, according to Gartner.
Interestingly, only 25% of organizations surveyed by the World Economic Forum think the increased use of AI and machine learning will create job losses, as compared to the 50% who think it will create job growth. That’s because, again, even though these technologies can be powerful, you need a strong foundation.
Just because AI and machine learning can help a development team speed time-to-value, does not mean you no longer need engineers. They understand an organization’s products and the unique pain points they solve. Without that foundation, you lose real-world context.
Conclusion
At the end of the day, using AI and machine learning without a robust data strategy in place is just an empty promise.
Take a hard look at how your organization’s information is managed. Ask yourself if your information management strategy is working for you, or if it’s working against you by impacting your ability to innovate with AI and machine learning.
Only then can you make real progress and harness the power of this technology to transform your data, improve your operations and accelerate innovation. In other words, first slow down to take stock and be more intentional and strategic with your own domain knowledge and expertise, before accelerating fast and scale with the speed of continuously self-improving AI.
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