As we say goodbye to 2019 and enter into a new decade, we tried to not just look back at what was and wasn’t achieved in terms of AI progress in the past year, but also to look forward at what’s next for data-driven enterprises in 2020 and beyond. In this story, we offer you our take on the upcoming trends in AI, machine learning, and data science that will impact data scientists, analysts, executives and organizations as a whole in the year to come.
2019 Year in Review: Enterprise AI is Moving Fast, But We Still Have a Long Way to Go
There is little doubt that 2019 has been an exciting year for AI: advancements on the research side in machine learning and deep learning is moving incredibly quickly. Every day, there are breakthroughs (e.g., the quick dominance of DeepMind’s StarCraft II AI), new research (like on curiosity-driven learning), and new technologies, making computation cheaper and faster than ever.
In the context of all these major innovations, Enterprise AI is undoubtedly moving forward, though perhaps not at the pace the media coverage on AI makes it seem. While the use cases for AI in the enterprise are very promising, progress is slower than the AI fantasy the media sells.
This is not necessarily bad news: after all, organizations are complex and need time to achieve truly impactful, sustainable transformation. There are engrained existing processes, revenue targets to hit, and – perhaps most importantly – there are people to consider.
AI initiatives take time and are based in organizational change, both from the top down and from the bottom up. In many ways, investing in the right technology and hiring the right key people are the easy parts; it’s the business-wide transformation (including upskilling existing staff and incorporating data processes at all levels of the organization) that is challenging.”
Florian Douetteau, CEO of Dataiku