2020 Predictions in Artificial Intelligence to Consider When Operationalizing AI Developments
With 2019 hardly fading in the rear-view mirror, it’s a good time to take a step back and see what’s in store for the next 12 months. For those managing and implementing AI and ML projects and deployments, it’s been a rapidly evolving ecosystem, and 2020 will be no different.
Here are Appen’s six 2020 predictions in artificial intelligence worth keeping an eye on.
Moving AI from POCs to Programs Still a Challenge
Many organizations continue to experiment with AI but seem to be making slow progress from proof of concept to active program phases. This is because there are still major roadblocks and hurdles to overcome before operationalizing AI at scale. We can expect this challenge to continue in 2020. IT leaders looking to get ahead will need to put extra effort into creating the right framework to move projects from POC to production and deliver business value, and that includes the correct algorithms and methods, the right training data, and a reliable way to measure success.
Focus Moves to Optimizing Data and Analytics Investments
Business leaders will focus on showcasing the ROI of data and analytics investments as these strategies mature. To highlight the global impact of D&A strategies, enterprises must turn to technologies that optimize costs, solve data challenges, and drive business value.
Evaluate your data strategies, including planning for data collection, ensuring you have enough clean data, understanding your data, and tapping partners like Appen to ensure you have high-quality training data for your initiatives.
Responsible AI Becomes a Key Component of the Future of Work
Responsible AI, or the practice of ethical, transparent, and accountable use of AI technologies, will be at the forefront of conversations about the future of work in 2020. AI continues to transform learning and work environments, changing what work we do, and how that work gets accomplished. AI and ML practitioners should be prepared to answer how AI will improve the worker experience through the likes of developing new worker skills, supporting organizational competency in AI, and spearheading innovation.
That’s not all – responsible AI should also extend to those that collect and label data. As explained in Appen’s Crowd Code of Ethics, it’s critical to support those that make AI and ML possible. This encompasses supporting fair pay, inclusion, crowd voice, privacy and confidentiality, communication, and well-being for our crowd.
Broad Adoption of AI for Customer Service
Due to recent gains in natural language processing (NLP), customer service is experiencing an upheaval. NLP advances are opening up possibilities with chatbots, online Q&As, sentiment analysis, and more. These advances stem from improved training data and accessible structured data – something that prevented NLP from truly impacting customer service in the past. Because of this, we can expect to see broad adoption of AI applications that modernize the customer service experience.
ML Tools & AIOps Gain More Traction in Enterprise
The growth of AI and ML over the last few years has led to an evolution of the entire ecosystem, including machine learning tools and AIOps initiatives. Expect massive growth in AI and ML tools in 2020, including the likes of data annotation, model training, model serving, and more.
As ML tools gain pickup, enterprises will turn to AIOps to efficiently monitor, automate, and provide service desk support to drive faster and better decision making. One challenge to monitor will be the cloud-based nature of these tools and how companies operating entirely on-premises will adapt. Despite large platforms like AWS, GCP, and Microsoft Azure opening the door for cloud deployments, many Fortune 500 companies are just not there yet.
AI Initiatives Will Need to Embrace Secure Data Practices
Continuing with cloud-based challenges, companies are also battling with security and ethics best practices. With data breaches (especially those including PII) becoming a costlier and costlier problem, conversations around AI initiatives will circle back to secure data practices, potentially driving more on-premises AI deployments.
Leaders that make AI work in the real world will succeed in expanding their AI projects to core business by making sure that their teams work with not just the most advanced algorithms, but also the best tools, scalable high-quality training data, and partner expertise.