Home > Blogs > 10 AI Trends to Watch for in 2019
From powering chatbots to boosting cybersecurity and fraud detection, Artificial Intelligence (AI) is increasingly finding applications across key business functions including sales, marketing, finance, customer service and IT. As enterprises put digital at the core of their transformation, the impact of AI on business performance will continue to increase exponentially. According to IDC, by 2021, AI applications across enterprises will account for $52 billion in global revenue.
How do businesses plan to go about investing in AI? Forrester predicts that in 2019, business will approach AI rationally by building data foundations, bridging AI skill shortage, and leveraging human reasoning capabilities. Leading companies like Apple, Facebook and Microsoft are already investing in AI technologies and tools to create more personal and effective customer experiences. In India alone, more than 68% companies will be investing in AI by mid-2019. To stay relevant, businesses must focus on redesigning core business processes using AI, and move from generic forms of AI to domain specific solutions that embed subject matters expertise and knowledge.
Here are some of the key people and technology related trends that will shape AI in 2019:
#1 AI will enable existing teams:
AI will be a powerful enabler of superior performance. According to RELX group survey, for 51% workers, AI and related technologies help increase efficiency and productivity. Organizations that leverage the powerful combination of humans and machines will achieve most success in AI in the year ahead.
#2: Companies will continue to fight for AI talent:
The reality is most organizations that want to embrace AI lack the right talent. Gartner predicts that by 2020, 80% of AI projects will remain alchemy, run by wizards whose talent will not scale in the organization. As businesses leverage advanced AI capabilities such as virtual agents to handle complex tasks, it will be critical to recruit talent with creative problem solving, resiliency, coding and statistical skills in order to scale rapidly.
#3 Human expertise will become critical:
According to Forrester, 10% of firms leveraging AI will bring human expertise back into the AI sphere. Why? More organizations will leverage knowledge engineering and digital decisioning platforms to build knowledge platforms. This will create the need for human reasoning capabilities to successfully analyze data and recognize patterns.
#4 Upskilling will assume new importance:
According to VP and CTO at LexisNexis Legal and Professional, in 2019, forward looking companies will develop necessary AI skills in-house. This is because two-thirds of AI decision makers struggle with finding and acquiring AI talent externally, and 83% struggle with retention. To maintain a competitive edge, companies will turn their attention to developing in-house experts and leveraging training partnerships.
#5 Demand for data scientists will grow:
According to IBM, the annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020. As organizations implement AI projects, the applications will need constant care from data scientists, causing the demand for data scientists to grow even further.
#6 UX/UI design will take center stage:
Superior UX/UI design is crucial to winning customer confidence. So it follows that as AI solutions become more customer-centric, superior UX/UI design will become essential to improving the productivity of users, helping them perform tasks at rapid speed and scale. A classic example of superior UX design is Amazon. The company conducts continual A/B testing on all aspects of its customer-facing user interfaces and continually updates and redesigns them based on that data.
#7 Interoperability among neural networks will become strategic:
One initiative that is going to promote interoperability of neural networks is the Open Neural Network Exchange (ONNX). It will help reuse the trained neural network across multiple frameworks to speed up AI adoption. AWS, Facebook and Microsoft are collaborating to build the ONNX to address challenges in choosing the right framework and porting trained model to another framework.
#8 The edge will become the next AI frontier:
By 2022, Edge Computing market share will reach $6.73 billion. As organizations continue to use IoT and sensors to gather data, they will be required to collect the data near the source. This will be critical to developing successful algorithms that require heavy duty computing power.
#9 Automated ML will gain prominence:
Auto ML platforms will become the new norm. It will help address complex ML scenarios without putting pressure on developers to go through elaborate workflows. In addition, they will deliver the right level of customization and agility. Google has already introduced automated machine learning on G Cloud and plans to use auto ML to make ML accessible to much larger audience of software engineers.
#10 AIOps will become mainstream:
AIOps and DevOps will converge in 2019. Why? It is predicted by 2022, 40% of all large enterprises will combine big data and ML functionalities. When AI is applied to the data sets, IT operations will transform from a reactive to a predictive mode, enhancing the precision of operational teams and enabling accurate root cause analysis.
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14) MarketsandMarkets, Edge Computing Market by Component (Hardware, Platform, Solutions), Application (Smart Cities, Location Services, Analytics, Augmented Reality), Organization Size (SME, Large Enterprises), Vertical, and Region - Global Forecast to 2022, https://www.marketsandmarkets.com/Market-Reports/edge-computing-market-133384090.html
15) Connected, AutoML and the Rise of Advanced Machine Learning Platforms, https://medium.com/connected/automl-and-the-rise-of-advanced-machine-learning-platforms-43a445023330
16) Analytics India, What Is AIOps & Why It Is The Future Of IT Operations, https://www.analyticsindiamag.com/what-is-aiops-why-it-is-the-future-of-it-operations/