The Top 5 AI & Machine Learning Trends for 2021 And Beyond

A tumultuous year full of turmoils with the pandemic turning the world upside down has finally come to an end, goodbye 2020, and good riddance! While there is optimism for the global economy’s direction, there is an urgent need for all businesses to adapt and pivot to new realities. The Covid crisis made clear the profound impact of digitization, especially innovation driven by digital transformation.  Here are some trends that gathered momentum during the last year, and that will continue to accelerate in 2021 and beyond:

  • Augmented Analytics will transform Business Intelligence – Augmented Analytics uses AI and ML technologies to assist with data preparation, insight generation, and explanation to expand how people explore and analyze data in analytics and BI platforms.  AI is proving to be a critical enabling technology, and enterprises need an efficient way to scale their AI practices and implement AI in business. As organizations face increased pressure to optimize their workflows, more and more companies will ask BI teams to develop and manage AI/ML models. This drive to empower a new class of BI-based “AI developers” will be driven by two critical factors: First, enabling BI teams with AutoML platforms is more sustainable and scalable than hiring dedicated data scientists. Second, because BI teams are closer to the business use-cases than data scientists, the life-cycle from “requirement” to the working model will be accelerated. More BI vendors will offer AI capabilities such as natural language processing, text analytics, predictive dashboards, and AI + BI will be the new norm. 
  • No-code AI will make AI/ML accessible for all. Organizations worldwide are investing in technologies that help them accelerate and democratize the data science process as the need for additional AI applications grows. Democratization implies empowering line-of-business, management, and operational teams with advanced analytical capabilities without requiring specialized data science skills using no-code AI. Many of these no-code platforms are workflow-driven, visual drag-and-drop tools that claim to help make AI easier for non-technical people. Although simple workflows are easy to build and conceptualize, the problem is that most AI/ML models require large, very complex, and sophisticated workflows that quickly become unwieldy and create a whole new set of challenges of their own. The vast majority of the work that data scientists must perform is often associated with the tasks that precede the selection and optimization of ML models, such as feature engineering. Organizations will need to look for new, more sophisticated AutoML platforms that enable true no-code end-to-end automation. Automatically creating and evaluating thousands of features (AI-based feature engineering) and ML operationalization will be critical. The rise of AutoML 2.0 platforms will take no-code to the next level and finally begin to deliver on the promise of one-click no-code development. 
  • AI/ML, Real-time Analytics, and IoT will enable Smart Manufacturing. The Covid-19 crisis saw supply chains getting disrupted, small and medium businesses crushed,  shortages at grocery stores, and online stores running out of stock for essential items. As companies make recovery plans, manufacturers urgently need to be more resilient and transform operations using advanced technologies. Industry 4.0 initiatives will transition from PoCs to production. Diverse data will be analyzed automatically to find hidden patterns and uncover insights. Streaming analytics, aka stream processing, will enable manufacturers to make intelligent decisions with real-time applications such as predicting supply chain disruption or preventing unplanned downtime. Ubiquitous sensors and real-time quality monitoring will significantly reduce product recall as the manufacturing world embraces predictive and prescriptive analytics. The intersection of AI/ML, real-time analytics, and IoT will make manufacturing more efficient, resilient, and agile. 
  • AI-powered Automation will trigger a new wave of innovation. The next digital transformation wave will focus on using AI to optimize organizational efficiencies, generate deeper data-driven insights, and automate business decision-making. AI-enabled digital transformation will expand from “early adopters” such as financial services, insurance, and manufacturing to other industries.  AI and ML will be embedded in multiple business functions across key business areas to drive efficiencies and create new products and services. The availability of automated ML platforms makes it possible for organizations to implement AI quickly and easily without investing in a data science team. AutoML 2.0 platforms automate up to 100 percent of the AI/ML development workflow to speed up the painfully slow AI deployment, allow businesses to build faster, more useful models, and accelerate digital transformation initiatives. 
  • Responsible AI, Explainability, and Model Interpretability will be critically important. The focus on bias in AI, regulatory, and privacy requirements will pave the way for more transparency in AI and ethical AI practices that build trust. As more organizations adopt AI into their business processes, there are concerns and risks about ML/AI models’ automated decisions. Interpretable features help organizations stay accountable for their data-driven decisions and meet compliance requirements.  White-box models (WBMs) provide clear explanations of how they behave, how they produce predictions, and what variables influenced the model. With WBMs,  AI is actionable, explainable, and accountable. Broader adoption of WBMs will empower enterprise model developers, model consumers, and business teams to execute complex AI projects with full confidence and certainty, building trust.            




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