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4 Key Trends of Gartner’s Artificial Intelligence Hype Cycle 2021

Artificial Intelligence

Gartner developed the Hype Cycle as a graphical and conceptual presentation to highlight the adoption, maturity and social application of specific technologies. The Hype Cycle for Artificial Intelligence (AI) this year presents some key trends which tend to dominate the AI landscape. The advancements in machine learning, chatbots, edge AI, computer vision, natural language processing (NLP), and others have led to their rapid adoption by businesses the world over. Organizations are using AI solutions to manufacture, upgrade their existing products, and expand their customer base. However, the main focus for businesses is the adoption of proof of concepts into their production processes at a faster pace. The AI landscape for this year shows the following 4 key trends:

Improved use of data, models and compute

More organizations across all verticals are adopting and innovating with AI. Hence there is an improved use of data, models and compute. Let’s take composite Artificial Intelligence for example, it combines two different AI techniques to achieve better results. It combines “connectionist” AI techniques like deep learning, with the more “symbolic” AI approaches like graph analysis, rule-based reasoning, or other modeling or optimization techniques. The result is that the AI application is equipped better to solve a wide range of issues in a diligent manner to improve productivity. Businesses can adopt generative AI to create and publish original content, synthetic data, and digital models of physical objects. Gartner predicts that by 2025, 30% of new drugs and resources will be developing with the aid of generative AI techniques.

Integrating AI with operational processes

While delivering continuously, integrating AI solutions into business workflows and enterprise applications is a daunting task for any organization. As per Gartner, it takes around 8 months for an AI model to integrate with the business workflow to deliver tangible results. It estimates that by 2025, 70% of businesses will have operationalized AI architectures. Due to rapid advancements in AI orchestration initiatives, integration will be faster. Businesses should take full advantage of model operationalization (ModelOps) for integrating AI based solutions. ModelOps reduces the time taken for integration into production and follows a principled approach to achieve a higher degree of success.

Data collection for Artificial Intelligence

We all know that AI uses the existing data and customer patterns to build better business solutions. As consumer behaviour changes from time to time, many AI and machine learning (ML) models have become obsolete. For e.g., the Covid-19 crisis has changed the conditions in which we conducted our lives and completely altered consumer behaviour. With this newfound data, the older AI and ML models had to be discarded for newer models. We see that there is a shift from traditional “big data” to what is known as “small and wide data”. Business leaders and data analysts are turning towards “small and wide data” which provides more depth and valuable information. Gartner expects that by 2025, 70% of businesses will shift their focus from “big data” to “small and wide data”. This will them help understand the market better and make AI work with less data.

Fair and responsible AI

With AI aggressively becoming the new norm in this digital age, we are coming to terms with it’s positive and negative impacts. As AI models require less human intervention, there is a possibility that some AI approaches can be misleading and biased. This can seriously affect a business’s reputation, value and customer base. For e.g., an AI system might perfectly identify an American wedding. But might not be able to detect the same in other places like Asia or Africa. There is a need for organizations to develop and offer AI models and solutions which can function without bias. The AI systems should respect privacy and safety of the society as a whole. Going forward, the AI landscape will be moving towards developing a fair and responsible AI.


Read Next: Artificial Intelligence + Robotic Process Automation: The Future of Business


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1 Comment

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