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From predicting consumer behavior and trends to more efficient forecasting of supply-chain issues, artificial intelligence is a tool that is gradually revolutionizing several industries and will continue to do so in the coming years. The AI market is increasingly surging, with the AI market size globally expected to topple $360 billion by 2028. Surging innovation in the Artificial Internet of Things (AIoT), increased investment in robotics, and machine learning are AI trends that have aided business transition as organizations modernize their business processes and services.

But, for all the promise accompanying artificial intelligence tools, there are some significant challenges that enterprises need to address to successfully implement this technology, including data scarcity, limited knowledge, and data security. Why should business leaders trust artificial intelligence going forward, and how can they address the concerns that come with AI implementation?

Artificial Intelligence Trends in 2021 ?

How well are enterprises adopting AI in 2021?

The urge to quickly interpret and analyze data in the pandemic has sped up the adoption of artificial intelligence throughout various industries, including healthcare, manufacturing, technology. New methods have been developed through advanced technology to collect and aggregate information. In healthcare, for example, AI solutions have been created to assess a backlog of health issues, including cancer. Machine learning is being applied to huge, real-time datasets to spot outbreaks, more accurately diagnose health conditions and predict how viruses evolve.

Another notable trend with artificial intelligence in 2021 is the advent of hyper-automated, intelligent business processes. AI tools are being embedded into how organizations execute their business processes, using smart programming and various algorithms to carry out basic tasks. With the drastic changes caused by the global pandemic, AI is heading towards hyper-automation, responding to unexpected procedures and manual data adjustments. The pandemic also exposed an excessive reliance on manual processes like loan applications in the banking industry and airline rescheduling. Industries have combined machine learning and robotics to handle unexpected surges in demand caused by life-shifting events like the pandemic. AI tools are graduating from consumer-level to enterprise-grade as a result.

Given how much artificial intelligence relies on big data, there’s the chance unethical data seeps into an AI training model. Enterprises recognize that as they create AI tools that execute their operations, they must create ethically-conscious AI that offers responsible, thoughtful solutions to their clients. Organizations are exploring various measures to make their artificial intelligence tools more compliant, including the cleaning and subsequent assessment of existing AI models and data sets.

Business leaders can seamlessly describe a model post-development through explainable machine learning, offering transparent architectures to allow users to fully understand data results. Enterprises also ensure sociological fairness in their machine learning predictions and debug their AI models to make them more secure and maintain privacy. Businesses can introduce trustworthy, advanced solutions into their respective marketplaces through more ethical practices and refining processes to ensure AI tools meet the necessary regulatory requirements.

The Challenges That AI Must Overcome

Despite the increased knowledge being shared about artificial intelligence, there remains a distrust around AI technology that should be addressed.

There is still the unknown nature of how deep learning models in artificial intelligence predicts output, causing worry among people unfamiliar with AI technology. People who don’t have specialized knowledge of AI tools simply won’t understand how well a specific set of inputs will create solutions for different problems. Also, they won’t know how artificial intelligence is gradually integrated into everyday items they use like smartphones and online banking. Aside from technology experts and researchers, not enough people know the true potential of AI. Only by educating non-specialists, including business leaders, about AI’s potential will the trust deficit be reduced. Companies can learn how to schedule their work, understand consumer behavior, and efficiently manage their product using AI technology.

Another challenge artificial intelligence faces are the potential for human error that weighs down AI from achieving its highest potential. While companies are seeing increased accuracy with their AI tools, more human efficiency is required, and improved deep learning models featuring hyperparameter optimization, high fine-tuning levels, and well-defined, accurate algorithms are required. One way to ensure humans aren’t saddled with too much hard work is to implement pre-trained AI models, trained on countless images and are constantly fine-tuned for maximum accuracy.

Also, there is the issue of bias. How well an AI system performs depends on how much data it’s trained on. However, some businesses struggle to implement enterprise AI because so much of the data organizations collect is meaningless, with no real purpose. Some of the biases within organizations hinder successful data analysis and the introduction of technology. Defining algorithms to address these biases will go some way towards correcting the problem.

The pandemic has transformed how enterprises operate and how we live as a whole. Though several parts of the global landscape have adopted digitization, more progress can be made. Artificial intelligence tools and platforms have been implemented to help enterprises understand client/consumer behavior in the new normal. In industries like the commerce industry, organizations that previously lagged with their digital channel uptake increasingly favor concepts like personalization and behavioral analytics. Throughout 2021, artificial intelligence tools provide enterprises with self-service technology access as they try to improve their competitive positioning.

Also, there is the increased adoption of AIoT, combining artificial intelligence with the Internet of Things (IoT). AIoT translates unused data quickly, and helps companies gather valuable insights from the constant data inflow. Through AIoT, enterprise AI becomes smarter and organizations enable more intelligent business decisions in real-time, improving productivity and analysis capabilities. Around 73% of data in an enterprise goes unused, resulting in businesses marrying the best elements of artificial intelligence and the internet of things to translate data and use it to make smarter business decisions.

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Written By:
Aviskaran team
Content Writer, Aviskaran Technologies