Latest Posts

Stay in Touch With Us

For Advertising, media partnerships, sponsorship, associations, and alliances, please connect to us below


+91 40 230 552 15

540/6, 3rd Floor, Geetanjali Towers,
KPHB-6, Hyderabad 500072

Follow us on social

Top AI Trends to Look Out for Your Business in 2022

  /  Latest News   /  Top AI Trends to Look Out for Your Business in 2022

Top AI Trends to Look Out for Your Business in 2022

These AI trends must be checked out

With more and more evolution, artificial intelligence is advancing at a great pace. Following the addition of more disruptive technologies in its cognitive capabilities and rising market demand of AI skillset, the technology is trending in 2021. AI is going to become only greater in the coming times with new abilities and attributes adding to it.


ROI-Driven Approach for AI Development

Some of the common reasons for AI projects’ unclear financial outcomes include limited access to quality training data, a lack of resources and skills to implement artificial intelligence at scale, a tendency to over complicate things — for instance, by using deep neural networks were simpler yet highly effective machine learning techniques could produce similar results an a failure to gain buy-in at the executive level and proceed to a full-blown project

Fostering collaboration between business and IT teams, which could help companies decide on the business outcomes of deploying AI innovations beforehand and ensure C-Suite support. Select a limited number of use cases for an AI pilot while devising a broader implementation plan early on. Up skilling existing IT teams and enlisting the help of experienced artificial intelligence companies — either for turnkey AI system engineering or data preparation tasks, such as discovery, cleansing, and labeling. Utilizing a powerful combo of SaaS, open-source, and custom-made AI solution components to speed up development cycles. Ditching vast piles of historical data, which has become less relevant since the COVID-19 outbreak, in favor of wide and small data; the former is aggregated from a variety of sources, while the latter is easier to interpret and deliver instant insights. Learning from failures, retraining algorithms on new data, and creating a continuous loop for machine learning model redeployment


AI-Augmented Automation

In the pandemic-ridden world, the role of information technology has shifted from supporting business operations to delivering business value. This change has had a profound impact on AI innovations and trends. Fueled by 5G roll outs, more powerful computer chips, the diminishing sensor prices, and flexible cloud services, the convergence of robotic process automation (RPA), artificial intelligence, and the Internet of Things (IoT)technologies has become a viable reality. This powerful trio brings companies one step closer to AI-augmented automation and pervasive intelligence.

From lightweight RPA robots that take over payment processing tasks in healthcare to self-checkout solutions that know precisely which items a customer has put in the basket, recent advances in artificial intelligence will help enterprises achieve resilience and significant cost reductions. 80% of executives are currently accelerating their business process automation efforts (World Economic Forum). 25% of companies already use AI in workflow automation, while 51% of enterprises are planning to do so shortly (Salesforce). By 2023, 40% of infrastructure and operations (I&O) teams will use AI-augmented automation in large enterprises, freeing up IT personnel’s time for strategic work (Gartner). 66% of businesses have increased their revenue by deploying artificial intelligence systems (McKinsey)

By 2021, enterprise AI budgets have increased, with 74% of companies allocating $500,000 or more for their artificial intelligence projects, which marks a 55% increase from 2020 (Appen). In their recent article for Harvard Business Review, David De Cremer and Garry Kasparov described AI-based machines as “fast, more accurate, and consistently rational,” noting, however, that present-day AI innovations often lack intuition, emotional intelligence, and cultural sensitivity. While end-to-end automation and subsequent workforce reductions are not yet among the artificial intelligence trends for 2022, governments will need to address the moral implications of artificial intelligence adoption in their national AI strategies, ensuring positive automation outcomes for all parties involved.

When it comes to business AI, companies should focus on creating intelligent systems that explain the rationale behind their decisions and do not discriminate against people based on their ethnicity, age, gender, religion, or place of residence. Additionally, stakeholders need to clearly communicate automation benefits to get employees on board and encourage them to participate in AI deployment and training.


Cybersecurity Revolution

Using intelligent algorithms for cyberattack prevention became one of the most significant AI innovations of the past years. Forward-thinking enterprises leverage artificial intelligence to detect suspicious traffic within corporate IT networks, identify malicious software programs, spot infected links in employees’ emails, and even model cyberattack scenarios based on vulnerability assessments. The trouble is, the other side (i.e., cybercriminals) can do the magic too. For example, hackers can now manipulate the data used for AI model training, causing adversarial attacks. Other AI breakthroughs in the cybercrime field include inference, which allows attackers to access sensitive data by reversing AI engineer systems, and advanced social engineering techniques driven by behavior analytics. Additionally, hackers may employ AI to pinpoint security vulnerabilities in corporate IT systems. To address these looming threats, companies looking to deploy AI innovations should closely monitor all the data presented to AI models, review real-world data used for model training, and build the elements of randomness into their artificial intelligence models.


Sustainability in Enterprises

Through 2022 and beyond, governments and businesses alike could turn to artificial intelligence to reduce reliance on fossil fuels, combat deforestation, and cut CO2 emissions. And while green AI innovations are still scarce at national levels, we’ve seen several commercial AI deployments that assist businesses in achieving their sustainability goals. Google, for instance, coupled their data center cooling technology with deep learning and reduced energy consumption by 40%. And CO2 AI, a novel sustainable artificial intelligence solution by Boston Consulting Group, helps enterprises cut CO2 emissions by 30–40% by accurately measuring their carbon footprint and making intelligent recommendations.