In the post pandemic era, we have witnessed companies face an ever-growing demand for digital transformation. This revealed gaps in digital services, databases and lags in developing and deploying solutions in the real world. The need of the hour for enterprises is to proactively come up with operational strategies and let go of their reactive nature.
As per Deloitte Insights’ recent report ‘Tech Trends 2022’, the navigation of companies to ‘the new normal’ points to an “AI – assisted future” where “…..pioneering enterprises are automating, abstracting and outsourcing their business processes to increasingly powerful tech tools.”
What are some of these trends that need to be the focus for all enterprises looking to innovate? These are some of the major AI trends to watch out for in 2022:
AI in Metaverse
In the past two years, there’s been a growing demand for multidimensional, lifelike, and immersive online spaces. 2022 will be critical for this, with companies like Facebook, Microsoft, and Nvidia investing heavily to establish a unified digital experience for users.
AI’s role in the metaverse spans different applications. For instance, deep reinforcement learning helps create virtual environments while AI-based NPCs can use a three-branch multi-stage CNN ML model for multiple-people tracking. Furthermore, LSTM and Mixture Density Network models can help transfer trained controllers in virtual environments to the physical world.
The future of the metaverse and the role of AI in it is still in the speculative stage. Businesses need to ask pressing questions like –
- How do they want to position their entry into the metaverse?
- How will they protect themselves against forgery and copyright infringements in an interconnected, cross-jurisdictional world?
- Will the use of non-fungible tokens (NFT) be sufficient for protecting brands?
- How will investing in metaverse technology accelerate business solutions?
AI powered hyperautomation
Hyperautomation will be a driving force in 2022. It is expected to streamline operations, increase efficiency, accuracy, and productivity. Interesting use cases of hyperautomation have been witnessed in assessing staff productivity and monitoring in the public sphere on COVID social distancing rules being implemented.
Hyperautomation builds upon automation but takes it a notch higher by personalizing automotive technologies. As a result, hyperautomation based on AI is an ever-improving process.
Things that organizations and businesses need to consider while adopting hyperautomation are: –
- What are the implications of moving from automation to hyperautomation using AI?
- How will hyperautomation based on AI affect human labour?
- Is hyperautomation scalable to all sectors, or will it be limited in its scope to supply chain?
Artificial intelligence is becoming increasingly important in the field of information security and will play a significant role in Industry 4.0. The cybersecurity industry for AI and machine learning is anticipated to reach US$38.2 billion by 2026. In the next five years, automated cybersecurity solutions could raise global revenue prospects by $5.2 trillion. AI will aid firms in improving their cloud migration strategy and maximizing the value of big data technology.
Attacks on the Internet of Things (IoT) is a particularly challenging issue. The Deloitte report ‘Tech Tends 2022’ suggests that companies and businesses deploy AI to learn, detect, and predict novel cyber-attack patterns. In effect, organizations can then respond to attacks faster than attackers can strike. At the same time, this will ease the burden on security operations center analysts and allow them to be more productive and proactive.
Low-code and No-code AI
Many companies and businesses lack a trained team of AI engineers who can come up with contextually relevant tools and algorithms. The No-code and Low-code turn can potentially solve this problem by coming up with simple interfaces that non-experts can use to design AI-driven tools. Much like the way web design and no-code UI tools now let users create web pages and other interactive systems simply by dragging and dropping graphical elements together, no-code AI systems will let us create smart programs by plugging together different, pre-made modules and feeding them with our own domain-specific data. This will help democratize AI technology.
Blockchain for AI
Blockchain mathematics has ushered in the use of cryptocurrency. Besides providing access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner, blockchain can monitor and govern interactions between participants without the interference of third parties. Combining this with AI is leading us to a phase of decentralized AI systems. It is high time to discuss the challenges, implications, and possible outcomes of such phenomena.
While AI will continue to be a major focus across a varied spectrum of industries, companies need to address the ethics of implementing AI. Researchers and industry leaders such as Kathy Baxter and Verena Rieser (Heriot-Watt University) are increasingly stressing on the ethics of AI. In fact, all 193 member states of UNESCO officially adopted an international ethical framework for using AI in 2021. The recommendations in this framework span four broad themes: protecting data, banning social scoring and mass surveillance, protecting the environment, and setting up tools to monitor and evaluate the ethical impact of AI.
An Electronic Frontier Foundation study suggests that organizations should strive towards forecasting, preventing, and mitigating threats from malicious use of AI. Policymakers must collaborate with researchers to address any potential misuse of AI. Additionally, engineers and researchers in AI should be aware of the dual-use nature of their work. Organizations should also focus on identifying best practices to better address dual-use concerns. Last, but not the least, organizations need to actively expand the range of stakeholders and experts involved in such sensitive discussions.