By Ajay Agrawal, Senior Vice President & Head of CoE – AI/Analytics, Happiest Minds Technologies
Today Artificial Intelligence (AI) is being widely discussed and adopted by the Technology and business leaders, Research, and Academica across diverse spectrum. Earlier, business strategy drove AI, but today AI has penetrated to drive business strategy. AI is now becoming the DNA of organizations and leaders’ success, and it is finding widespread adoption for improving insights and efficiency for internal stakeholders while improving the experience and business value for their customers.
It is interesting to observe a few critical trends for 2022; and how the business landscape is changing with AI for the better.
Some critical trends include:
1. Vision Analytics: As per the Garter hype cycle, computer vision applications are touching the plateau of productivity. Vision analytics is being used for deep learning techniques on images and videos, also for multiple domains such as Industrial, Manufacturing, Media, EdTech, Healthcare, Energy and many more.
2. AI Engineering: Rapidly taking strides from proof of concept to production, by 2024, 75% of organizations would scale AI. Gartner primarily focuses on MLOps, ModelOps, and DataOps as a trend. Most organizations will need to build a clear AI strategy with the ability to handle AI applications with huge data sizes, complexity involving multi-GPU, distributed training, and inferences having a significant impact on business strategies. Organizations would also need to look at AI-governance and Responsible AI.
3. Decision Intelligence: The decision intelligence goal is to enable the business owner to trust AI better and make informed business decisions. Decision Intelligence is a larger ambit that may involve techniques like Composite AI, Agent-based systems, and Explainable AI to aid a stronger decision intelligence system.
4. Generative AI: AI is being used for making decisions, recognition, and classification for quite some time now. One of the major trends is about generation using AI for various applications. AI can be used for writing code, synthetic data creation, product creation and drug discovery among others.
5. Adaptive models: Post-COVID with the change in user behavior, the erstwhile AI models are no longer valid, especially in complex systems as customer behavior learning takes time with traditional machine learning approaches. Adaptive models provide us the ability to train ML models with lesser data and learn continuously within the environment. Adaptive models like agent-based models can simulate user behavior more effectively.
6. Augmented BI: According to Verified Market Research, the Global Augmented Analytics Market size reach USD 62.5 Billion by 2028, growing at a CAGR of 29.8% from 2021 to 2028. Vastly improving the experience, the Dashboards will be replaced with automated, conversational, mobile, and dynamically generated insights customized for every persona involved.
7. Graph Analytics: As per Gartner, “As many as 50% of client inquiries around the topic of AI involved a discussion around the use of graph technology”. The graph is not new, and social media platforms use the graph for everything. Now many organizations are adopting graph analytics to make connected knowledge systems aiding better decision making and collaboration. The graph can augment well with AI to add more value to the organization.
8. XOps: Enables organizations to operationalize data and analytics to drive business value. The failure of most of the analytics projects is for not putting proper focus on operationalization in the early phases of the project. XOps can enable the monitorability, reproducibility, integrity, and integrability of analytics.
9. Continuous Intelligence: By 2022, 50% of businesses would need Continuous Intelligence that uses real-time context data, as per Gartner. Continuous Intelligence combines batch system intelligence with real-time event augmentation. For Example, Sports goods selling websites may use a recommendation system on batch, but if any sporting event, the recommendation system can change to display items from the current sports match context in real-time.
10. Data Fabric: In a move to support a decentralized data platform, Data fabric integrates across platforms and users, making data available everywhere it’s needed. With inbuilt analytics reading metadata, data fabric can learn what data is being used. Its real value exists in its ability to make recommendations for more, different, and better data, reducing data management by up to 70% as per Gartner
11. Meta Verse: Apart from Enhancing the real-world experience, Metaverse offers a hyper-real alternative world powering the next generation virtual 3-D environments with an immersive experience to its users. It’s a combination of multiple technologies like personal computing, AR, VR, 3D Holographic Avatars, Virtual worlds, etc. Earlier adopters of Metaverse have been Video games and Virtual environment games. However, there is more excitement all around with Facebook’s recent announcement of its vision for Metaverse. With Metaverse, one can imagine almost everything, what you can do in the real universe. From attending a live event, playing, working, and even buying land in the metaverse. You can imagine just jumping to a metaverse cabin at your home, and you reach the virtual office where you can have a real-like experience working with your colleagues.
AI will play a huge role in enabling Metaverse. To name a few, AI would help create more accurate Avatar creation, support multilingual abilities for conversation, create Digital Human, support Human-computer interface and create metaverse from the real world by object detection and quick scans, model training, simulation, and testing.
The individual trends complement each other. Data Fabric can help provide seamless access and govern the continuous intelligence can work on real-time data. Generative AI can create synthetic data for a more accurate decision intelligence and adaptive models to be built on top. Graph and augmented BI can help in enriching the organization’s knowledge base and empower business insights. AI Engineering can bring the process altogether, XOps we can monitor, trace AI and data. With these focused technologies in the AI ambit, AI is getting better and more relevant with every passing day.
