10 Promising Enterprise AI Trends 2022

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10 Promising Enterprise AI Trends 2022

June 10, 2022

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A Deloitte survey shows rapid adoption of artificial intelligence solutions. Though only a few organizations are completely fueled by AI, businesses are accelerating capability building for their AI applications to scale to the enterprise level. More importantly, the willingness to experiment with AI has laid a foundation in the enterprise, which is expected to bear fruit in the coming few years.

Some of the reasons for this fast adoption of AI include:

  • ● Using data for human-centric, systematic deployment and scaling of AI for executing core business processes
  • ● Improving data-backed decision-making
  • ● Enhancing customer delight and workforce experiences
  • ● Strengthening competitiveness

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For businesses already on the path or planning to invest in AI, it is important to understand the trends and invest accordingly to optimize resources and performance.

1. Automated Machine Learning (AutoML): In an age of automation, iterative tasks associated with applying machine learning are also automated. It covers the entire pipeline from raw dataset to developing the ML model to be deployed. Some of the emerging trends in this area include better tools for data labelling and automatic tuning of neural net architectures. This is expected to lower the cost of AI, hastening its wider adoption. The next step would be XOps, as Gartner calls it, with improvement in processes such as PlatformOps, MLOps and DataOps for operationalizing the models.

2. Conceptual Design with AI: DALL·E and CLIP (Contrastive Language-Image Pre-training) from OpenAI enable generating new visual designs from a text description by combining it with images. This can help create innovative designs that can be implemented on production scale and disrupt industries that rely on creativity.

3. Multi-Modal Learning: As AI matures, ML models are able to support multiple modalities including IoT sensor data, text, speech, and vision. This is being leveraged to efficiently perform common tasks such as comprehending documents. This has wide use, including in the medical field where medical diagnosis can benefit from multi-modal techniques such as optical character recognition and machine vision.

4. Tiny ML: As AI and ML find their way into devices of all sizes, Tiny ML is becoming popular in hardware-constrained devices such as microcontrollers that power cars, utility meters, and refrigerators. Localized analysis can be made possible for sound, gestures, environmental conditions, and vital signs. Solutions for security and management of Tiny ML need to be developed for greater effectiveness and governance.

5. Multi-Objective Models: Currently, AI models tend to be developed for a single objective at a time. Going forward, multi-task models that execute multiple objectives will become possible. This will improve the outcomes of the AI models because of a holistic approach to the tasks.

6. Improved Employee Experience: AI will increasingly be used to supplement human efforts, reducing the burden on employees by taking over repetitive jobs. This will free up resources to provide value and lower personnel cost, enabling businesses to remain lean and effective.

7. Democratized AI: AI tools these days do not require technical skills to be used. As a result, it empowers even the non-technical staff to use them and build AI models. Subject matter experts will be able to participate more proactively in the AI development process, which will further accelerate time-to-market.

8. Responsible AI: AI development is guided by regulations such as GDPR and CCPA for greater AI transparency due to the use of personal data for making substantive decisions. Responsible AI even to develop AI algorithms will become important

9. Quantum ML: Powerful AI and machine learning models are become possible due to the use of quantum computing. Cloud providers such as Amazon, Microsoft, and IBM are providing quantum computing resources and simulators that can help businesses solve problems for which solutions have not been found yet.

10. Digital Twins Mature: Digital twins, which are virtual models simulating reality, are becoming popular for modeling and simulating human behaviors. They can help forecast the future and identify alternative solutions. Converging digital twins with traditional industrial simulations and AI-based agent-based simulation will also find use in applications such as ESG modeling, drug design, and smart cities.

Indium–Your AI Adoption Partner

Realizing the value of AI in improving outcomes, businesses today are investing in proof-of-concept even without a clear business value. AI is being used across functions and is improving smart decision making but requires collaboration between the technology team and subject matter and business experts to achieve the goals.

An AI, data, and software specialist like Indium Software can help businesses initiate AI projects, design them for scale, and achieve results efficiently. Our team of technology and domain experts can help businesses identify AI opportunities and accelerate AI innovation. We can create intelligent systems that mimic human behavior in completing tasks.

Must Read: CEOs and CXOs, how are you handling the shift to AI technologies?

We create self-learning algorithms that increase accuracy over time. The machine learning systems constantly evolve by examining data and learning new things from it without human intervention.

Our capabilities include:

  • ● Predictive Models using cutting-edge methodologies to design processes for complete automation using AI techniques
  • ● Natural Language Processing to analyze existing text data and capture trends, threats, and opportunities
  • ● Predictive & Perspective Analysis
  • ● Cognitive Analytics using computer vision techniques to read not just text, but also images and videos
  • ● Text Analytics to gain insights from big data through our automatic AI powered text analytics software
  • ● Internet of Things (IoT) to reduce accidents and minimize equipment downtime by identifying trends and anomalies in sensor data.

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