Future of connected world with AIoT

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11:45 Minutes The average reading duration of this insightful report.

Primers

Primers: Primers are quick short form business reports that educate leaders on key emerging technologies.

Future of connected world with AIoT

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AIoT is a revolutionary blend of AI and IoT that creates a connected world with limitless opportunities. Smart devices can collaborate to make informed decisions without human intervention, transforming various industries. As AI and IoT converge, their applications will become more advanced, presenting new prospects for businesses and consumers.

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What’s inside

  1. Insights and technology trends
  2. Overview, benefits, and architecture
  3. Use cases, challenges and key players
  4. Enterprise AIoT adoption strategy

Insights and technology trends

  1. AIoT enables devices to become smarter and more autonomous by analyzing massive data streams in real time and making informed decisions based on that data. They are expected to become more personalized and intuitive by implementing self-learning algorithms, providing real-time solutions. 
  2. Fast, reliable connectivity ensures seamless user experience and data exchange, allowing AIoT systems to respond and act promptly.As cybersecurity technology advances, users gain confidence in the security of AIoT systems.
  3. AIoT can contribute to sustainability initiatives through intelligent monitoring and control systems.
  4. Integrating new technologies in AIoT systems has become an essential component in the future of enterprises and individuals. Key Technology Trends include:  Secure Access Service Edge (SASE), Nanotechnology, Multiplicity, Contextual Proactivity, Seamless Multi-Modal Interaction, Brain- Computer Interfaces.

Overview, benefits, and architecture

AIoT combines sensors, AI, data and ambient computing elements to create a responsive, context-aware environment. It uses embedded devices and natural user interfaces to provide services based on detected requirements and user input.

AIoT can revolutionize how users interact with technology, offering greater convenience and seamless connectivity. The benefits include: intuitive and seamless experience without commands, automated decision making, efficiency and convenience.

While creating an AIoT system, a well-balanced architecture is crucial to manage data processing speed and costs. There is a flow of information in the system based on the external inputs, that ultimately results in a response based on analysed data points by AI and ML algorithms. Download Complete Research

AIoT adoption – Connected enterprise strategy

The 5 step enterprise strategy include the following:

  1. Define AIoT Vision, scope, goals and objectives
  2. Assess IoT Capability, identify AIoT devices & systems
  3. Define Connected enterprise System Architecture
  4. AIoT System Development & Pilot implementation
  5. Enterprise – wide implementation

Use cases, challenges and key players

Use Cases for following domains are discussed:

  • Manufacturing: Employee safety, Inventory management, Collaborative robots (Cobots), AI-driven product design
  • Smart Homes: Home Security, Health and Wellness, Child and Elderly Care, Home Automation
  • Healthcare: Predictive Healthcare, Smart Wearables
  • Automobile: Autonomous Vehicles, Smart Traffic Management, Connected Car Services
  • Retail: Smart Shelves, Customer Experience, Ambient Commerce
  • BFSI: Fraud Detection, Personalized Financial Services

Implementing a complex system like AIoT requires careful planning, collaboration, and attention to detail. Data management, privacy concerns, and integration with various systems can pose significant obstacles to successful implementation.

The AIoT space is dominated by key players such as IBM, Microsoft, Siemens, GE, Cisco, Huawei, ABB, Bosch, SAP, and Honeywell. Download Complete Research

Credits
Author@lab45: Anju James
Contributing Authors@lab45: Hussain S Nayak, Nagendra Singh

Latest stories

Digital sovereignty rising: The emergence of decentralized identity

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14:33 Minutes The average duration of a captivating reports.

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Primers: Primers are quick short form business reports that educate leaders on key emerging technologies.

Digital sovereignty rising: The emergence of decentralized identity

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Digital identity systems have evolved and continue to evolve. They are core to our interactions with the digital world and have made great strides in both security and convenience. However, the privacy and data-use consent of identity-holders remain problematic.

What's Inside

  1. History of digital identity
  2. Web 3.0 and a new digital identity
  3. User & organization benefits
  4. DID obstacles to adoption
  5. Market and product outlook

History of digital identity

We have seen Digital Identity evolve from the silo identity model to the federated identity model. The current systems reside at a very low trust level, with over 93% of users distrusting social media platform’s digital custodianship. We believe the next stage of evolution will be Decentralized Identity

Web 3.0 and a new digital identity

Web 3.0, the internet’s next evolution aims for a decentralized interconnected and intelligent web. It aims for decentralized, peer-to-peer networks for secure, trustless transactions— without intermediaries. Unlike today's static web that does not adapt to the needs of its users, Web 3.0 will be dynamic and interactive, leveraging AI and blockchain to personalize, adapt, and democratize the internet. As user identity is crucial in Web3, DID will be foundational. We explain the ecosystem and functionality of the DID network. Download Complete Research

User and organizational benefits

User benefits include credential forgery prevention, password-free authentication, spam prevention and many others. While Organizations will benefit from operational cost reduction and security cost reduction, enhanced user experience thereby improving the brand. Organizations must however use a phased approach to implement, which is explained.

DID obstacles to adoption

We identify four key obstacles that present themselves and what organizations can do to overcome them.

Market and product outlook

The global decentralized identity market was valued at $285 million in 2022 and is expected to grow at a CAGR of 88.7% over the next 5 years. We evaluate top players and products in the market and how they have helped the technology evolve. Download Complete Research

Credits
Author@lab45: Sujay Shivram, Abhigyan Malik

Top trending insights

Generative AI startups: Landscape & trends

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12:49 Minutes The average duration of a captivating reports.

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Primers: Primers are quick short form business reports that educate leaders on key emerging technologies.

Generative AI startups: Landscape & trends

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Generative AI is forming a new economic ecosystem, reshaping the behaviour of key players in the IT industry, generating opportunities for super-scalers, and unveiling numerous niches for startups. The outlines of this new IT landscape are emerging, prompting a closer examination.

What's inside

  1. Introduction: The rise & impact of generative AI
  2. The technology & business stack of GenAI
  3. Business niches for GenAI startups
  4. Future trends in GenAI
  5. Appendix 1: Reality & expectations of GenAI
  6. Appendix 2: Startups across the GenAI tech stack

Introduction: The rise & impact of generative AI

Generative AI has caused significant disruption, expanding its offerings and services well beyond traditional AI domains. This has led to an explosion of potential use cases for customers who aren't AI experts. Unlike before, customers no longer require a team of AI experts, curated data, or precisely measurable outcomes to adopt AI tool and gain immediate benefits. The interaction with GenAI is so seamless and intuitive that the onboarding for new customers is frictionless, eliminating barriers to adoption and facilitating rapid technology spread. The high variability in potential inputs and priming of generative models allows for a diverse range of applications impacting nearly every imaginable aspect of people activities. This is a foundation of a new era of Artificial Intelligence.

In this primer we leveraged our knowledge of 50+ GenAI-related and VC-backed startups to reconstruct the technological stack of the forming GenAI space.

The technology & business stack of GenAI

Large tech companies are leveraging their existing technological and capital advantages to create the framework for the GenAI market landscape, which we are going to explore in this section.

While offering of the LLMs on the current scale and heavy focus on unstructured data are somewhat new, the other elements of the tech stack closely mirror those needed for any large computational modeling. Established companies in the field of traditional AI are at an advantage, as they can expand and repurpose preexisting software, infrastructure, and services. Download Complete Research

Business niches for GenAI startups

While large players are occupying a sizable portion of the GenAI tech stack, there remains more than enough room for GenAI startups to flourish. The landscape of AI and ML is continuously evolving, with new startups, technologies, and methodologies emerging regularly.

Bottom-right (AIOps): Here, startups may offer tools for easier adoption of LLMs, facilitating the initial process of customizing and implementing these models.

Ascending (Integration): Moving upwards represents the process of integrating LLMs into various applications and business operations. Startups could offer integration services, templates, or frameworks to streamline this, or build an entire end to end app for a selected market niche.

Moving left (Service platforms): As we move leftwards, the focus shifts from core LLM functionality to auxiliary services. This could range from platforms offering specialized training data, to marketplaces for LLM apps, to optimization tools. These firms may automate the need for certain experts.

This taxonomy can serve as a foundational overview for anyone looking to understand the current state of the LLM ecosystem. It’s also worth noting that the landscape of AI and ML is continuously evolving, with new startups, technologies, and methodologies emerging regularly. Let’s inspect each block in greater detail:

Future trends in GenAI

The future of the GenAI landscape is going to be defined by several processes:

  1. Consolidation of major players
  2. Rise in open-source adoption
  3. Surge in service platforms
  4. Expansion of skill marketplaces
  5. Segment-specific applications
  6. Regulatory oversight and standardization

Appendix 1: Reality & expectations of GenAI

While enhancing the users with great capabilities, the LLM-based service is neither a freebie, nor a cornucopia. Each implementation of LLMs carries its own advantages and downsides. In this section of the Appendix, we discuss what can and cannot be realistically expected from a GenAI model in each of the most popular use cases.

We start with primary properties of a pre-trained LLM model, underlying its strong sides and functionalities as well as build-in flaws. And we move to the current ways of augment LLM model to work around the flaws. Download Complete Research

Appendix 2: Startups across the GenAI tech stack

The table of 78 startups we have based our analysis on is presented in this section.
The states of startups are set to the August of 2023.

Credits
Author@lab45: Rinat Sergeev

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