Embeddable AI: Market Trends, Real-World Applications, and Future Prospects
This episode explores the realm of embeddable AI, beginning with an introduction to its everyday applications and a global AI market overview. It highlights embeddable AI in action, featuring contributions from Starling Bank and IBM, and traces its evolution. The discussion delves into AI's impact across various market segments, including consumer tech, and AI-driven decision-making in life sciences. The rise of smart devices, exemplified by Waymo, and the exploration of AI foundation models are covered. The episode also navigates AI market drivers and challenges, provides best practices for adopting embeddable AI, and offers insights into the future of AI. Concludes with closing remarks and a teaser for the next episode.
Chapters:
0:00
Introduction to embeddable AI and everyday applications
1:39
Global AI market overview and trends
2:15
Embeddable AI in action: Starling Bank and IBM's contributions
4:15
Understanding embeddable AI and its evolution
6:38
AI's impact on various market segments and consumer tech
9:15
AI-driven decisioning in life sciences and beyond
12:06
Waymo and the rise of smart device adoption
14:00
Exploring foundation models in AI
15:28
Navigating the AI market: Drivers and challenges
17:28
Best practices for embeddable AI adoption
19:06
The future of AI and concluding thoughts
21:01
Closing remarks and next episode teaser
Key Points:
- Embeddable AI processes data directly on the hardware in real-time, offering faster responses, enhanced privacy, and a more intuitive user experience.
- The AI market is projected to reach one trillion dollars by 2030, with embeddable AI expected to capture thirty-six percent of the market share.
- To effectively leverage the transformative potential of embeddable AI, organizations must prioritize continuous research and development, data privacy, and flexible AI models.
Transcript:
Hello everyone! Welcome to another exciting episode of Beyond Tech Frontiers, where we dive deep into the mechanics of disruptive innovation, market trends, the future of work, and ethical tech. I’m your host, Sabine VanderLinden. Today, we’re exploring a fascinating topic that’s set to redefine various industries: Embeddable AI.
Let's start with a story that might sound familiar to many of you. Meet Sophia. Her morning starts like many of ours do—by unlocking her iPhone to a slew of personalized notifications. As she walks out the door, her phone alerts her that traffic on her usual route is heavier than average due to an accident and suggests an alternative path. On her way to work, she receives a message in Spanish from a client. Although she doesn’t speak the language fluently, her iPhone’s AI-powered translation feature seamlessly converts the text to English. She replies, and the AI translates her message back to Spanish before sending it.
Sophia marvels at how seamlessly her iPhone anticipates her needs and simplifies her day. This is all thanks to embeddable AI technologies. Unlike traditional AI, which relies heavily on cloud computing, embeddable AI processes data directly on the hardware in real-time. This means faster responses, enhanced privacy, and a more intuitive user experience. It’s like having a personal virtual assistant integrated into your daily life.
Now, let’s zoom out and look more at the AI market. The global artificial intelligence market is on a meteoric rise and is projected to reach one trillion dollars by 2030. A significant portion of this growth is attributed to embeddable AI, which is set to capture thirty-six percent of the market share. Remarkable advancements in AI capabilities, the increasing adoption of AI across various industries, and the strategic development of governmental and national AI strategies are driving this surge.
One prime example of embeddable AI in action is Starling Bank, a UK-based digital bank founded by Anne Boden. Starling Bank leverages AI technologies to revolutionize the banking experience. They embed AI directly into their mobile app to enhance security, personalize customer interactions, and streamline operations.
For instance, Starling Bank uses AI algorithms to monitor transactions in real-time, swiftly identifying unusual patterns that may indicate fraudulent activity. The AI system can alert users to potential scams by analyzing customer behavior and transactional data, enhancing security and building trust. The bank’s AI also analyzes individual spending habits to provide tailored insights and budgeting tools, helping customers manage their finances more effectively.
In addition, Starling Bank launched the “Safe Phrases” campaign to educate customers on common language used by scammers and provide guidance on how to respond to suspicious messages or calls. The bank empowers customers to protect themselves in an increasingly digital world by embedding AI-driven insights into these educational efforts. While Starling Bank prides itself on human-centric customer service, AI supports the team by handling routine inquiries, allowing customer service representatives to focus on complex issues and offer personalized assistance.
Through the strategic integration of embeddable AI, Starling Bank enhances operational efficiency and significantly improves customer satisfaction. Their innovative approach sets new standards in the financial industry, demonstrating how AI can be seamlessly incorporated to provide a more secure, personalized, and responsive banking experience.
So, what exactly is embeddable AI? Embeddable AI, or embedded AI, refers to artificial intelligence technologies that can be integrated directly into applications, devices, or systems, allowing them to perform intelligent tasks without requiring separate AI infrastructure. This enables real-time data processing, decision-making, and automation without relying on external cloud services. Essentially, it makes products smarter and more efficient by incorporating AI capabilities like natural language processing, machine learning, and predictive analytics.
IBM is a recognized leader in this space with its embeddable AI portfolio, which includes enterprise-grade AI products such as the Watsonx AI and data platform, APIs, libraries, and applications. These are embedded within commercial solutions to enhance functionality, bringing real-time processing, enhanced privacy, and improved user experiences.
The concept of embeddable AI has evolved from the broader field of artificial intelligence and edge computing. The increasing adoption of Internet of Things devices and advancements in AI algorithms optimized for edge computing have significantly contributed to developing embeddable AI solutions. This evolution is driven by the need for low-latency processing and real-time decision-making in applications such as autonomous vehicles, Internet of Things devices, and robotics.
Projections suggest that the AI market was valued at approximately one hundred ninety-six point six three billion dollars in two thousand twenty-three and is expected to grow at a compound annual growth rate of thirty-six point six percent from two thousand twenty-four to two thousand thirty. Other estimates indicate the market could reach a little less than one point four trillion dollars by two thousand thirty. From a global embedded AI market viewpoint, projections show that the market will be worth nine point five billion dollars in two thousand twenty-four and is expected to reach thirty-six point two billion dollars by two thousand thirty-four, growing at a compound annual growth rate of fourteen point three percent during the forecast period.
The AI market is a hotbed of innovation, witnessing several transformative trends. One of the most notable is the increasing adoption of generative AI, which is revolutionizing how we create and interact with content. Generative AI is becoming a powerful tool that is reshaping industries by enabling the creation of new, original content, personalized products, and services with minimal human intervention.
Another significant trend is the growing importance of AI expertise. As AI technology becomes more sophisticated, the demand for skilled professionals who can develop, implement, and manage AI systems is skyrocketing. This expertise is crucial for integrating AI technology into various industries, from healthcare to finance.
The life sciences industry, for example, is on the brink of a significant transformation powered by artificial intelligence. AI technologies are set to revolutionize various facets of this sector, from accelerating drug discovery to personalizing patient care and enhancing risk management practices. By harnessing AI's capabilities in data analysis, pattern recognition, and predictive modeling, life sciences organizations can drive innovation, improve patient outcomes, and optimize operational efficiencies.
One of the most profound impacts of AI in life sciences is in drug discovery and development. Traditional methods are often time-consuming and costly, with a high failure rate during clinical trials. AI algorithms can analyze vast amounts of biochemical and genetic data to identify potential drug candidates more efficiently. Machine learning models can predict how different compounds will interact with biological targets, enabling researchers to focus on the most promising options. This accelerates the development pipeline, reduces costs, and increases the likelihood of successful outcomes.
Another area of great excitement is the shift towards personalized medicine. By enabling the analysis of individual patient data at an unprecedented scale, AI systems can help develop customized treatment plans tailored to a patient’s unique genetic makeup and health profile. This personalized approach can improve treatment efficacy, minimize adverse effects, and enhance patient care.
The AI market is vast and diverse, and it has been segmented into several categories to better understand its scope and applications. The solution segment includes hardware, software, and services, each playing a crucial role in the AI ecosystem. Hardware encompasses physical components like processors and sensors, while software includes the algorithms and applications that drive AI functionality. Services involve the support and maintenance required to keep AI systems running smoothly.
The market is segmented into deep learning, machine learning, natural language processing, machine vision, and generative AI. Each technology has unique capabilities and applications, from understanding human language to recognizing images and generating new content.
The end-user segment is equally diverse, covering industries such as healthcare, banking, financial services, insurance, law, retail, advertising and media, automotive and transportation, agriculture, and manufacturing. Each industry leverages AI to enhance efficiency, improve decision-making, and deliver better outcomes.
Let's now connect embeddable AI with AI-driven decisions. AI-driven decisioning uses artificial intelligence algorithms and models to automate and enhance decision-making processes. Instead of relying solely on human judgment, AI-driven systems analyze large volumes of data to identify patterns, make predictions, and arrive at informed decisions quickly and consistently. This approach leverages machine learning and data analysis to improve the accuracy and efficiency of decisions across various applications.
Imagine a banking system that uses AI-driven decisioning to approve loan applications. The AI analyzes an applicant’s credit history, income, employment status, and other relevant data to determine the lending risk. Based on this analysis, the system can make immediate decisions on whether to approve or decline the loan, improving speed and consistency while reducing the potential for human error or bias.
AI-driven decisioning involves using AI algorithms to make automated decisions based on data analysis. This critical component of embeddable AI enables real-time decision-making in various applications such as autonomous vehicles, smart sensors, and industrial machinery. AI-driven decisioning enhances such products’ performance, efficiency, and functionality.
Consumers and users increasingly adopt smart devices with embedded AI capabilities, like the smartphone story we discussed earlier. The demand for enhanced user experiences and the ability to process and analyze data in real time are key drivers of this trend. Smartphones and wearables, for instance, include personal assistants, health monitoring, and personalized recommendations to enhance user experiences. Home automation systems feature intelligent thermostats, security systems, and voice-activated assistants to streamline daily tasks.
Consider Waymo, for example, which exemplifies the application of AI-driven decision intelligence. Waymo’s vehicles are equipped with various sensors, including LiDAR, radar, and high-resolution cameras, which work together to create a comprehensive, three-hundred-sixty-degree understanding of their surroundings. The AI system detects and classifies objects like pedestrians and other vehicles and predicts their future movements by analyzing speed and behavior patterns. By computing optimal driving trajectories that adhere to traffic laws and account for real-time conditions, Waymo’s technology enables vehicles to make split-second decisions in dynamic situations.
In addition, continuous learning from millions of miles driven and extensive simulation testing refines its decision-making capabilities, significantly reducing the potential for human error and enhancing overall safety. As this technology advances, it promises to reshape transportation by improving traffic efficiency and accessibility while also addressing ethical considerations and regulatory challenges inherent in autonomous driving.
Through its research and development efforts, IBM has significantly contributed to the field of embeddable AI and AI-driven decision intelligence. IBM’s embeddable AI portfolio includes Watson APIs and applications like IBM Watsonx Assistant, IBM Watson Discovery, IBM Instana Observability, and IBM Maximo Visual Inspection. IBM Research has also introduced new software libraries that can be run across various environments, including public clouds, on-premises, and at the edge.
Watsonx is IBM’s next-generation enterprise studio for AI builders. It combines traditional machine learning and new generative AI capabilities powered by foundation models. IBM focuses on trust, transparency, openness, and governance. They believe that data should be trusted, and companies should know how to use it. You own your data if you’re working with IBM’s AI technology. IBM doesn’t own your data or use it; you use it for your purposes to drive growth outcomes in your business. IBM also focuses on governance, risk management, and compliance, not only of the data but also on how you use AI technology inside your organization or enterprise.
Foundation models are large-scale AI models trained on vast and diverse datasets that can be adapted to perform various tasks. These models serve as a “foundation” because they capture extensive knowledge during their initial training, which can be fine-tuned for specific applications with relatively little additional data. Foundation models can understand complex patterns in data, such as language, images, or other types of information, making them highly versatile in various domains.
A well-known example of a foundation model is a language model like GPT-4. Trained on an even larger and more diverse dataset than its predecessors, GPT-4 has significantly enhanced understanding of language nuances, context, and reasoning abilities. Developers can use GPT-4 to build advanced applications for tasks such as translating languages, summarizing complex documents, generating creative writing, or answering detailed questions without training a new model from scratch for each task. Fine-tuning GPT-4 with specific data makes it highly adept at the desired application, saving time and computational resources.
Watsonx does the same for either billion-dollar businesses or small one-founder companies. It enables organizations to embed AI technology into their commercial solutions, providing flexibility to build and deploy on any cloud in a containerized environment. Watsonx can be a real differentiator for businesses, allowing them to leverage AI to enhance their products and services.
IBM aims to augment human expertise and enable humans to be more productive in their work. This means a different way of working and driving productivity, with a focus on AI skills. This will be super important in the future.
Several powerful drivers propel the AI market right now. The increasing availability of data is a primary catalyst, providing the raw material that AI systems need to learn and improve. Advancements in AI capabilities, such as more sophisticated algorithms and faster processing speeds, are also driving the market forward. Additionally, there is a growing demand for automation and efficiency across industries, making AI an attractive solution.
However, the market is not without its challenges. A significant restraint is the lack of standardization, which can hinder the development and deployment of AI systems. Another barrier is the need for substantial investment in AI research and development, as not all organizations have the resources to invest heavily in this area. Data privacy and security concerns also pose significant challenges, as AI systems often require access to sensitive information.
Government agencies and regulatory bodies play a crucial role in shaping the AI market. Developing national AI strategies and regulations around AI adoption is essential for ensuring that AI technologies are used responsibly and ethically. These regulations help to build trust and confidence among users, paving the way for broader AI adoption.
By understanding these drivers and restraints, organizations can better navigate the complex AI landscape and leverage its potential to drive innovation and growth.
As we stand on the brink of a technological revolution, embeddable AI emerges as a transformative force poised to reshape industries and enhance our daily lives. To thrive in this evolving landscape, organizations of all sizes must take decisive action and embrace the unparalleled opportunities that embeddable AI presents.
Here are the key strategies to prepare effectively: Invest in continuous research and development to stay ahead of technological advancements. Prioritize data privacy and security to safeguard sensitive information. Adopt flexible AI models to integrate intelligent capabilities seamlessly within applications.
By embracing these strategies, businesses can unlock the full potential of embeddable AI, driving innovation and growth. The seamless integration of AI into devices and systems empowers users like Sophia to experience personalized, efficient, and intelligent interactions that simplify and enrich daily life.
With the embeddable AI market projected to capture thirty-six percent of the one trillion dollar AI market by two thousand thirty, the time to act is now. Organizations that prioritize innovation, data security, and adaptable AI solutions will not only gain significant competitive advantages but also play a pivotal role in shaping the future of intelligent technology.
As we look ahead, the transformative power of embeddable AI invites us to reimagine possibilities. By harnessing its potential, we can create a future where technology seamlessly integrates into our lives, driving progress and improving the human experience.
That’s all for today’s episode of Beyond Tech Frontiers. I hope you found our exploration of embeddable AI as fascinating as I did. If you enjoyed this episode, please subscribe and leave us a review. Join us next time as we continue to delve into the frontiers of technology and innovation. Thank you for listening! Until next time, I’m Sabine VanderLinden, signing off.