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EMBEDDABLE AI SET TO CAPTURE 36% OF THE $1 TRILLION AI MARKET BY 2030

Sabine VanderLinden


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Scouting for Growth

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16 min read
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Oct 6, 2024

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Listen to this story on #beyondtechfrontiers #btf on your favorite podcast
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Hello everyone!

Let’s start this blog with Sophia’s morning routine.

As many of us are nowadays, Sophia unlocked her iPhone to a flurry of
personalized notifications first thing when she wakes up.

As she walked out of the door, her mobile device alerted her that traffic on her
usual route to work was heavier than average due to an accident and suggested an
alternative path. Surprised, she tapped on the notification to see a real-time
map highlighting the new route.



On the subway, Sophia finds a seat and starts scrolling through her photos. The
iPhone has automatically created a photo album titled “Weekend Hike,” compiling
images from her recent trip to the mountains. It even suggests sharing the album
with her closest friend, Mia, who appears in many of the photos.

“How does it know?” Sophia wondered.

The answer lay in the embeddable AI technologies within her iPhone. Unlike
traditional AI, which relies heavily on cloud computing, her device processes
data directly on the “edge” or the hardware in real-time. This meant faster
responses, enhanced privacy, and a more intuitive user experience.

At work, she received a message in Spanish from a client. Although she didn’t
speak the language fluently, her iPhone’s AI-powered translation feature
seamlessly converted the text to English. She replied, and the AI translated her
message back to Spanish before sending it.

Sophia marveled at how seamlessly her iPhone anticipated her needs and
simplified her day. The embeddable AI was not just a wonderful feature for
day-to-day activity; it was like a personal virtual assistant integrated into
her daily life.

Sophia’s experiences highlight the growing presence of embeddable AI in our
daily lives.


AN OVERVIEW OF THE FAST GROWING AI MARKET

The global artificial intelligence market is on a meteoric rise. It is projected
to reach an astounding $1 trillion by 2030. A significant portion of this growth
is attributed to embeddable AI, which is set to capture 36% of the market share.
Remarkable advancements in AI capabilities drive this surge such as the
increasing adoption of AI across various industries, the strategic development
of governmental and national AI strategies.



The AI industry is characterized by a high degree of innovation, with both tech
giants and nimble startups investing heavily in AI research and development.
These investments are not just about staying ahead of the curve. They are about
redefining the curve itself. As AI continues to evolve, it is becoming an
integral part of our daily lives, enhancing everything from personal devices to
complex industrial systems.

A 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, embedding AI directly into their mobile
app to enhance security, personalize customer interactions, and streamline
operations.

 * Advanced Fraud Detection: Starling Bank utilizes AI algorithms to monitor
   transactions in real time, swiftly identifying unusual patterns that may
   indicate fraudulent activity. By analyzing customer behavior and
   transactional data, the AI system can alert users to potential scams,
   enhancing security and building trust.
 * Personalized Financial Insights: The bank’s AI analyzes individual spending
   habits to provide tailored insights and budgeting tools. Customers receive
   personalized notifications and suggestions that help them manage their
   finances more effectively, such as prompts to save money or alerts about
   upcoming bills.
 * Educational Initiatives: Recognizing the importance of customer awareness in
   combating fraud, Starling Bank launched the “Safe Phrases” campaign. This
   initiative educates customers on common language used by scammers and
   provides guidance on how to respond to suspicious messages or calls. By
   embedding AI-driven insights into these educational efforts, the bank
   empowers customers to protect themselves in an increasingly digital world.
 * Enhancing Customer Support: While Starling Bank prides itself on
   human-centric customer service, AI supports the team by handling routine
   inquiries and providing quick answers to common questions. This allows
   customer service representatives to focus on more complex issues and offer a
   higher level of personalized assistance.

Through the strategic integration of embeddable AI, Starling Bank not only
enhances operational efficiency but also 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.


DEFINING EMBEDDABLE AI

Embeddable AI, also called 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 allows for real-time data processing, decision-making,
and automation without relying on external cloud services. This approach enables
businesses to enhance their products with AI integration, such as natural
language processing, machine learning, and predictive analytics, making them
smarter and more efficient.

These investments are not just about staying ahead of the curve but about
redefining the curve itself. AI innovation is driving substantial growth in the
tech ecosystem, with significant investments and corporate commitments, such as
Salesforce’s funding towards AI initiatives and establishing centers for
collaboration and skills development.

> Embeddable AI — we coined this term a couple of years ago before the
> generative AI buzz really took off. It was really focused on providing
> fit-for-purpose technology that companies like independent software vendors
> (ISVs) could use and embed or incorporate into the products and technologies
> they brought to market. Today, embeddable AI has evolved into not only the
> ability for a company to leverage something like natural language processing
> (NLP) or speech-to-text or text-to-speech technologies but also now has
> expanded when we introduced watsonx.” Dawn Herndo, Vice president, EMEA AI and
> build partnerships, IBM



There are many market players on the market, and one recognized player in that
space is IBM with its embeddable AI portfolio, which includes enterprise-grade
AI products such as the watsonx AI and data platform, APIs, libraries, and
applications, that are embedded within commercial solutions to enhance their
functionality within applications bringing:

 * Real-Time Processing: Immediate data analysis and response without reliance
   on cloud connectivity.
 * Enhanced Privacy: Data is processed locally, minimizing the risks associated
   with transmitting sensitive information.
 * Improved User Experience: Devices can anticipate user needs and provide
   personalized services seamlessly.

> Embeddable AI is the first-of-a-kind suite of IBM core AI technologies that
> can be easily embeddable within enterprise applications to serve a variety of
> use cases. IBM


ORIGINS OF EMBEDDABLE AI

As I dove into the topic, I learned that the concept of embeddable AI has
evolved from the broader field of artificial intelligence and edge computing.
The increasing adoption of IoT 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, IoT devices, and robotics.

With advancements in AI technologies and the increasing demand for intelligent
solutions across various industries, the global artificial intelligence industry
is getting more and more substantial with the demand for AI-enabled services to
ease every life by removing repetition.

Estimates suggest that the AI market was valued at approximately $196.63 billion
in 2023 and is projected to grow at a CAGR of 36.6% from 2024 to 2030. Other
projections indicate the market could reach a little less than $1.4 trillion by
2030.



From a global embedded AI market viewpoint, the projection shows that the market
from embedded AI will be worth USD 9.5 billion in 2024 and is expected to reach
USD 36.2 billion by 2034, growing at a CAGR of 14.3% during the forecast period
6. The market experienced a 17.8% CAGR between 2019 and 2023, driven by the
proliferation of IoT devices and the need for real-time data processing.

As noted above, there are many key players in the embedded AI market space.
Those big tech players include the usual suspects: IBM Corporation, Microsoft
Corporation, Google LLC, Qualcomm Inc., Oracle Corporation, Siemens AG, NVIDIA
Corporation, Intel Corporation, and Amazon Web Services, Inc. among the few.
These companies are at the forefront of innovation, continuously enhancing their
AI capabilities to serve their users and maintain a competitive edge.

The market that will invest significantly in embeddable AI tech includes the
BFSI (Banking, Financial Services, and Insurance) sector, which is projected to
lead the embedded AI market with a CAGR of 14.1% through 2034. Other significant
sectors include manufacturing, healthcare, and retail, where embedded AI is used
for intelligent automation, process optimization, predictive analytics, and
enhancing customer experiences.


AI MARKET TRENDS

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, personalised 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 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 the
capabilities of AI 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 rate of failure 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.

An area of great excitement is the shift towards personalized medicine by
enabling the analysis of individual patient data at an unprecedented scale. By
integrating information from genomics, proteomics, electronic health records,
and lifestyle data, 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 overall patient care. For example, AI models can predict a patient’s
response to a particular cancer therapy based on their genetic mutations,
leading to more effective and targeted treatments.


AI MARKET SEGMENTATION

The AI market is vast and diverse, 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 the 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.

In terms of technology, the market is segmented into deep learning, machine
learning, natural language processing, machine vision, and generative AI. Each
of these technologies 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,
BFSI (Banking, Financial Services, and 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.


GENERATIVE AI-DRIVEN DECISIONING

Let’s now connect embeddable AI with AI-driven decisions. AI-driven decisioning
refers to the use of 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 risk of lending. 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 such as the smartphone story above. The demand for enhanced user
experiences and the ability to process and analyze data in real-time are k sey
drivers of this trend.

 * Smartphones and Wearables would include personal assistants, health
   monitoring, and personalized recommendations to enhance user experiences.
 * Home Automation Systems would include intelligent thermostats, security
   systems, and voice-activated assistants to streamline daily tasks.
 * Enhanced User Experiences would include devices like Sophia’s iPhone,
   anticipating user needs and providing convenience and efficiency.

Think about Waymo, for instance, which exemplifies the application of AI-driven
decision intelligence, crucial for navigating complex environments and ensuring
passenger safety. Waymo’s vehicles are equipped with an array of sensors,
including LiDAR, radar, and high-resolution cameras, which work together to
create a comprehensive, 360-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 refine 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.


IBM RESEARCH ON EMBEDDABLE AI INNOVATION

IBM has significantly contributed to the field of embeddable AI as well as
AI-driven Decision Intelligence through its research and development efforts.
IBM’s embeddable AI portfolio for instance includes Watson APIs and applications
like IBM watsonx Assistant, IBM Watson Discovery, IBM Instana Observability, and
IBM Maximo Visual Inspection 20. IBM Research has also introduced new software
libraries that can be run across various environments, including public clouds,
on-premises, and at the edge.

In addition to the above, 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.

> We focus on trust, transparency, openness, and governance. IBM is
> differentiated in the market because we believe that data should be trusted
> and that you should know how to use it. And if you are a company and you’re
> working with our AI technology, you own your data. We don’t own your data. We
> don’t use your data. You use it for the purposes that you need to accomplish,
> again, to drive those growth outcomes in your business. We also have a very
> keen focus on governance and risk management and compliance — not only of the
> data but how you use AI technology inside your organization or enterprise.”
> Dawn Herndo, Vice president, EMEA AI and build partnerships, IBM



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 then 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.

Unsurprisingly, 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 needing to train a new model from scratch for each task. By fine-tuning
GPT-4 with specific data, it becomes highly adept at the desired application,
saving time and computational resources.


IBM watsonx

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.

> We were focused on augmenting or supplementing human expertise and enabling
> humans to be more productive in what they do. So that’s a key aspect. It means
> that we have a different way of working and a different way to provide and
> drive productivity. So, people now must focus on their skills in AI. This is
> going to be super important going forward as well. Dawn Herndo, Vice
> president, EMEA AI and build partnerships, IBM


AI MARKET DRIVERS AND RESTRAINTS

It is important to remember that 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. Concerns around data privacy and security 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. The development of national AI strategies and regulations around AI
adoption are 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.

> We know that the AI technology space is rapidly changing and evolving, getting
> better and more secure and sometimes less secure. With rapid change and with
> so much unknown in the AI technology space, you have to protect your company…
> So governance as a whole and some of the regulations that are going to come
> out in the future, especially here in the EU, are going to be very
> instrumental to the design and to the way that you incorporate AI into your
> business in the future. Dawn Herndo, Vice president, EMEA AI and build
> partnerships, IBM

By understanding these drivers and restraints, organizations can better navigate
the complex AI landscape and leverage its potential to drive innovation and
growth.




PREPARING FOR THE FUTURE: EMBRACING THE FUTURE OF EMBEDDABLE AI IN THE AI MARKET

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.

> When we talk about what's the future of technology, what's the future of AI,
> it’s designing these new products and these new software platforms that are
> going to be ready for any type of consumption. If I want to be able to offer
> my customers a software experience that is completely mobile and only
> accessible on a mobile device, I can certainly do that. Or if I want to offer
> them the choice of any platform and any way to interact with the software, we
> can do that too. Dawn Herndo, Vice president, EMEA AI and build partnerships,
> IBM

Here are the key strategies to prepare effectively:

 1. Invest in Continuous Research and Development: Organizations must allocate
    resources to ongoing R&D efforts to stay ahead of technological
    advancements. This commitment ensures the development of innovative AI
    solutions that push the boundaries of what’s possible, driving competitive
    advantage across industries.
 2. Prioritize Data Privacy and Security: As AI systems become more embedded in
    our lives, safeguarding sensitive information is paramount. Organizations
    must implement robust data privacy and security measures that comply with
    regulatory requirements, fostering trust and confidence among users.
 3. Adopt Flexible AI Models: Leveraging adaptable, fit-for-purpose AI models
    allows businesses to integrate intelligent capabilities seamlessly within
    their applications. This flexibility enhances user experiences and
    operational efficiency, positioning organizations as leaders in their
    fields.

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.

> IBM builds AI for business. It doesn’t matter the size of the business or the
> size of the organization. Or if you’re one department inside of an
> organization that’s very large, there is an opportunity for us to partner
> together and work together in several different aspects. Dawn Herndo, Vice
> president, EMEA AI and build partnerships, IBM



With the embeddable AI market projected to capture 36% of the $1 trillion AI
market by 2030, 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.

> My top tip is to get started. Try it. You’d be amazed at what you’ll find and
> how, frankly, easy it is to use today. Dawn Herndo, Vice president, EMEA AI
> and build partnerships, IBM


Dawn and Sabine at the IBM office on the London South Bank



ABOUT DAWN HERNDON

Dawn is an experienced global business leader with over 25 years at IBM,
showcasing expertise across various functional areas, general management, and
building strategic partnerships. Currently serving as the IBM Vice President of
EMEA Build Ecosystem and AI Partnerships, Dawn is at the forefront of the
evolution of AI, focusing on embedding AI and watsonx. She spearheads the
development of strategic partnerships that drive innovation and deliver value
for organizations across industries and market segments.




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WRITTEN BY SABINE VANDERLINDEN


134 Followers
·Editor for

Scouting for Growth

🔮 Tech Maven in #FinTech & #InsurTech. ✍️ Unpacking corporate venturing,
ethical AI & startup synergy. 🚀 CEO @AlchemyCrew. 🎙️ #ScoutingForGrowth Host.

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