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AI Industry Deep Dive
Applied Intelligence Group
As we reach the halfway point of 2022, we’re now living in a world where global developments mean we rely on the power of transformative technologies such as AI, IoT, and Quantum, to provide solutions more than ever. The adoption and continued progression of these technologies are essential across a wide range of industries and geographies, that need support to overcome challenges faced. The implementation and acceleration of transformative technology means projects can move forward at pace, and this is where we will really start to see a change in the current status quo. Individually both AI and IoT are valuable, but the power of these technologies when they come together is where we will continue to see the biggest impact that will make a real difference. Their confluence will turn what were simple solutions into complex, truly impactful offerings that will alter society and businesses for years to come. Individually both AI and IoT are valuable, but the power of these technologies when they come together is where we will continue to see the biggest impact that will make a real difference. Their confluence will turn what were simple solutions into complex, truly impactful offerings that will alter society and businesses for years to come. It's the continued advancements of each of these technologies that will enhance the other. Omdia, our technology research brand, estimates that by 2030 there will be 75 billion IoT devices around the globe, which will provide an unimaginable amount of data to strengthen AI systems. At the same time, AI allows this vast amount of data to be analyzed and acted upon with unprecedented speed. As we move forward, organizations will be looking at how they can gain competitive advantages through leveraging quantum computing to move beyond the limitations that traditional architecture presents; and increasing investment in AI-driven automation. Alongside this, also ensuring they converge Subject Matter Experts with Data Scientists and Engineers throughout various levels of the process. Whilst there is significant momentum in the space, Omdia predicts there are areas that still need to be addressed across 2022 and beyond. These include interoperability with IT existing systems, industry-wide standardization of AI measurement and success; implementing standardized regulation and governance; and a greater drive to improve diversity within the sector.
Jenalea Howell Vice President, Applied Intelligence Group at Informa Tech
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Beyond automation: How AI is fuelling the next industrial revolution
CONTENTS
Introduction
Transport
Cybersecurity
Robotics
Telecoms
Conclusion
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Introduction Transport: Improving transport efficiency and safety with AI Cybersecurity: How AI is being used to fight cybercrime Robotics: Shaping the new industrial revolution with AI-based Robotics Transforming Telecoms: Reducing cost, delivering engagement with AI Conclusion Find Out More
Contents
Beyond its standalone capabilities, AI is acting as a powerful catalyst, amplifying the impact of technologies such as the Internet of Things (IoT), robotics and big data. In turn, this is giving rise to innovative solutions that are reshaping industries, enhancing efficiencies as well as creating unprecedented business opportunities. On the factory floor and in warehouses across the US, AI-driven insights from IoT sensors are increasingly helping organizations to improve productivity, cut costs and monitor the supply chain. In smart cities, the technology can help to manage traffic flow and reduce the number of road traffic accidents. And in retail and telecoms, AI-driven chatbots and virtual assistants are helping to improve customer service and reduce churn.
This report not only gives a voice to the underrepresented and unsung heroines in the field of artificial intelligence, but it also provides a framework and direction for businesses, institutions, agencies, governmental organisations and future generations of the evolving society to build a foundation of best practices when it comes to shaping the future, our future.
TRANSPORT
- Market Outlook
AI: The catalyst for industrial transformation
The convergence of artificial intelligence (AI) with other cutting-edge technologies is ushering in a new era of industrial transformation.
HOME
As anyone who works in the AI business will be aware, adoption of AI technology has been phenomenal over the last few years with research firm Omdia revising growth upwards from 100% to 124% for 2024 in its recent Artificial Intelligence Software Market Forecasts. Globally, AI software is predicted to reach $58 billion by 2028, growing at an average rate of 53% CAGR (compound annual growth rate) from 2023.
Indeed many vendors with LLMs (Large Language Models) are seeing ‘triple digit’ revenue growth, reports Omdia, with some tools such as Git Hub CoPilot – a Gen AI coding tool that can speed up developers’ work by 30% - not surprisingly experiencing a surge in adoption. That said, as with any new technology there will be bumps in the road. Adoption of AI will not be at the same rate across all industries and there will be adjustments in the price of AI stocks along the way. Nor will a solution that’s right for one company or industry necessarily be right for another.
Growth of adoption of AI technology over the last few years
+100%-124%
Omdia's Artificial Intelligence Software Market Forecasts
In this report, we will look at some of the industries where GenAI/AI applications have been widely adopted. In particular, we will focus on four sectors: transport, cybersecurity, robotics and telecoms. In each we will look at what’s working well and areas where further development is required to ensure growth. We will also make some predictions for future AI use. Read the full article here: The AI Summit New York 2024
While we see a near-term frenzy in GenAI interest and actual adoption,the prospects for disillusionment are real.
"
BACK TO TOP
Neil Dunay, Principal Forecaster, Omdia
Reasons for this growth include the following factors:
One sector that is being rapidly transformed by AI, including Generative AI, is transportation. According to Precedence Research, the global AI market in transportation was valued at $3 billion in 2022 and is expected to reach $23.11 billion by 2032, representing a CAGR of 22.7% over the forecast period.
Transport:
The findings from this latest study on the AAA Foundation’s work in emerging technologies suggest that ADAS have the potential to transform road safety.
Driving transport growth with AI
This new round of funding will enable Waymo to continue to build the world’s leading autonomous driving company.
Dr David Yang, President and Executive Director of the AAA Foundation.
INTRODUCTION
CYBERSECURITY
Speaker
Rise of AI-powered logistics and supply chain
Regulations imposed by government to ensure vehicle safety
Increased demand for advanced driver assistance systems
Overcoming Industry Challenges
While we see massive growth in the use of AI in the transportation sector, clearly challenges still exist. These include well-documented technical challenges such as the need for high-quality real-world data for training AI models, as well as extremely-powerful hardware to process this data in real time.
Improving transport efficiency and safety with AI
When it comes to transport, it’s fair to say that AVs have attracted more publicity than any other area. In the US, both Waymo and Cruise, subsidiaries of Alphabet (Google) and General Motors respectively, have tested self-driving cars in various cities including Phoenix, Houston, Austin and San Francisco for some time.
Ruth Porat, Outgoing Finance Chief, Alphabet
While autonomous vehicles understandably capture the biggest headlines, it is not the only area within the transportation sector where AI is making inroads. According to Research and Markets, the traffic management market is expected to grow from $42.3 billion to $72.5 million between 2023 and 2028 (representing a CAGR of 11.4%) as more vehicles hit US roads.
Improving Road Safety With AI
AI is revolutionizing road safety. AI-powered Advanced Driver Assistance Systems (ADAS) can accurately identify pedestrians, cyclists, and other vehicles, enabling vehicles to react promptly. By anticipating potential collisions, AI can initiate braking before a human driver can react. It can also detect lane markings and alert drivers if they unintentionally drift. According to the National Safety Council (NSC), ADAS technologies could have prevented or mitigated 1.69 million injuries in the US between 2011-2015 (about 60% of the total number of traffic injuries). The majority of these were achieved thanks to forward collision and lane keeping assist technologies. Going forward, research from the AAA Foundation for Traffic Safety predicts that ADAS could prevent approximately 37 million crashes and 14 million injuries over the next 30 years. “The findings from this latest study on the AAA Foundation’s work in emerging technologies suggest that ADAS have the potential to transform road safety,” said Dr David Yang, President and Executive Director of the AAA Foundation.
But they also include legal, ethical and social concerns which are arguably much more complex to address. For example, one of the biggest issues facing autonomous vehicles (AVs) right now is overcoming understandable public concerns around AI safety.
This requires building public trust through education projects and working with bodies such as Autonomous Vehicle Industry Association (previously known as Self-Driving Coalition for Safer Streets) – a trade association for companies developing AV technologies to improve safety and enhance mobility.
Developing ethical frameworks (see Ethical Decision Making in Autonomous Vehicles: The AV Ethics Project) is also crucial as autonomous vehicles will inevitably face complex ethical dilemmas in their decision making. In addition, developers need to navigate complex regulatory environments and of course ensure sufficient investment for long term commercial success.
AUTONOMOUS VEHICLES
Underlining its commitment to AVs, Alphabet has just announced it is to invest a further $5 billion in the Waymo self-driving car business. “This new round of funding will enable Waymo to continue to build the world’s leading autonomous driving company,” Alphabet’s outgoing finance chief Ruth Porat said in the company’s second-quarter earnings call, adding that Waymo is an “important example” of Alphabet’s long-standing investments. This latest investment follows news that Waymo is planning to expand its Level 4 robotaxi service to include a wider service map in the San Francisco Bay area, the Los Angeles Metropolitan Area (where it began autonomous rides in early 2024) as well as in Austin, Texas where it plans to start transporting the public later this year.
Nor is Waymo the only company investing in autonomous robotaxis that rely on AI technology. Although the official announcement has been delayed to improve its design, Tesla’s much-vaunted ‘robotaxi’ is expected to be officially announced in October. Tesla CEO Elon Musk has claimed the project, which includes the computer self-driving software to operate them and ultimately transform every other Tesla vehicle into a giant autonomous fleet, could take the company’s valuation as high as $30trn – much greater than its current $775bn value.
Finally, ride-hailing service Uber plans to deploy driverless cars on its service as part of a deal with BYD, China’s biggest electric vehicle (EV) manufacturer. BYD and Uber recently unveiled a “strategic partnership” under which Uber drivers will be offered preferential prices and financing rates to help them buy 100,000 BYD EVs. The two businesses also said they would work together on making BYD’s autonomous cars available on Uber in the future.
making cities smarter
In cities such as Los Angeles and New York, traffic management systems are now using AI-powered video surveillance to analyze traffic patterns in real-time, identifying bottlenecks and congestion hotspots and dynamically adjusting timing on traffic lights to improve traffic flow. AI is also being used to detect accidents, breakdowns and other incidents that can cause traffic disruptions.
When it comes to public transport, AI algorithms can optimize routes, schedules, and service levels by analyzing data on passenger demand, traffic patterns, and transit schedules. For example, AI can be used to adjust bus or train schedules in real-time based on traffic patterns or passenger demand.
A study in the Journal of Advanced Transportation, cited by AI surveillance and analytics company Maris, ‘found that AI algorithms reduced average waiting times for passengers by up to 42% compared to a traditional bus scheduling system’. The research also claims that using AI algorithms reduces travel times by up to 8.2%. Numerous public transport systems are now using AI to optimize travel times and improve reliability including the Los Angeles Metro, New York City Transit Authority and San Francisco Municipal Transport Agency.
While autonomous vehicles understandably capture the biggest headlines, it is not the only area within the transportation sector where AI is making inroads. According to Research and Markets, the traffic management market is expected to grow from $42.3 billion to $72.5 billion between 2023 and 2028 (representing a CAGR of 11.4%) as more vehicles hit US roads.
$211.86 billion
Autonomous vehicle market projection by 2023, from $33.41 billion in 2023.
And while General Motors recently announced it was indefinitely suspending its work on Origin (the name of Cruise’s autonomous vehicle which doesn’t have a steering wheel or pedals). Chief Financial Officer Paul Jacobson told The FT that it didn’t rule out a return to the technology in the future. Indeed, according to a recent report from Research and Markets, the autonomous vehicles market is on the cusp of significant expansion, with experts projecting its growth trajectory to climb from $33.41 billion in 2023 to an impressive $211.86 billion by 2032. This represents a CAGR of 22.7% during 2024-2032, underlining the fast-paced adoption and development of autonomous vehicle technologies.
$42.3 billion
TRAFFIC MANAGEMENT MARKET
$72.5 billion
Projected to grow from
in 2023 to
in 2028
Growing demand for autonomous vehicle
Growing demand for smart traffic management systems
Greater focus on improving transportation efficiency
Overcoming industry challenges
When it comes to transport, it’s fair to say that AVs have attracted more publicity than any other area. In the US, both Waymo and Cruise, subsidiaries of Alphabet (Google) and General Motors respectively, have tested self-driving cars in various cities including Phoenix, Houston, Austin and San Francisco for some time. And while General Motors recently announced it was indefinitely suspending its work on Origin (the name of Cruise’s autonomous vehicle which doesn’t have a steering wheel or pedals). Chief Financial Officer Paul Jacobson told The FT that it didn’t rule out a return to the technology in the future. Indeed, according to a recent report from Research and Markets, the autonomous vehicles market is on the cusp of significant expansion, with experts projecting its growth trajectory to climb from $33.41 billion in 2023 to an impressive $211.86 billion by 2032. This represents a CAGR of 22.7% during 2024-2032, underlining the fast-paced adoption and development of autonomous vehicle technologies.
Underlining its commitment to AVs, Alphabet has just announced it is to invest a further $5 billion in the Waymo self-driving car business.
Alphabet’s outgoing finance chief Ruth Porat said in the company’s second-quarter earnings call, adding that Waymo is an “important example” of Alphabet’s long-standing investments. This latest investment follows news that Waymo is planning to expand its Level 4 robotaxi service to include a wider service map in the San Francisco Bay area, the Los Angeles Metropolitan Area (where it began autonomous rides in early 2024) as well as in Austin, Texas where it plans to start transporting the public later this year.
Growing demand for autonomous vehicles Regulations imposed by government to ensure vehicle safety Increased demand for advanced driver assistance systems Rise of AI-powered logistics and supply chain Greater focus on improving transportation efficiency Growing demand for smart traffic management systems
Access an exclusive interview with Dr. Junfeng Jiao, Director, Texas Smart Cities, United States as he discusses the future of smart cities and ethical AI. Read the full article here:
ROBOTICS
For cybersecurity professionals, AI is a ‘double-edge sword’ – representing both a broad challenge as well as massive opportunity for many organizations.
Cybersecurity:
How AI is being used to fight cybercrime
Indeed, in its 2023 Generative AI Cybersecurity Study of over 600 cybersecurity decision makers, Omdia discovered an increasingly symbiotic relationship between Gen AI and cybersecurity.
The survey highlights that generative AI emerges as a force for change within the cybersecurity landscape, characterized by unprecedented potential and formidable challenges.
Maxine Holt , Research Director, Omdia
According to a recent study from British cybersecurity company Dark Trace, 74% of 1,800 security leaders and practitioners surveyed in North America, Latin America, Europe and Asia Pacific reported seeing an impact from AI-powered cybersecurity threats. Furthermore, 87% said that AI-powered threats would continue to trouble their organizations ‘for months and years to come’. In the US, Cybersecurity and Infrastructure Security Agency (CISA) Director Jen Easterly told an audience at the Atlantic Council (April 2023) that popular AI tools like ChatGPT ‘were the biggest issue we’re going to deal with this century’, due to the variety of ways they can be used by cybercriminals and nation states. More recently (May 2024) Easterly said to Axios at the RSA Conference that “Generative AI is not just teaching cyber bad guys new tricks — it's also making it easier for anyone to become a bad guy.” Indeed, organizations face an increasing number of AI-generated threats that they need to protect themselves from. These include the following:
Increasingly sophisticated deepfake audio and video is helping hackers to breach cybersecurity by tricking human beings into believing what they are hearing or seeing is real. One example earlier this year saw a finance worker at a multinational firm in Hong Kong pay out $25 million to fraudsters who were using deep fake technology to pose as C-Suite executives on a video call. In another example, Mark Read, the CEO of the world’s biggest advertising group, WPP, told executives he was the target of an elaborate deepfake scam that involved an artificial intelligence voice clone. Fraudsters created a WhatsApp account with a publicly available image of Read and used it to set up a Microsoft Teams meeting that appeared to be with him and another senior WPP executive, according to an email recently obtained by The Guardian newspaper.
of 1,800 security leaders reported seeing an impact from AI-powered cybersecurity threats
74%
Deepfakes
Even before the advent of Generative AI, social engineering attacks - particularly phishing and spear phishing (targeted) attacks - were a massive problem for organizations. Indeed Santander bank reports that 91% of cyber-attacks start with a phishing email. With Generative AI, however, it has become possible to generate large volumes of highly personalized phishing emails which are often much more convincing than traditional attempts with fewer grammatical errors. As cybersecurity entrepreneur and threat analyser Shomiron Das Gupta told Outlook India Magazine:
of cyber-attacks start with a phishing email
91%
Since a platform like ChatGPT can simulate human-like responses, it can be used to trick people into divulging sensitive information or clicking on malicious links.
Gen AI can be used to automate the creation of evasive malware. By generating diverse code variations, it’s possible to produce malware that bypasses traditional signature-based antivirus detection. Additionally, AI can be used to analyze existing malware samples and identify successful evasion techniques, allowing for the creation of even more sophisticated threats. These AI-generated malwares can be designed to self-mutate or learn from their environment, making detection and response challenging for security teams.
Improving cyber defenses with AI
Just as cybercriminals are using Gen AI technology to deliver more effective attacks, cyber professionals are deploying Gen AI to improve their organization’s defenses. According to Omdia’s Generative AI Cybersecurity Study three-quarters of organizations surveyed identify Gen AI as an ‘invaluable asset in shoring up their security posture’ with few entities planning to ‘maintain a status quo in their cybersecurity portfolios this year.’ “Instead, a majority demand heightened performance from their cybersecurity technology partners, placing a distinct emphasis on the seamless integration of generative AI into product evolution,” said Maxine Holt. Indeed, the Omdia survey claims that three-quarters of CISOs and Heads of Security plan to invest in Gen AI for cybersecurity within the next fiscal year with just over one fifth (21.3%) already using Gen AI regularly within the cybersecurity function. Over two-thirds (68%) are also experimenting or exploring how GenAI could benefit them.
According to a 2022 survey by IBM, organizations that use security AI and automation extensively report an average cost of a data breach at $3.60 million, $1.76 million less than breaches at organizations that don’t use security AI and automation capabilities. This is a 39.3% difference in average breach cost. Organizations with fully deployed security AI and automation were also able to identify and contain a data breach 108 days faster than companies with no security AI and automation deployed.
Below we outline just a few of the ways that Gen AI is helping organizations to anticipate threats before they happen and tackle cybercrime when it does occur.
Proactive security stance
Importantly, Gen AI enables organizations to transition from a reactive to proactive cybersecurity stance. By alerting teams to potential threats based on learned patterns, it can allow pre-emptive actions to be taken before a breach even occurs. It can also create simulations of cyber-attacks that mimic real world behavior and tactics so that teams can test their defenses against various threat vectors and identify any weaknesses in advance of an attack.
As Omdia Principal Analyst Eric Parizio states in Dark Reading:
Proactive Security creates the opportunity for enterprises to consistently and programmatically address the specific circumstances — unknown IT assets, vulnerable software, misconfigurations, and the like — that create opportunities for threats to exploit the extended enterprise environment.
For cybersecurity professionals overwhelmed by the sheer volume of cyber-attacks, Gen AI offers the opportunity to augment their own intelligence and speed up the MTTD/R (Mean Time to Detect and Resolve) security issues. By analyzing network traffic, system logs and user behavior to identify potential anomalies that might indicate a cybersecurity attack, AI powered cybersecurity tools free up security analysts to focus on investigating high security threats. What’s more, unlike humans, AI security systems don’t get tired so they can monitor networks and systems continuously!
Improved detection
Examples of AI-powered cybersecurity platforms include:
CrowdStrike Falcon
Palo Alto Networks Cordex XDR
IBM Security QRadar Advisor with Watson
In the aftermath of an attack, it’s essential for organizations to restore the functionality of their data and systems. They need to recover or replace the lost or corrupted files, fix the vulnerabilities and resume the normal operations. This can be time-consuming, costly, and risky. AI can help by automating the recovery process, using tools such as backup, recovery, and patch management. AI can also be used to test and validate recovery outcomes, using methods such as simulation, verification, and optimization.
HELPING RECOVERY
PHISHING
MALWARE AUTOMATION AND ANTI-VIRUS EVASION
In an experimental project led by researchers at Hyas, a piece of AI-generated malware dubbed BlackMamba was able to bypass cybersecurity technologies including industry leading EDR (Endpoint Detection and Response). Using a large language model (LLM) to create a polymorphic keylogger, the BlackMamba malware mutates every time it runs, enabling it to slip through predictive cybersecurity software.
Weaponizing AI-Powered Systems
Most of these attacks are fairly easy to mount and require minimum knowledge of the AI system and limited adversarial capabilities.
Alina Oprea, Professor, Northeastern University
In addition to the above threats, cybercriminals could potentially target and manipulate AI-powered systems themselves, including autonomous vehicles, chatbots or even critical national infrastructure (CNI). A recent report from the US National Institute of Standards and Technology (NIST), ‘Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations’, examines several different types of attacks that need to be considered when deploying an AI system.
For instance, one example of a poisoning attack would be slipping numerous instances of inappropriate language into conversation records during the Gen AI training phase, so that a chatbot interprets these instances as common enough to use in its own customer interactions. Similarly, abuse attacks involve the insertion of incorrect information into a source, such as a webpage or online document, that an AI then absorbs.
$3.6 million
$1.76 million
Less than breaches at organizations that don't use security AI
Average cost of a data breach at organizations using security AI
CYBERSECURITY:
deep fakes
In the US, Cybersecurity and Infrastructure Security Agency (CISA) Director Jen Easterly told an audience at the Atlantic Council (April 2023) that popular AI tools like ChatGPT ‘were the biggest issue we’re going to deal with this century’, due to the variety of ways they can be used by cybercriminals and nation states. More recently (May 2024) Easterly said to Axios at the RSA Conference that “Generative AI is not just teaching cyber bad guys new tricks — it's also making it easier for anyone to become a bad guy.” Indeed, organizations face an increasing number of AI-generated threats that they need to protect themselves from. These include the following:
According to a recent study from British cybersecurity company Dark Trace, 74% of 1,800 security leaders and practitioners surveyed in North America, Latin America, Europe and Asia Pacific reported seeing an impact from AI-powered cybersecurity threats. Furthermore, 87% said that AI-powered threats would continue to trouble their organizations ‘for months and years to come’.
phishing
Gen AI can be used to automate the creation of evasive malware. By generating diverse code variations, it’s possible to produce malware that bypasses traditional signature-based antivirus detection. Additionally, AI can be used to analyze existing malware samples and identify successful evasion techniques, allowing for the creation of even more sophisticated threats. These AI-generated malwares can be designed to self-mutate or learn from their environment, making detection and response challenging for security teams. In an experimental project led by researchers at Hyas, a piece of AI-generated malware dubbed BlackMamba was able to bypass cybersecurity technologies including industry leading EDR (Endpoint Detection and Response). Using a large language model (LLM) to create a polymorphic keylogger, the BlackMamba malware mutates every time it runs, enabling it to slip through predictive cybersecurity software.
improved detection
helping recovery
Importantly, Gen AI enables organizations to transition from a reactive to proactive cybersecurity stance. By alerting teams to potential threats based on learned patterns, it can allow pre-emptive actions to be taken before a breach even occurs. It can also create simulations of cyber-attacks that mimic real world behavior and tactics so that teams can test their defenses against various threat vectors and identify any weaknesses in advance of an attack. As Omdia Principal Analyst Eric Parizio states in Dark Reading:
CrowdStrike Falcon Palo Alto Networks Cordex XDR IBM Security QRadar Advisor with Watson
For cybersecurity professionals overwhelmed by the sheer volume of cyber-attacks, Gen AI offers the opportunity to augment their own intelligence and speed up the MTTD/R (Mean Time to Detect and Resolve) security issues. By analyzing network traffic, system logs and user behavior to identify potential anomalies that might indicate a cybersecurity attack, AI powered cybersecurity tools free up security analysts to focus on investigating high security threats. What’s more, unlike humans, AI security systems don’t get tired so they can monitor networks and systems continuously! Examples of AI-powered cybersecurity platforms include:
As cyber threats become increasingly sophisticated, the integration of generative AI into cybersecurity strategies means threats can be managed and prevented ahead of potential attacks. Explore the insights behind the use of Generative AI for Enhanced Cybersecurity in the US in our latest article: Unlocking the Future: Harnessing Generative AI for Enhanced Cybersecurity in the US
“
it is hard to find time to address long-term objectives or revamp operational processes when you are always engaged in hand-to-hand combat.
4%
Understandably, faced with overwhelming alert volumes and an ever-evolving threat landscape, most business leaders have up until recently taken a largely reactive approach to cybersecurity challenges.
TELECOMS
Shaping the new industrial revolution with AI-based Robotics
Benefits of AI in Robotics
One industry which is undergoing a massive transformation, thanks in part to AI, is robotics. Indeed, AI in robotics is vital in helping robots perform important tasks, some of which are very repetitive and can lead to errors if done by humans. Figures from Mordor Intelligence predict that the number of industrial robots will reach 518,000 units globally in 2024, while AI in the robotics market is expected to register a CAGR of 29.21% during the forecast period (2024-2029). Benefits of using AI – including Gen AI - in robotics are numerous. However, the main ones are as follows:
518,000 units
the predicted number of industrial robots in 2024
Mordor Intelligence
Enhanced perception
Modern robots rely on a huge number of datasets to train computer vision models that allow them to recognize objects and complete their tasks. Thanks to AI, data from sensors (such as cameras, radar and lidar) can be interpreted more easily. This can help robots to perceive their environment in much greater detail than was previously possible.
Improved decision-making
Thanks to AI, robots can evaluate potential risks and choose the safest and most efficient course of action. The technology also allows them to learn from their experiences, improving decision-making over time. Using AI, robots can also apply knowledge gained from one task to another which helps to accelerate the learning processes.
Autonomous navigation
Just as autonomous vehicles are using AI in transportation (see above section), robots are also using AI to move autonomously through complex environments. Data from multiple sensors combined with AI algorithms allows robots to avoid collisions and interact with the environment while Simultaneous Localization and Mapping (SLAM) technology uses AI to help robots create maps of unknown environments and determine their own location within those maps.
Predictive maintenance
By processing data from various sensors (temperature, vibration, pressure etc.) in real time, AI can identify patterns which may indicate a potential future malfunction. It can also use historic data to predict when components are likely to fail, based on factors such as wear and tear and amount of use, and schedule in maintenance accordingly.
Improved human co-operation
Increasingly humans are working alongside robots with figures from Statista showing the number of collaborative robots (cobots) having doubled between 2017 and 2020, from 11,000 units worldwide to 22,000 units. Not only does AI enable robots to understand and respond to human gestures when working together, it can also be used to optimize task allocation between robots and humans.
AI is helping to improve the productivity of robots in several sectors. Here we look at a few areas where robotics with AI is being widely used with great effect.
Thanks to these benefits,
Robots are increasingly used in all areas of healthcare both to plug gaps where there are not enough staff to carry out tasks as well as to improve patient outcomes. For example, Vitestro, a Dutch medical robotics company, recently developed an autonomous blood drawing device that combines AI-based, ultra-sound guided 3D reconstruction with robotic needle insertion. Robotic assistants, many of which now incorporate AI, are also helping to perform some surgeries. These include the Da Vinci Robots from Intuitive which are used for minimally invasive torso surgeries and the Mako Robots from Stryker which are pre-programmed to help in hip and knee replacements. Finally, service robots are transporting linens and supplies in healthcare environments, while the use of cleaning and disinfection robots can help to reduce hospital-acquired infections.
Industry
Robotics are an integral part of what’s referred to as Industry 4.0, or the fourth industrial revolution alongside technologies such as IoT and drones. For example, AI-equipped humanoid robots are increasingly deployed within smart factories to handle delicate components with extreme precision while AI enables Autonomous Mobile Robots (AMRs) to navigate complex warehouse environments, optimize routes and collaborate with human workers.
According to the Boston Consulting Group (BCG), robotics are set to transform industry with 70% of companies questioned in its Advanced Robotics in the Factory of the Future survey expecting advanced robotic solutions to be a ‘very important productivity driver in production and logistics by 2025’ despite only ‘20% having a holistic understanding of their implementation.’
Recently Chinese luxury electric car maker Zeekr, owned by Chinese automotive company Geely, announced it has deployed humanoid robots in its factories for the first time. Called the Walkr S Lite, the two-legged robot, which is made by Shenzhen-based UBtech Robotics, can be seen in this video performing basic tasks such as moving boxes around the carmaker’s “intelligent factory” in China’s Hangzhou Bay region.
The combination of AI and robotics is transforming the agricultural industry too. A pioneer in agricultural technology, John Deere has been at the forefront of autonomous tractor development for some time while Harvest CROO Robotics specializes in robotic strawberry harvesting, using AI to identify and pick ripe strawberries. Robotic systems can provide precise food rations for cattle, milk cows effectively as well asa accurately sort and grade produce based on size, quality and other criteria. Drones equipped with AI-powered cameras can also capture high-resolution images and analyze crop health to identify potential diseases.
While AI-driven robots are playing an increasingly important role alongside IoT sensors. In addition to playing an increasingly important role within back-end warehouse and logistics hubs (see Industry section above), AI ‘chatbots’ and virtual shopping assistants AI-driven robots are also being used in more customer-facing roles within the retail industry.
Handling Routine Inquiries - Chatbots can handle common customer queries like order status, returns, and store locations, thereby freeing up human agents for more complex issues.
Despite these advances though it’s clear there are more opportunities for retailers and tech companies to improve the chatbot proposition even further. The Uberall survey reveals that 36% think that chatbot accuracy needs to be improved, particularly in the areas of AI and natural language processing, in order to understand what customers are looking and ‘to get a human customer rep involved where needed.’
24/7 Availability - AI-driven tools offer round-the-clock assistance, ensuring customers can get help whenever needed.
Quick Response Times - Chatbots can provide instant responses to customer inquiries, reducing wait times and improving satisfaction.
Personalized Recommendations - By analyzing customer data, AI can offer tailored product recommendations, enhancing the shopping experience.
Improved Customer Satisfaction - By providing efficient and helpful service, chatbots can reduce customer churn and associated costs.
There’s definitely growing interest in branded chatbot experiences, but most consumers still need convincing. Many are wary, either because chatbot technology in the past was not advanced enough to ensure a good experience, or because consumers worry chatbots could easily become another spam channel. Brands have to do a better job creating AI experiences that customers find personalized, helpful and worthwhile.
Florian Huebner, Co-CEO and Co-Founder, Uberall
Looking beyond chatbots, some retailers have trialed AI-driven robots in the retail space. For example, as early as 2016, hardware retailer Lowe’s introduced an Autonomous Retail Service Robot (ARSR) named LoweBot to help people find what they were looking for in some of its Bay area stores. And although the retailer later dropped the ambitious experiment to focus on other technology solutions, there still remains plenty of interest among brands to introduce AI robots in the retail space claims. Dr Sarah-Jayne Gratton in The Rise of Robotics in Retail: “As we venture into the year 2024, AI robotics is not merely ancillary support but a transformative force redefining the shopping experience, inventory control, and supply chain management,” she writes.
For organizations some of the main benefits include the following:
According to a study of more than 1000 US adults by Uberall, 80% of consumers have had positive experiences with chatbots and 40% are interested in chatbot experiences from brands. In addition to online retailers such as Amazon, several brick-and-mortar retailers now offer a chatbot service to their customers. These include Walmart which provides product information, store locations and order tracking to customers, Nike which offers product recommendations and helps customers find their right size and Sephora which offers personalized beauty recommendations based on customer preferences and skin type.
Finally, AI-equipped robotics is playing an increasingly important role in space. Designed to help astronauts reduce their stress levels and complete day-to-day tasks, Airbus’ CIMON-2 made its debut on used on the International Space Station (ISS) in 2020. Similarly, NASA’s R5 (also known as Valkyrie) - a six-foot-tall, 132kg humanoid robot – can also help astronauts in space. As well as boasting multiple actuators, it’s powered by a battery, has gripper hands and features cameras and sensors on the chest and head.
70%
HEALTHCARE
agriculture
retail and customer service
ROBOTICS:
As UK based software company Darktrace states in its State of AI Cybersecurity 2024 report,
Thanks to these benefits, AI is helping to improve the productivity of robots in several sectors. Here we look at a few areas where robotics with AI is being widely used with great effect.
INDUSTRY
AGRICULTURE
RETAIL AND CUSTOMER SERVICE
According to a study of more than 1000 US adults by Uberall, 80% of consumers have had positive experiences with chatbots and 40% are interested in chatbot experiences from brands. In addition to online retailers such as Amazon, several brick-and-mortar retailers now offer a chatbot service to their customers. These include Walmart which provides product information, store locations and order tracking to customers, Nike which offers product recommendations and helps customers find their right size and Sephora which offers personalized beauty recommendations based on customer preferences and skin type. Despite these advances though it’s clear there are more opportunities for retailers and tech companies to improve the chatbot proposition even further. The Uberall survey reveals that 36% think that chatbot accuracy needs to be improved, particularly in the areas of AI and natural language processing, in order to understand what customers are looking and ‘to get a human customer rep involved where needed.’
In the same way, cyber professionals are using Gen AI in several ways to improve defenses. Below we outline just a few of the ways that Gen AI is helping organizations to anticipate threats before they happen and tackle cybercrime when it does occur.
First proposed by the European Commission on 21 April 2021, the EU AI Act is the world’s first piece of dedicated AI legislation, although others are set to follow over the coming years (see Global AI legislation). Formally approved by the EU on June 14th, 2023, the act is expected to pass a final draft by the end of 2023 though most likely won’t come into force until 2025.
For cybersecurity professionals, many of whom are increasingly overwhelmed by the sheer volume of cyber-attacks, Gen AI and AI more broadly offer the opportunity to augment their own intelligence and speed up the MTTD/R (Mean Time to Detect and Resolve) security issues. For example, AI can analyze network traffic, system logs and user behavior to identify potential anomalies that might indicate a cybersecurity attack, freeing up security analysts to focus on investigating high security threats. It can also automate certain steps in the incident response process - such as isolating infected systems, quarantining files, and patching vulnerabilities – as well as generate reports on security incidents, including the root cause and recommended actions.
*
LLMs
Large language models
CONCLUSION
robotics/ humanoid tech
Proactive Threat Detection and Analysis
According to Precedence Research, the global AI in telecommunications market was estimated at $1.23 billion in 2023 and is projected to hit $46.33 billion by 2033, a CAGR of 41.4% from 2024 to 2033. Figures from Omdia show that telecoms AI software alone is expected to grow from $419 million in 2018 to more than $11.25 billion in 2025. In its State of Play of AI in Telecoms report, Omdia states that nearly a third of CSPs (Communications Service Providers) were investigating AI technology as of February 2023. Just over a fifth were piloting at least one function or business unit using, and slightly fewer had live deployments of AI for at least one function or business unit.
Regarding Gen AI specifically, Omdia estimates that virtually all telcos are looking at the technology – ‘a fifth are testing it and another fifth are already using it’ claims the report.
Below we look at the some of the main use cases which are driving this growth in the use of AI.
Fraudulent activities such as subscription and SIM swap fraud are a multi-billion-dollar problem for the telecoms industry. A report by the CFCA (Communications Fraud Control Association) estimates that fraud costs telecom companies globally almost $40 billion annually. Yet despite preventative measures, telecommunications fraud rose by 12% in 2023, resulting in a substantial $38.95 billion loss (equivalent to approximately 2.5% of total sector revenues). By analyzing call patterns, device information and location data, AI can identify suspicious SIM swap attempts. Using AI, anomalous behavior, such as unusual call durations, high volumes of international calls, or strange call termination patterns, can also be flagged in real-time. Furthermore, AI-driven systems can be used to create unique behavioral profiles for each user, helping to detect account takeovers or identity theft.
Thanks to AI, telecoms operators can analyze patterns in traffic fluctuations, signal strength variations and equipment performance metrics. This allows them to identify any potential problems before they escalate into major outages, thus reducing downtime and maximizing revenue. For example, AI can optimize resource allocation for different network slices (IoT, VR, AR etc) based on real-time data, as well as help optimize performance for different types of traffic by intelligently managing network resources. By examining historical network data patterns and user behavior, AI algorithms can also help to forecast prospective network problems in advance and fix issues before they even arise. This is known as predictive maintenance.
According to New York based marketing company Emplifi, 86% of customers would leave a brand they trusted after just two poor customer experiences. That’s why in a highly competitive sector such as telecoms, good customer experience is crucial.
By a 2:1 margin, CSPs believe that Gen AI will have more impact on customer-facing functions than on internally-facing functions. CSPs are looking to reduce customer support costs, so it is logical that their focus is on customer-facing functions.
improving customer experience
network optimization
fraud prevention and security
State of Play Report, Omdia
Indeed 68% of respondents to the Omdia report state that Gen AI will have the most impact with customer facing functions compared to 32% for internally-facing functions (generatinig marketing materials, writing code etc). For example, with Using AI-driven chatbots and virtual assistants, telecoms companies can provide 24/7 customer service, instantly answering inquiries, personalizing support across languages, and guiding customers to self-service option. Recently Vodafone announced the latest iteration of its customer-focused, Gen AI virtual assistant, Super TOBi across For example across 13 countries in Europe and Africa. Vodafone has recently announced the latest iteration of its customer-focused, Gen AI virtual assistant, Super TOBi. Powered by Microsoft Azure, the online chatbot can support customers in 11 different languages.
AI systems can also be used to tailor products and services based on individual consumer behaviour, as well as identify customers most at risk of leaving. They can then develop strategies to keep customers on board and therefore reduce churn. One company that recently announced it is using AI systems across its network is AT&T. Thanks to a partnership with NVIDIA RAPIDS Accelerator for Apache Spark, the telco claims to have boosted operational efficiency in several areas, from training AI models and maintaining network quality and optimization, to reducing customer churn and improving fraud detection.
$46.33 billion
market projection by 2033
According to Precedence Research
$1.23 billion
The global AI in telecommunications market estimate worth in 2023
Transforming Telecoms:
Reducing cost, delivering engagement with AI
Gen AI deployment in Telecoms
AI is transforming the telecommunications industry too, helping to boost network efficiency, streamline operations and improve customer experience at a time when operators are under increasing pressure to reduce their costs and drive subscription revenue from customers.
TRANSFORMING TELECOMS:
IMPROVING CUSTOMER EXPERIENCE
NETWORK OPTIMIZATION
State of Play of AI in Telecoms report
According to New York based marketing company Emplifi, 86% of customers would leave a brand they trusted after just two poor customer experiences. That’s why in a highly competitive sector such as telecoms, good customer experience is crucial. Indeed 68% of respondents to the Omdia report state that Gen AI will have the most impact with customer facing functions compared to 32% for internally-facing functions (generatinig marketing materials, writing code etc). For example, with Using AI-driven chatbots and virtual assistants, telecoms companies can provide 24/7 customer service, instantly answering inquiries, personalizing support across languages, and guiding customers to self-service option. Recently Vodafone announced the latest iteration of its customer-focused, Gen AI virtual assistant, Super TOBi across For example across 13 countries in Europe and Africa. Vodafone has recently announced the latest iteration of its customer-focused, Gen AI virtual assistant, Super TOBi. Powered by Microsoft Azure, the online chatbot can support customers in 11 different languages.
Read the latest Telecoms article: Maximizing the Impact of 5G: How AI is Revolutionizing Telecom Operations to discover more, and join us at The AI Summit New York 2024 to access the experts and intel in telecoms.
Discover the opportunities:
...looked at just a few of the industry sectors that are being rapidly transformed by AI technology including transportation, cybersecurity, robotics and telecoms. For example, AI-driven robots are increasingly working alongside humans in warehouses, operating theatres and even in space with great success. Meanwhile, Gen AI is helping to drive greater productivity in several areas, from cybersecurity to computer coding and content creation.
In this report we have...
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However, AI isn’t a ‘one size fits all’ solution.
What works for one company might not work for another. Similarly, solutions will vary from sector to sector. Nor has been it universally accepted – at least not yet. In sectors such as retail and telecoms there are still opportunities to improve natural language processing to improve take up of chatbots and virtual assistants, especially among older generations. In transportation, organizations need to address ethical and safety concerns around AI before autonomous vehicles become truly widespread.
Nevertheless, what’s clear is that AI is rapidly transforming industry both in its own right as well as alongside new technologies such as IoT and big data. For example, in the pharmaceutical industry, patient data in increasingly being used in conjunction with AI for personalised medicine solutions while in offices IoT-based sensors are being used alongside AI to optimise energy use, helping to save both money and the planet.
Just as the first industrial revolution had a profound effect on workers in the 19th century so to will the latest industrial revolution, Industry 4.0, of which AI plays a key role. For businesses that embrace the new technology, there will inevitably be exciting opportunities to develop new business models, increase efficiencies and drive profitability. New jobs which we can’t yet imagine will also be created for future generations to carry out.
Nevertheless, what’s clear is that AI is rapidly transforming industry both in its own right as well as alongside new technologies such as IoT and big data. For example, in the pharmaceutical industry, patient data in increasingly being used in conjunction with AI for personalised medicine solutions while in offices IoT-based sensors are being used alongside AI to optimise energy use, helping to save both money and the planet. Just as the first industrial revolution had a profound effect on workers in the 19th century so to will the latest industrial revolution, Industry 4.0, of which AI plays a key role. For businesses that embrace the new technology, there will inevitably be exciting opportunities to develop new business models, increase efficiencies and drive profitability. New jobs which we can’t yet imagine will also be created for future generations to carry out.
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