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Artificial Intelligence

Artificial intelligence is the key to sustainable and scalable innovation

 

Artificial intelligence is reshaping the future of just about every industry. AI has also been the main driver of big data, fintech, regtech, health tech and smart mobility and will continue to act as a technological innovator for the foreseeable future. Organizations that value innovation and want to remain competitive and relevant within their industries are increasingly relying on AI for sustainable and scalable growth.

 

Join one of Esme Learning’s artificial intelligence courses to keep pace with the evolution of the role of people in the workplace. We offer numerous artificial intelligence courses from top-tier universities to help leaders understand the power of AI. These courses provide the framework for adopting AI-enabled systems to fuel the next wave of innovation and equip them with tools to future-proof their businesses while reaching for sustainable growth.

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Courses
AI Leadership
AI Leadership
Learn how to harness AI as a leadership tool to usher in a wave of innovation in your organization.
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Oxford AI in Fintech and Open Banking Programme
Oxford AI in Fintech and Open Banking Programme
Learn the basics of artificial intelligence in the financial industry and the opportunities it provides for innovation while understanding the possible pitfalls of adoption.
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AI Startups & Innovation Programme
AI Startups & Innovation Programme
Learn how to overcome the barriers surrounding expertise, networks, time and methodologies to successfully create a new artificial intelligence startup.
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Data Strategy: Leverage AI for Business
Data Strategy: Leverage AI for Business
Learn how to craft a data strategy to fuel your AI systems to derive the desired outcomes, increase efficiency and create new opportunities.
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Cambridge RegTech: AI for Financial Regulation, Risk, and Compliance Programme
Cambridge RegTech: AI for Financial Regulation, Risk, and Compliance Programme
Learn how to navigate the complex and ever-changing regulatory landscape and the roles artificial intelligence and machine learning are playing in its transformation.
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Smart Mobility: Reimagining the Future of Transportation Tech & Sustainable Cities
Smart Mobility: Reimagining the Future of Transportation Tech & Sustainable Cities
Learn how AI-powered innovations like autonomous vehicles are reshaping the future of transportation and enabling the creation of smart cities.
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Leading Health Tech Innovation
Leading Health Tech Innovation
Learn how artificial intelligence is revolutionizing healthcare and the opportunities it’s creating for entrepreneurs, innovators and investors.
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Artificial Intelligence Is Driving The Sixth Innovation Cycle

“AI is going to change the world more than anything in the history of mankind. More than electricity.”

 

Dr. Kai-Fu Lee, AI Futurist and Pioneer

 

The theory of “creative destruction,” first coined by economist Joseph Schumpeter in 1942, suggests that business cycles operate under long waves of innovation. These waves are often driven by technological innovation. Think Industrial Revolution in the first wave, railroads in the second and the internet in the fifth.

 

The World Economic Forum estimates that the fifth wave is nearly over as artificial intelligence rings in the sixth wave of innovation.

 

The sixth wave of innovation fueled by artificial intelligence

 

This era will lead to increased automation of systems, predictive analytics and data processing. Physical goods and services will be digitized and the time taken to complete tasks will reduce from hours to seconds.

 

Historically, the technological innovations driving these waves of change have boosted economic growth and improved living standards. But they also create disruption and lead to monopolies, particularly in the cycle’s upswing.

 

During this phase, organizations that can realize the potential of AI and act on it have the strongest possibilities of expanding their profit margins, establishing themselves as a stronghold in their industry and overtaking their competitors.

 

Why do AI projects need leadership buy-in?

 

Put simply, because the stakes are too high. AI projects lead to the creative destruction of entire businesses processes. It also requires company-wide collaboration, new resources, changes in work culture and mindset. The waves of success or failure from AI projects will ripple through the whole organization.

 

Without proper leadership, 80% of AI projects don’t reach deployment and only 60% are profitable.

 

What is the role of the non-technical business leader in an AI project?


“Our future must be one where [leaders] can be tech-savvy but not one that eliminates our concerns and reflections on human identity.”

 

David De Cremer, Author of Leadership by Algorithm

 

Artificial intelligence thrives on routine, repetitive tasks that are systematic and consistent. AI is good at jobs that require hard skills, not soft skills. This means that it’ll become even more necessary for leaders to become fluent in soft skills that relate to human thinking.It’s Moravec’s paradox:what is easy for humans is difficult for AI and what is difficult for humans seems easy for AI.

 

Business leaders know better than AI algorithms which values define their organization, what makes their company different from their competitors and how their core values affect their operations.

 

Every company has unique data and it’s the business leader’s role to shape a strategy that will extricate value from the AI systems.

 

Business leaders aren’t expected to become coders themselves. Still, they need to become more tech-savvy to pursue their business strategy in an environment where technology is part of the business process.

 

Business leaders need to understand what AI algorithms do and also what their limits are.

 

They need to clarify exactly where AI can be used to promote productivity and efficiency in the decision-making chain of the company. Leaders need to have enough knowledge of technology to leverage their granular understanding of the organization’s business processes to generate the most efficiency through artificial intelligence.

 

Most importantly, business leaders need to decide where in the business processes they need to take humans out of the loop and where humans must stay to ensure automation doesn’t lead to segregation of work culture.

 

What role can leaders play in the better design of artificial intelligence systems?


David De Cremer answers the question in his book: “AI will never have ‘a soul’ and it cannot replace human leadership qualities that let people be creative and have different perspectives. Leadership is required to guide the development and applications of AI in ways that best serve the needs of humans.”

Financial Industry as the Early Adopter of Artificial Intelligence

Fintech was one of the first to adopt intelligent systems. Watching the market, plugging data into their models and executing trades are fast becoming a thing of the past. Now, traders are overseeing the AI tools that are spotting trading opportunities and making those trades. This frees them to guide the AI and machine learning-based tools to figure out what is considered a good trade. The shift from execution and tactical roles to more strategic roles is becoming the standard in the finance industry.

 

AI is bringing sophisticated analytical abilities to a generalized environment, thus changing the role of the financial professional forever.

 

What are the changes artificial intelligence is bringing to the finance market?

 

The global AI in the fintech market is expected to reach USD 26.67 billion by 2026 with a CAGR of 23.17%.

 

The computational arms race, together with access to endless amounts of data, is transforming AI applications in fintech to unprecedented levels.

 

  • In finance, AI is being leveraged to create a person’s overall financial health profile considering their cash accounts, credit accounts and investment accounts.
  • AI and ML apps are processing vast amounts of data and keeping up with real-time changes to create customized advice based on new incoming data. This has helped FinServs offer more personalized customer services.
  • Process automation is another important area where AI in financial services is thriving and also making way for cognitive process automation, which can handle even more complex automation processes.
  • Credit card companies are implementing predictive analytics into their existing fraud detection workflows to reduce false positives.
  • The pandemic hastened the implementation of point-of-sale financing alternatives as a potential new avenue for growth. Apart from the standard metrics like bank account statements for underwriting, these fintechs leverage AI models to sort through numerous customer data points to createa sharper customer risk profile.​
  • Traditional financial institutions are now adopting AI solutions to harness information and insights locked away in unstructured documents and automate the manual process.

 

A 2020 survey conducted by Cambridge Centre for Alternative Finance (CCAF) shows that 85% of all respondents in the study used some forms of AI, with the fintech companies being slightly ahead of incumbents in the adoption of AI. This study is suggestive of the fact that AI is becoming mainstream in the financial services industry.

 

The Director of CCAF, Robert Wardrop, also directs our Cambridge RegTech: AI for Financial Regulation, Risk and Compliance Programme.

How is Artificial Intelligence changing RegTech?

RegTech has become an important way for companies to manage complex regulatory processes. FinServs use regtech for regulatory reporting, risk management, identity management and transaction monitoring. It helps businesses remain compliant, which in turn reduces penalty burdens.

 

Increased digitization has led to more cybercrimes like data breaches, cyber hacking, money laundering and fraud. Financial institutions are the biggest target of these crimes. Leveraging big data, machine learning, blockchain and AI, regtech can provide insights into these criminal activities.

 

Top Applications of Artificial Intelligence in RegTech

 

The use of artificial intelligence in regtech is growing and continuing to disrupt the regulatory landscape. AI is helping design more technologically advanced solutions to help navigate the increasing demands of compliance within the financial industry.

 

According to Deloitte research, the following areas of RegTech have a growing number of AI applications:

 

Regulatory Reporting

AI is enabling automated data distribution and regulatory reporting by harnessing big data analytics, real-time reporting and cloud computing.

 

Risk Management

Artificial intelligence and machine learning in regtech are assisting compliance and regulatory risk detection, assessing risk exposure and anticipating future threats.

 

Identity Management & Control

AI is helping carry out counterparty due diligence, Know Your Customer (KYC) procedures, anti-money laundering (AML) and anti-fraud screening and detection.

 

Compliance

AI is aiding the real-time monitoring of the current state of compliance and changes in regulations.

 

Transaction Monitoring

AI, together with blockchain technology and distributed ledger, is facilitating real-time transaction monitoring and auditing.

The Role of Artificial Intelligence in Healthcare

Artificial intelligence is progressively becoming critical to healthcare. It’s being used to improve the speed and accuracy of diagnosis, assist in clinical care, healthcare research and drug development. Artificial intelligence, machine learning and big data profoundly impacted public health interventions, disease surveillance, outbreak response, drug discovery and health systems management in the wake of COVID-19.

 

AI is also empowering consumers to take greater control of their healthcare and realize their evolving needs. Further, AI is having a significant impact in underserved communities, where telehealth is bridging the gap in access to healthcare.

 

Dr. Tedros Adhanom Ghebreyesus, WHO Director-General, says, “Artificial intelligence holds enormous potential for improving the health of millions of people around the world.”

 

Investors certainly understand that. In Q1 of 2021, healthcare AI companies raked in a record-breaking $2.5 billion in 111 deals. This number is up 140% compared to the $1 billion raised in the first quarter of 2020, with the upward trajectory expected to continue.

 

The State of Artificial Intelligence and Machine Learning in Mobile Health (mHealth):

 

AI and ML are bringing significant market growth to healthcare. The AI-led health tech market is predicted to reach 120.2 billion by 2028 and is expected to expand at a CAGR of 41.8%.

 

 

Top Applications of AI and ML in Healthcare

 

According to Accenture research, the top applications for artificial intelligence and machine learning include:

 

  • Robot-assisted surgery
  • Virtual nursing assistants
  • Administrative workflow assistance
  • Fraud detection
  • Dosage error reduction
  • Connected machines
  • Clinical trial participant identifier
  • Preliminary diagnosis
  • Automated image diagnosis
  • Cybersecurity

 

AI and the Future of Transportation 

 

The innovations that artificial intelligence is fueling in urban solutions can bring about multiple benefits like efficient energy, water and waste management, reduction in pollution, noise and traffic congestions and improve the wellbeing and quality of life of people everywhere. 

 

Urban transportation, for example, is one of the biggest polluters. Additionally, it poses a threat to security — 1.3 million people are killed due to road traffic accidents each year, and 93% of these accidents are related to driving errors. Further, urban transport is not inclusive or equally available to all residents. 

 

AI-powered solutions like optimized traffic routing, new modes of transport and autonomous vehicles can significantly cut down emissions, improve safety and make urban mobility more inclusive.

 

According to Heikki Ailisto, Research Professor at VTT Technical Research Centre of Finland and the Lead of Finnish Center for Artificial Intelligence, “sustainability is a phenomenon whose different aspects are complex, and their interplay makes it even more complicated. It is hard to make mathematical, systemic, or societal models that consider all relevant variables simultaneously. AI, however, can do that. It has been successfully used to optimize, simulate, and control similar complex settings, for example, in the energy sector and medical industry.”

 

AI Applications in Smart Cities and Urban Mobility

 

Smart cities and their related activities have always produced data that have informed the insights of local authorities and other stakeholders about the dynamics of those cities, but within a narrow scope. AI can take both data utilization and data-driven decision-making to the next level. 

 

The EU Parliament defines urban AI as “Artifacts operating in cities, which are capable of acquiring and making sense of information on the surrounding urban environment, eventually using the acquired knowledge to act rationally according to predefined goals, in complex urban situations when some information might be missing or incomplete.” 

 

By 2025, AI is expected to power over 30% of smart city applications. These applications encompass solutions that can contribute to urban resilience, sustainability, social welfare and wellbeing of urban residents. 

 

AI applications are evident in every level of the smart mobility and smart city tech stack, including:

 

Business Stack

  • Mobility as a service (emerging business models)

  • Commercial logistics businesses (fleet hubs and depots)

 

Asset Stack

  • Vehicle fleets (autonomous vehicles, electric vehicles)

  • EV charging stations and plugs (batteries and alternative fuels)

 

Infrastructure Stack

  • Road infrastructure

  • Real estate, parking and services

  • Electricity grids (wires)

  • Telecom infrastructure (pipes)

  • Manufacturing infrastructure (tools)

 

Financial Stack

  • Payments enabling transactions

 

Beyond these key levels, AI is also responsible for innovations in data and connectivity, data sharing platforms and cybersecurity — all of which work in tandem to shape the future of urban mobility. 

 

AI applications in smart cities and smart mobility can be categorized into: 

 

AI for governance 

Urban planning, tailored subsidy provision, disaster prevention and management

 

AI for living and liveability, safety, security and healthcare 

Smart policing, personalized healthcare, noise management and improved cybersecurity 

 

AI for education and citizen participation 

Locally accurate, validated and actionable knowledge that support decision-making

 

AI for economy

Resource (cost and time) efficiency and improved competitiveness through sharing services, efficient supply chains and tailored solutions for customer   

 

AI for mobility and logistics 

Autonomous and sustainable mobility, smart routing and parking assistance, supply chain resiliency and traffic management

 

AI for infrastructure 

Optimized infrastructure deployment, use and maintenance of waste and water management, transportation, energy grids and urban lighting

 

AI for the environment

Biodiversity preservation, urban farming and air quality management

 

Growing Investments in Smart Mobility

 

The most significant investments in smart mobility have been related to AI-based solutions and services, including automation, connectivity and electrification. 

 

According to McKinsey, since 2010, investors have poured nearly $330 billion into more than 2,000 mobility companies focused on automation, connectivity, electrification and smart mobility. About two-thirds of the total investment — $206 billion — went to autonomous-vehicle (AV) technologies and smart mobility. 

 

The study also showed that about 90% of these investments went to new entrants in future mobility, with 65% of the investments coming from venture-capital and private-equity companies and 28% from tech companies. Traditional automotive firms accounted for only 7% of the total invested, roughly 20 billion. 

 

AI systems in cities will increasingly work in open, dynamic and hyper-connected environments that will require close collaboration between the private and public sectors. As investments in urban mobility increase, the sector needs new solutions that can help achieve targets like sustainable and equitable mobility and a higher quality of life for residents. 

 

The visionary behind the concept of mobility as a service and CEO of MaaS Global, Sampo Hietanen, says, “Technological development based on AI is not enough unless we have a service that can be sold.”

 

Juha Salmelin, lead of LuxTurrim5G at Nokia, says, “The technology is ready, but innovations are needed to utilize it for new digital services.”

How can Entrepreneurs and Innovators take advantage of the $29+ billion global AI market?

According to International Data Corporation, that number is expected to hit $97.9 billion by 2023, a compound annual growth rate of 28.4%. These numbers present an enormous opportunity for entrepreneurs looking to build AI startups.

 

Saar Yoskovitz, CEO of Augury, says, “Every company needs to leverage AI. It has become another layer in the tech stack.” Still, the AI startup journey isn’t easy.

 

Barriers AI startups are facing and how to overcome them

 

  • Finding the right product-market fit is extremely challenging

AI models require a lot of customization, even within the same industry.

 

  • AI startups need high-quality datasets

For a new startup, this is a chicken-and-egg problem and not easy to overcome.

Saniya Shah, CEO and co-founder of Pilota, stresses the importance of proprietary datasets, “This is what makes your business defensible and attractive to investors. Almost every investor has asked us where our data comes from and what would stop others who can access this data from replicating what we do. So when creating a business centered around AI, it is extremely important to make sure that your data is proprietary and you are not just building a business on analyzing public datasets.”

 

  • Convincing investors of the results from the AI application

Jay Srinivasan, CEO and co-founder of atSpoke, says, “Investors are interested in startups that are building tailored AI solutions for previously unsolvable problems. So focus on areas where there are many inefficiencies and repetitive human processes, such as call centers and back-office paperwork processing.”

 

Startups need to convince investors that their tool makes a real difference, not just a minor improvement.

 

  • Convincing customers to adopt the AI solution

Many customers are still suspicious of the power of AI, and the fact that some models are too complex and opaque doesn’t help.

 

Vasiliy Buharin, Associate Director of Product Innovation at Activ Surgical, says, “Know your customer. You may have the algorithm to solve the worst LA traffic or shorten airplane boarding time 10-fold. But if the solution requires people to behave like pre-programmed automatons, it will never be adopted, and your company will fail. Your customer has a certain way of doing things.”

 

Why is Data the force behind Artificial intelligence?

 

Data is the bedrock of AI tools. Data analytics and artificial intelligence make it possible to derive business insights, optimize processes and grow the business. Any application that uses AI and machine learning will only be as good as the quality of data it ingested.

 

AI, ML and data science overlap but aren’t interchangeable.

 

A simplistic explanation of what each does:

 

  • Data science produces insights
  • Machine learning produces predictions
  • Artificial intelligence produces actions

 

The idea of big data overlaps with all these fields. Big data is a way of referring to the voluminous quantities of data a company generates, whether it's structured or unstructured. This data can be used to generate insights about the company and result in better decision-making.

 

The relationship between AI and Data Strategy

 

Artificial intelligence systems make use of big data to produce two types of outcomes:

 

  • Predictive analytics: These are models that predict the possibilities of a particular event happening in the future.
  • Real-time analytics: Data analytics can also detect deviations in a process by modeling against historical parameters in real-time. This is also a type of machine learning.

 

Artificial Intelligence Courses for Business Leadership

 

Esme Learning brings you best-in-class executive artificial intelligence courses geared especially for business leaders, managers, entrepreneurs, innovators and the C-suite.

 

In collaboration with top-tiered universities such as the University of Oxford, Massachusetts Institute of Technology, University of Cambridge and Imperial Business School, we’re offering an array of courses designed to teach artificial intelligence with a view to immediate execution.

 

AI Leadership teaches you the essentials of leadership and AI concepts, how AI tools can be integrated into the leadership process to improve your capabilities and how to develop an agile leadership style that’s attuned to the ever-changing world and prepared to respond to disruptions.

 

Oxford AI in Fintech and Open Banking teaches you how open banking and artificial intelligence are overhauling the financial services industry and delivering innovative financial products and services.

 

Imperial AI Startups and Innovation teaches you the tactical and strategic foundations of AI, AI-related metrics, the process of innovation, risk management and their roles in bringing a new venture forward.

 

Data Strategy: Leverage AI for Business teaches you how to gain insights into making data-driven decisions and how to solve high-value problems in the areas of AI and data.

 

Cambridge RegTech: AI for Financial Regulation, Risk and Compliance teaches you about the technological innovations in the regulatory landscape and how AI, big data and cloud computing are providing new solutions and creating opportunities.

 

Leading Health Tech Innovation teaches entrepreneurs, healthcare providers and innovators to embrace the changes in the digital health space and realize the opportunities to create and implement AI-led products.

 

Smart Mobility: Reimagining the Future of Transportation Tech & Sustainable Cities teaches innovators, entrepreneurs and policymakers to leverage AI to meet the goals of decarbonization, improve public health and build equitable mobility and sustainable cities.

Sources

  1. Compare the Cloud, “Artificial intelligence and the new wave of innovation”

  2. Builtin, “The Future of AI: How Artificial Intelligence Will Change the World”

  3. World Economic Forum, “Waves of change: Understanding the driving force of innovation cycles”

  4. Knowledge@Wharton, “Artificial Intelligence Will Change How We Think About Leadership”

  5. Forbes, “How To Create An AI (Artificial Intelligence) Startup”

  6. Sartorius, “Understanding the Relationship Between Data Science, Artificial Intelligence and Machine Learning”

  7. Techiexpert, “Artificial Intelligence in RegTech, a problem solver or an industry disruptor?”

  8. Deloitte, “RegTech Universe 2021”

  9. WHO, “WHO issues first global report on Artificial Intelligence (AI) in health and six guiding principles for its design and use”

  10. Fierce Healthcare, “Global investment in telehealth, artificial intelligence hits a new high in Q1 2021”

  11. DZone, “Applications of AI and ML in 2021 mHealth”

  12. Accenture, “Artificial Intelligence (AI): Healthcare's New Nervous System

  13. EU Parliament, “Artificial Intelligence in smart cities and urban mobility”

  14. KPMG, “Future of Mobility”

  15. McKinsey, “Mobility’s Future: An Investment Reality Check”

  16. FCAI, “Artificial Intelligence A Key to Sustainable Smart Cities and Mobility”

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