“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.”

• 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 tocreate a person’s overall financial health profileconsidering 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 offermore personalized customer services.

• Process automationis another important area whereAI in financial services is thrivingand also making way forcognitive process automation, which can handle even more complex automation processes.

• Credit card companies are implementingpredictive analytics into their existing fraud detection workflowsto 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.

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.

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%.

80% of mobile healthcare apps will be AI-based.Themarket for AI-based wearableswill generate $180 billion by 2025.AI applications will create $150 billion in savings for US healthcare.The WHO and the FDA have publishedregulatory frameworks and principles for medical AIand regtech will play a growing role in this field.Top Applications of AI and ML in Healthcare

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

Robot-assisted surgeryVirtual nursing assistantsAdministrative workflow assistanceFraud detectionDosage error reductionConnected machinesClinical trial participant identifierPreliminary diagnosisAutomated image diagnosisCybersecurity

According to International Data Corporation, that number is expected to hit $97.9 billionby 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 themFinding 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 AIapplication

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 insightsMachine learning produces predictionsArtificial 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.

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