AI industry trends: Challenges to operationalising AI in FS

NextWave Consulting collaborated with Monmouth University to survey a diverse group of C-Level Executives, Hiring Managers, Line Managers, Entry Level Employees and University Students, to assess the current and expected hiring trends for entry-level professional workers in an AI-enabled workforce. This research was led by a NextWave Board Adviser who is also an Executive in Residence at Monmouth University as part of both institution's desire to connect the academic and commercial communities.

Following this survey, NextWave interviewed a select group of experienced C Suite professionals working across banks and wealth management firms, who are currently involved in the upskilling of their current workforce with artificial intelligence, as well as setting ethical and operational guidelines for their organizations. Output and feedback from these interviews were used to connect the survey responses with the current state of financial services, from the perspective of people who will be recruiting future generations in a world where AI is rapidly developing as a competitive advantage.

The research was structured to:  

• Analyze the impact of artificial intelligence technology on the entry-level professional job market, focusing on the financial services industry
• Identify significant trends impacting entry-level professional jobs, the opportunities and threats to these jobs and the changing skill sets required to perform these jobs
• Identify how the hiring of entry-level personnel will adapt in the future and how universities will need to alter curriculum to best prepare the workforce for these new expectations
  

Methodology:

We conducted live interviews of approximately one hour with each survey group member. The survey allowed for open-ended discussion about overall trends, in addition to the specific survey answers. The responses are presented in aggregate and do not specifically name or identify any of the participants or their affiliations.

This article is the first out of two which will cover in detail the four themes which emerged from this research, starting with the challenges of adopting AI in the swiftly evolving business landscape:

• AI as the opportunity of a generation – While the survey participants believed that AI was a ‘once in a generation’ opportunity, across the population there was discussion about the significant risks that are broader, trickier, and more dangerous than those of past technological innovations. We looked at how AI requires upskilling and preparation for new talent, who will become the workforce of the future.
• R&D, Adoption & Operationalization – Incorporating this technology into the day-to-day business function will likely be more difficult, risky, and disruptive than technology innovations of the past, providing a challenge to all industry sectors including financial services and fintech.
  

Is AI a threat or opportunity for the next generation of leaders?

Survey participants agreed that the efficiencies promised by AI represents an opportunity not seen in generations (89% responding as such). However, most of the discussion was about the numerous and significant threats this technology poses. There existed a large divergence of opinions regarding the nature and severity of the threats based on the age of the participant, the size of their company, and the industry they work for. There was universal agreement that AI technology will be disruptive to many businesses and that it presents wider-ranging social impacts than previous technological advancements, specifically in terms of job opportunities.

 The efficiencies are the opportunity

The inevitable and ubiquitous nature of AI technology requires companies to leverage its benefits and embrace this new mode of digital acceleration or to suffer the consequences of falling behind the competition. The ability to automate and eliminate repetitive and menial tasks was most noteworthy in discussions about the benefits.

The threats are significant

A major cultural threat identified was the fear of unexpected outcomes, both socially and professionally. The need for high levels of quality assurance, especially in the early phases of technology adoption, was cited numerous times. It was believed that the output of AI cannot be delivered directly to clients, but rather needs to be managed, validated and edited first by humans.

Data exposure, particularly client data, was considered potentially the biggest and most complex concern for using AI technology. This threat was seen as most acute during the evolution of the technology and its initial large-scale deployments. 

Another key concern was the fear of this technology being used ‘in the wrong hands’ because it can enable a threat actor to develop and use a repeatable AI process and efficiently automate malicious activities on a large and precise scale.

 It’s going to be disruptive

The dissonance between views on AI suggests there will be generational gaps and large pockets of resistance to this technology. Those in the position of resistance will focus on the cultural, ethical and operational threats posed by AI and leverage the inertia of corporate culture to slow or stop the adoption.

Senior executives at large and mature companies will be most vulnerable to emphasizing the threats over the opportunities. For example, when asked about the impact of AI on entry-level professional hiring requirements over the next 3-5 years, 73% believed that the same amount or more hiring would occur. Some job functions will likely be completely eliminated but more will be reduced significantly or drastically changed. In most cases, there will still be a need for an overseer of the technology and the exception handler, therefore more job functions than job roles will be terminated by AI. However, the opportunity lies in the technology’s ability to will eliminate a certain level of busy (manual) work and allow knowledge workers to focus on analysis and decision making.

Ultimately, however, the biggest disruption will be in the shift in working habits. By now, many of us have heard the phrase “you will lose your job not to AI, but to another person using AI”. If you are not using AI, you simply will not be able to operate at the level expected by your organization and other competitors.

What are the challenges and solutions to R&D, Adoption & Operationalization?

Only 11% of survey participants described their company’s AI policy as clear and concise. Most of those were considered clear only because the policy was for zero use. Implementing AI “at scale” was a central theme, particularly the many technical, operational, financial, and social challenges as impediments. Most participants felt that their company and the industry lacked a top-down strategy for adoption and use of AI at any significant scale.

Participants were careful to distinguish between the phases of R&D versus full scale operationalization of the technology. Below are some findings that pose questions about operationalizing AI while suggesting possible solutions for organizations.

AI at scale requires investment and AI Strategy Leads

Cloud and big data are the genesis of AI; capacity and data analytics enabled AI to become a reality. The limitations are not the technology, but human factors of adoption and operationalization into the workflow at scale. The difficulty at present is that the technology is still in the R&D phase and most companies are not willing to dedicate significant capital to AI. Therefore, the hiring of AI-specific skills is on a limited basis and typically at junior levels. Organizations must start thinking of implementing AI Strategy Leads and Heads of AI Ethics to oversee the adoption of AI.

 Need a Strategy

Most respondents described their company’s current approach to AI as “from the bottom up” and many companies have simply banned the use of it until more is known. The most ambitious uses of AI were described as “small and impactful” and considered relatively safe.

Respondents generally agreed that companies need a top-down AI strategy and that its adoption should not be accidental as it appears to be at most companies. AI encroaches on every function and will impact every employee. It will need to ‘leave the lab’ for many companies to see large-scale benefits and for the macro impact to be felt in the job market.

The future of AI in organizations – adoption is not the same as operationalization

Few, if any, large companies can articulate a clear and concise policy that is indicative of a strategic approach ready to roll out company-wide. The ability to contain the risks of AI output, as well as its volume and speed, was referenced numerous times by our participants as a roadblock to operationalization.

Most respondents agreed that additional staff will be needed over the next 3 to 5 years to build out the operational infrastructure to use this technology. There will be an initial ramp-up of hiring to meet the demand of learning to establish the automation process. The first job reductions will likely be relatively minor and will target lower paid workers. The bigger ROI will be replacing people with more experienced knowledge. Major staff reductions will likely not occur for 5-10 years out as the technology is proven and fully integrated into the process.

Conclusion

The landscape is evolving; industry giants like Microsoft and Google are democratising AI through user-friendly platforms like ChatGPT and CoPilot, amplifying visibility and accessibility. This new wave of innovation is bringing the power of AI directly to every area of the business. Generative AI, together with automation technologies has the potential to transform efficiency, precision, and cost-effectiveness at enterprise scale.

However, there are clear cultural, ethical, strategic and operational challenges that need to be overcome for full scale adoption of AI. Ultimately, this study suggests that AI is poised to revolutionize the financial services industry by automating a wide range of tasks, as long as we find solutions to some of the challenges posed. Harnessing the power of AI smartly and responsibly can increase efficiency, reduce costs, and improve accuracy, while also allowing human workers to focus on more complex and value-added activities.

NextWave's focus on driving business outcomes for our clients, powered by the latest technologies and our collaborative ecosystem, is pioneering the integration of AI into financial solutions. We help financial organisations to leverage and operationalize AI, working with or in place of AI Strategy Leads to help navigate the challenges of this new technology in areas such as:

We implement next-generation AI solutions to automate mundane tasks, liberating resources for strategic initiatives. Our integration of Appian, Alteryx, ServiceNow and Quantexa technologies ensures rapid business transformation. By harnessing these tools, we empower organizations to navigate complexities with agility, driving efficiency and fostering sustainable growth in the dynamic financial sector.

Contact us to learn more about our AI services and start harnessing the power of AI for your organization.

While this article explored the challenges (and some solutions) of operationalizing AI for organizations and employees, our next article focuses on the opportunities of AI for the next generation, including some unique perspectives on upskilling future talent.

About NextWave

NextWave is an award-winning digital acceleration consultancy. We help leading firms in Financial Services to successfully deliver complex transformation and the automation of businesses and functions across Asset & Wealth Management, Banking, and Insurance - www.nxwave.com  

Clients come to NextWave because they have key business growth, efficiency and control objectives and are seeking a more future-focused alternative to the big-name consultancies. We provide strategy, industry specialist transformation teams, and AI and automation solutions rapidly to help clients deliver business outcomes at a more reasonable price.  A brief background can be seen here - Overview Deck & Brief Videos

About Monmouth University