20+ years of industry experience as an analytics professional have taught me that while underlying enabling analytical technologies have grown exponentially complex and capable, as business leaders we have not kept pace with respect to our decision making abilities.
In 2022, what should be smarter, more accurate data driven decisions using analytical systems, AI & ML, and leading to measurably beneficial outcomes in organizations, far too often fall back on razor-thin business cases fueled by superficial analysis, a host of assumptions and a great deal of organizational impatience.
These observations form the basis of my research into organizational decision making which started at Cambridge University (examining strategic decision making by international organizations) and continues now at Cardiff University (researching adoption and use of AI/ML in Financial Services), hand in hand with a 20+ year career as a Technology Entrepreneur and a Product-Analytics Consultant and Executive in large and small public and private companies globally.
A sincere belief in the human mind and its capacity to learn, to improve and to make better decisions coupled with the right technology drives this research. If this is of interest to you, please reach out and let's have a chat.
MBA (Technology Management)
MSt (Cambridge, Strategic Decision Making)
PhD (Cardiff, Analytics Adoption & Use, In Progress), CSPO (Agile, Product)
Guest Lecturer, FinTech, Data Analytics, Product Development, Cambridge Business School, Executive Education Programs
- Technology Adoption & Use - In general and specifically within Financial Services
- AI/ML Adoption Readiness within Financial Services
- Quantification of Technology Impact on Organizational Performance - within Financial Services
- Prioritization of investments and go to market sales activities
Factors influencing the adoption, application and impact of advanced analytics (AI, ML) within financial services firms and their client bases.
The global appetite for AI and ML solutions continues to grow exponentially, however their practical adoption and application remain problematic. The purpose of this research is to discover the driving factors for their adoption by Financial Services organizations. This knowledge can be used to reduce barriers and enhance successful adoption and application of AI and ML solutions to realize internal performance impacts and target markets.
- Which factors influence the successful adoption and use of AI technologies within FIs?
- Can AI adoption readiness be accurately diagnosed and predicted prior to adoption?
- Can successful ongoing AI use/application be diagnosed accurately and in a timely manner to allow for interventions?
- Can successful AI adoption and application be correlated to organizational performance outcome?
Organizations seek innovative ways to differentiate and prevent mass-commoditization while improving their own performance. As enabling technologies, AI & ML may provide a pathway for organizations to achieve these results. The business case for AI adoption within Financial Services remains unclear. News articles and research reports by top advisory firms and vendors promote the potential of AI and ML as transformational technologies, however to many CIOs this substantial investment is not straightforward.
Research exists on the topic of technology adoption, and the adoption of AI adoption and application within FS. However, the research does not provide a resounding business case and roadmap for achieving successful, sustained adoption and realizing measurable performance gains.Research Plan Differentiators:
- Granular Data Gathering - The overwhelming majority of existing data gathering is survey based.. While insightful, the data is insufficient to accurately evaluate and determine underlying factors as it is largely subjective. My research will identify objective individual and group level activities indicative of these factors
- Longitudinal Analysis - While extant data sets are predominantly of a single point in time, this research will evaluate activities longitudinally to gather sufficient information and gain perspective on occurrences and outcomes.
- Data Analysis Techniques - Existing analysis leverages qualitative analysis techniques with basic statistical analysis. I will use ML techniques to discover relationships within the data and the driving factors.
- Validation and Correlation Analyses - Existing research does not validate observations, relegating them to hypothetical models. A wide array of factors are proposed but not correlated to successful or failed adoption. or factors to performance gains within the adopting organizations. My research will address these gaps.
Outcomes of this research will include the development of applied analytical models for use in market facing Applications, enabling practitioners to leverage results while abstracting the complexity. For example, the envisioned Sales Prioritization Application gathers organizational data from internal and external public sources and analyzes the readiness of an organization (such as a bank, other FS firm) to successfully adopt and benefit from AI driven solutions. This Application will identify areas of strengths, weakness and high/low levels of readiness, and prescribe the optimal course of action to engage with and support the prospect/client, shortening time to market, adoption and revenue by a target of >25% as compared with existing systems.