Below is the summary of my 20k dissertation, which I completed last year. It was hard to summarise all the findings, but this gives you a good overview. If you are not. Reader, you can also find a Podcast of the dissertation here:
Introduction
AI is rapidly transforming industries, and the product value chain (PVC) is no exception.6 This blog post summarises the key findings and recommendations from recent research exploring the multifaceted force of AI and its effects on the PVC. The research, conducted using a mixed-methods approach, highlights the interplay between technological advancements, individual perceptions, and organisational strategies in shaping AI-driven transformation.
A Brief History of AI
Both excitement and setbacks have marked the journey of AI. The term “Artificial Intelligence” was first coined in 1956 at the Dartmouth Summer Research Project on AI.1 Early AI systems faced challenges in real-world situations, leading to periods of stagnation known as “AI winters”.2 However, recent advances in machine learning, particularly with deep learning and transformer architecture development, have elevated AI to new heights.3 We are now in the third wave of AI, with Generative AI (GenAI) taking centre stage.4 GenAI, powered by deep learning models like transformers, can generate original creative content, automate tasks, and enhance decision-making processes.5
Research Methods
The research methodology employed an inductive approach. It began with a quantitative phase using a survey with open and closed-ended questions. This survey aimed to gather data to inform the development of interview questions and identify suitable interview participants. The survey was disseminated via LinkedIn and professional networks, targeting individuals across diverse sectors within the product and technology domains with a sample size of 508. From the survey, 20 founders, CEOs, senior executives, AI practitioners and product professionals were interviewed. The study anticipated generating a rich tapestry of insights by engaging a heterogeneous cohort of respondents regarding seniority and industry. It hypothesised that AI applications and their perceived effectiveness and disruptive potential would exhibit inter-industry variation. Consequently, the manufacturing and automotive sectors were excluded from the sample, as their primary focus on manufacturing automation lay beyond the scope of this investigation.
Analysis of AI in the Product Value Chain
A survey of 49 participants, conducted as part of research into AI’s impact on the product value chain (PVC), revealed that 86% believe AI will significantly impact their industry within the next five years. However, there is a disparity between this perceived potential and organisational preparedness, with only 41% having a clear AI strategy and 48% stating AI is their organisation’s top priority. This gap highlights a potential vulnerability and ill-preparedness for many organisations who, despite foreseeing disruption, are not ready to navigate it strategically. Some of the reasons mentioned in the survey included financial constraints, change fatigue and being ‘busy’ with day-to-day activities.
Opinions on AI’s impact on jobs are mixed, with 33% expecting job losses and 41% remaining neutral. There is also uncertainty about how AI will disproportionately affect lower-skilled jobs, with 29% taking a neutral stance. This highlights a potential lack of understanding of AI and its future implications.
Despite 50% agreeing that AI poses significant ethical issues, the majority could not articulate solutions. Nonetheless, 72% believe the potential benefits of AI outweigh the risks.
Organisational readiness for AI is mixed, with 43% unprepared and 45% lacking a clear vision for AI integration. Satisfaction with current AI use is low, with only 35% satisfied. However, 54% acknowledge AI’s active role in shaping products within their industry.
The survey also highlighted barriers to AI adoption, including concerns about data privacy and security (20%), lack of understanding or trust in AI technologies (18%), and the shortage of skilled personnel (12%).
Qualitative data was collected through surveys and interviews. 20% of survey participants provided qualitative responses, which were analysed using interview data.
Thematic Analysis
The analysis identified five overarching dimensions:
- Ethical Concerns:
- Work displacement due to automation
- Concentration of AI control within a limited number of powerful entities
- A potential decline in human creativity
- Need for a human-centric approach in AI development and deployment
- Barriers to Adoption:
- Clear governance frameworks and evolving regulations
- Integration challenges with existing systems and processes
- Competitive Advantage:
- AI’s potential to disrupt existing business models, both internally and externally
- Enhanced capabilities, innovation, efficiency, and productivity
- Focus on Generative AI and its potential to improve accuracy and decision-making
- Collaboration and Inclusivity:
- Evolving relationship between humans and AI, with a focus on collaboration
- Need for skills like prompt engineering and understanding AI logic
- Importance of inclusivity and technological access
- Organisational Transformation:
- Strategy and adoption of AI in organisations
- Adaptability and strategic planning
- Scalability and efficiency
- Empowerment and augmentation of human capabilities through AI
Recommendations for Organisations Navigating AI-Driven Transformation in the Product Value Chain
This research explored the effects of AI on the Product Value Chain (PVC), highlighting key areas for organisations to consider.
AI’s Transformative Impact:
AI is fundamentally changing every stage of the PVC, from product conception and design to marketing and post-launch optimisation. For example, AI can generate product ideas, validate potential product-market fit, optimise product features, streamline development, improve testing efficiency, tailor marketing campaigns, and enhance customer support. While the potential for disruption is significant, many organisations are unprepared to leverage AI’s full potential strategically.
Strategic Options:
Organisations must strategically invest in AI technologies and skills, proactively addressing ethical implications. Collaboration with external partners and a phased, value-driven approach to AI implementation are crucial for success. Organisations should focus on demonstrating tangible business value and using metrics to measure the impact of AI on their goals and user experience.
Organisational Maturity:
There is a positive correlation between organisational maturity and the propensity for AI adoption within the PVC. Mature organisations embrace AI, while less mature ones are reluctant due to the required transformations.
Recommendations for leaders
- Develop a Clear AI Strategic Vision: Leaders must create a clear AI strategic vision that aligns with business objectives and translates into clear, executable, tangible outcomes.15
- Address Ethical Concerns: Data privacy and security concerns should be addressed from the outset by aligning them with global frameworks such as the EU AI Act and the U.S. Executive Orders.16
- Understand the Operating Model: Leaders should understand their operating model using frameworks such as the PVC and BMC to understand areas where AI can be leveraged to reduce cycle time, increase capability, and drive efficiencies as a point of competitive advantage.
- Invest in AI-Ready Workforce: Leaders must invest in an AI-ready workforce by developing necessary AI expertise through recruitment or upskilling to facilitate effective implementation and adaptation.17
- Measure Success: Leaders should define and monitor technical and business metrics to measure the success of AI implementation.
Conclusion
AI is transforming the PVC, and organisations must be proactive in their approach to AI adoption to remain competitive.18 Organisations can harness AI’s transformative potential while mitigating the associated risks by developing a clear AI strategic vision, addressing ethical concerns, understanding the operating model, investing in an AI-ready workforce, and measuring success. Remember, AI is not merely a technological implementation but a business model transformation initiative.19 Leaders must adopt a learning culture and have a strategic partnership strategy with AI vendors, start-ups, government, and other relevant organisations to build an AI ecosystem network that can be leveraged for rapid innovation.
