Last week, S&S held a workshop with a manufacturing client focused on their aspirations for Artificial Intelligence (AI). The purpose of the session was to help the client align on their AI goals and determine what it would take to successfully integrate AI into their operations. To facilitate this journey, we followed a structured approach to the client reflect on their current state, draw inspiration from industry trends, and visualise their desired AI-powered future.
Reflecting on the Current State
Our first step was to gain clarity on the client’s current AI situation. This was a crucial phase, as it helped uncover why they were interested in AI and what internal drivers or blockers existed. Some key points that surfaced during the discussion included:
- The Board is asking for AI updates: Senior leadership is eager to understand what the company is doing about AI.
- Growth aspirations: There’s recognition that AI could support growth, but the team is unsure about how to fully utilise it.
- Data as a foundation: Conversations around AI often led to the importance of data — its availability, governance, and quality.
- Fear from past failures: Previous attempts with AI had not been successful, leading to hesitations.
- Ongoing efforts: Some teams were already using AI tools and learning valuable lessons.
- Unblocking challenges: The client identified blockers preventing AI implementation and expressed a need to overcome them.
By facilitating an open discussion, we were able to identify the main challenges — data availability and quality being the most prominent — and the fear some individuals had about AI. This allowed us to build a clear picture of where the client stood and what they needed to move forward. Importantly, the team also identified 26 potential AI opportunities examples include: customer experience, knowledge management, and productivity.
Learning from Industry Experiences
The next phase was to reflect on lessons from other organisations. We shared 18 case studies with the client, covering topics such as AI adoption, data-driven operations, and change management. We asked the client to evaluate which examples felt most relevant to their journey and why. Key takeaways included:
- Blockbuster’s failure to adapt to digital disruption: Blockbuster failed to adapt to the rise of digital streaming and online rental services and dismissed a potential partnership with Netflix. For the client, this example highlights the risk of not embracing new trends such as AI.
- Microsoft’s growth mindset shift: Microsoft embarked on a cultural transformation to shift is organisational mindset to a growth mindset, this involved a significant change in culture and behaviours. For the client, this highlighted the need to move to a data-centric mindset and to develop an environment where employees are encouraged to learn and be collaborative.
- Netflix’s data personalisation: Netflix uses a wide array of data to power it personalised recommendation engine, enabling Netflix to increase user engagement and retention rates. This case study resonated with the client as a strong example of using data to improve customer experience.
A recurring theme throughout the discussion was the critical role of data. To effectively implement AI, it became clear that the client needed to align a robust data strategy with their operating model. This involved designing a strong data framework, establishing governance, and implementing reliable infrastructure.
Envisioning the AI Future
The final stage of the workshop enabled he client brainstorm and prioritise potential AI initiatives. The goal was to pinpoint a single, high-impact AI initiative to begin with, that can be used as a proof of concept to build momentum moving forward. We asked the client to brainstorm all possible AI use cases and share them on a whiteboard, encouraging the team to think as broadly as possible at first.
Using an impact vs. effort matrix, we collaboratively assessed the initiatives to prioritise one that could serve as a strong proof of concept. This initiative, chosen for its significant potential and feasibility, would first be implemented in one region before scaling across markets. With this initiative in hand, we turned our focus to addressing enablers and blockers. The client identified potential hurdles, such as a lack of internal expertise and uncertainty about how to communicate AI’s value to stakeholders. We provided guidance on how to navigate these blockers, emphasising the importance of training, communication, and cross-functional collaboration.
The role of data in AI
A key focus of our recent workshop was the pivotal role that data plays in successfully implementing AI solutions within an organisation. AI initiatives are inherently data-driven, and we discussed an iterative approach where the below four steps can be followed:
- Data landscape: Laying the groundwork for AI starts with mapping your data landscape—identifying data sources, assessing quality, ensuring accessibility, and aligning with clear objectives. A solid foundation is essential for effective AI.
- Data strategy: A strong data strategy includes designing governance frameworks and integrating tools to ensure data flows smoothly across the organisation. This alignment enables scalability and seamless AI deployment while considering the organisations operating model.
- AI implementation: AI solutions should be prioritised, deployed, and integrated iteratively, allowing for ongoing refinement and development. This ensures AI aligns with evolving business needs and minimises the risk of implementing AI for the sake of implementing AI while offering little real value to the business.
- AI optimisation: Post-implementation, continuous monitoring and improvement, combined with scaling successful initiatives, are key to maximising AI’s impact. Feedback loops and performance monitoring drive ongoing optimisation.
Achieving success with AI requires more than just technical implementation; several key enablers are critical to creating an environment in which AI can thrive. Examples include:
Clear business case:
- AI initiatives must align with organisational goals and solve real problems.
- A robust business case ensures efforts are targeted and add value as well as ensuring alignment on the vision for the initiative.
Investment:
- AI projects require significant financial resources for infrastructure, data, and talent.
- Organisations need to invest not only in technology but also in systems for scalability and sustainability.
- In order to roll out AI initiatives, there is also a significant investment needed in time and resources to ensure the projects success.
Executive sponsorship and leadership commitment:
- Strong leadership support is essential for driving AI initiatives forward.
- Executive backing helps clear roadblocks and enables cross-functional collaboration.
Organisational behaviour and culture:
- AI adoption requires a culture that promotes data-driven decision-making and experimentation.
- Resistance can arise if the organisation isn’t culturally prepared for AI’s impact on workflows.
Change and adoption practices:
- AI often leads to significant changes in roles and workflows, requiring re-skilling and adjustments.
- Effective change management practices—including communication, training, and support—ensure successful AI integration into daily operations.
Commitment to Action
The workshop ended on a proactive note. We asked each attendee to write down a personal commitment outlining the steps they would take to drive the agreed AI initiative forward. This ensured ownership and accountability, both crucial for AI success. This workshop demonstrated that successful AI adoption is not just about technology, but about people, data, and strategy. By reflecting on their current state, learning from industry examples, and focusing on one actionable initiative, the client is now well on their way to becoming an AI-driven business.
If your organisation is considering AI, remember these key principles:
- Get your data right. It’s the foundation of AI.
- Focus on people and culture. AI is not just a technological shift; it’s a mindset shift.
- Start small but think big. A focused AI initiative can build the momentum you need for broader adoption.
Together, these steps will help you unlock the full potential of AI and position your business for future success.