Artificial intelligence holds immense potential for reshaping how organisations operate. But navigating the complex terrain of AI adoption strategically can be challenging for organisations, as they grapple with various technical, financial, and workforce-related considerations.
In this exclusive interview with Llewellan Vance, Startup Ecosystem Lead at Huawei Cloud, we discover how you can use design thinking to drive a more seamless, effective, and forward-looking AI adoption journey in your organisation.
Reading time: 4 minutes
‘Done well, AI is the art of the possible.’
Highlights Llewellan Vance, Startup Ecosystem Lead at Huawei Cloud, in a recent interview with the British Council’s Global Head of Services and Talent, Corporate English Solutions, Kate Sullivan.
Artificial intelligence holds immense potential for reshaping how organisations operate, revolutionising processes and enhancing efficiency while presenting new opportunities for growth and innovation. Yet its rapid advancement and haphazard, hurried implementation have raised concerns and sparked apprehensions among individuals and industries.
Who hasn't come across alarming headlines about job displacements due to AI technologies like ChatGPT?
Llewellan urges us to shift our perspective. ‘You’re not going to be disrupted by ChatGPT or similar AI tools. You’re going to be disrupted by someone who knows how to use them effectively.’
This underscores the critical need for a more strategic approach to AI implementation. One that carefully considers the impact on the workforce and the organisation's long-term objectives. And one that includes understanding of key skills needed, along with investment in employee upskilling and reskilling to close the AI skills gap and harness the full potential of these technologies.
But navigating the complex terrain of AI adoption strategically can be challenging for organisations, as they grapple with various technical, financial, and workforce-related considerations.
Enter design thinking.
This 5-step process provides a framework for organisations to approach AI adoption strategically:
- Empathise: Understand the needs and challenges of the target audience.
- Define: Clearly articulate the problem to be solved.
- Ideate: Generate a wide range of potential solutions.
- Prototype: Create tangible representations of solutions.
- Test: Validate solutions with real users.
‘If AI is the art of the possible, design thinking is the human-centric methodology vital for transforming the possible into reality’, explains Llewellan.
Let’s explore how.
Strategy and leadership: The path to the North Star
A recurring challenge in AI adoption, Llewellan points out is ‘the lack of cohesive strategy at the top’.
Design thinking steps in as a strategic compass. Start your journey by anchoring your efforts in your organisation's mission and purpose, your North Star. Ask: What will propel you to get there? What threats do you foresee if you don’t get there? What is the role of technology, including AI? What’s holding you back?
At the ‘Empathise’ stage, a mixed methods approach - think workshops, interviews, surveys, and observations - can unearth invaluable insights. ‘Explore in depth, using the 5 whys to minimise noise, pinpoint critical pain points and get to the true causality behind them’, Llewellan adds.
With precisely defined problem statements, you are ready to ideate and investigate solutions to each. Ask: How might we? What do we need to create or procure? What do we need to redesign? Who do we need to engage? Are we even ready for AI integration?
Llewellan advises using a DFV (Desirability, Feasibility, Viability) matrix to rate each solution, creating use cases, goal-oriented pathways for implementing AI technologies that align with your organisation's strategic objectives. Designate clear ownership for each use case to ensure accountability in driving them forward.
With this strategic approach to AI implementation, you can avoid haphazard adoption and ensure that AI technologies are integrated purposefully and effectively.
ROI focus: Evidencing value
‘“How much is it going to save me?”, is one of the first questions we get, particularly if the organisation doesn’t have a lot of budget to spend on innovations,’ Llewellan notes. Lack of confidence around the ROI of investing in AI can be a barrier to adoption in many organisations.
In the 'Empathise' stage, while understanding challenges and stakeholder needs, seize the opportunity to explore diverse stakeholder expectations of ROI. What outcomes do different groups anticipate? What benefits are they seeking? Which metrics are essential, and how should they be presented?
This information is crucial to focus your problem statement and evaluate potential solutions using the DFV matrix, highlights Llewellan – enabling you to set specific, measurable, quantitative and qualitative objectives that align with your organisation's strategic goals and stakeholder expectations.
Prototyping and testing also play a pivotal role, demonstrating ROI by creating a compact yet fully functional model of the AI solution. By simulating how the technology will operate, stakeholders can witness potential efficiencies, improvements, and cost savings in real-world scenarios. This tangible evidence, coupled with user feedback and performance metrics, allows organisations to make a data-driven case for AI adoption.
Change management and stakeholder engagement: Bridging the gap
What happens when your employees are concerned about job displacement, uncertainty about their roles evolving, and potential privacy and data security issues? Lack of engagement or resistance to AI adoption.
It's time to open the floor and involve everyone.
Design thinking provides a solution by actively involving employees in the process. The very first step is ‘Empathise’, seeking to understand their experiences, views and concerns, fostering a supportive atmosphere. Llewellan emphasises the importance of engaging cross-functional teams, breaking down the barriers that often separate different divisions and causing frustration and counterproductive efforts. And continuing the process with open and transparent communication fosters a collaborative environment that promotes understanding, builds trust, and ultimately garners stakeholder support and reduces resistance to for AI initiatives.
Involving stakeholders early on has another benefit: they take ownership of decisions and feel more invested in the successful implementation of AI initiatives. Llewellan underscores how important this is: ’70-80% of all Proofs of Concept (POCs) fail at the first base because the basics of collaboration and ownership were not done correctly’.
Technical complexity: Taming the monster
Over time, organisations have amassed a labyrinth of technologies, from analogue relics to cutting-edge digital solutions. This amalgamation, dubbed 'the Frankenstack' by Llewellan, can be overwhelming.
It’s time to manage that tech monster!
Design thinking shapes the path towards a structured framework for integrating legacy assets and new technologies into a cohesive, modular, and manageable structure
The first key point to remember is that AI's effectiveness hinges on well-structured data. Llewellan emphasises ‘rubbish in equals rubbish out’ - if the quality of your data input is subpar, AI performance will suffer. As you ‘Empathise’ and ‘Define’, identify whether your physical infrastructure and data are ready for AI integration. Do you need to make any enhancements as a first step? What potential solutions are there?
Llewellan adds that this approach also facilitates the adoption of new digital assets that align with the overarching strategy, effectively circumventing the chaotic complexities typically associated with technological evolution.
And Llewellan’s final piece of advice?
Develop your own skills in AI adoption. Leverage online resources and courses, many of these are free or very low cost. Explore open-source tools and platforms. They may not have the comprehensive features of premium tools but are a great starting point for AI experimentation.
The integration of design thinking in AI adoption significantly contributes to overcoming the pain points and challenges organisations face in implementing this transformative technology. By connecting with stakeholders, defining clear objectives, and showcasing AI's potential through practical prototyping, design thinking can help steer your organisation and drive a more seamless, effective and forward-looking AI adoption journey.
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