From Curiosity to Capability: Making AI work for your organisation
It’s hard to go very far without encountering a conversation about AI. From LinkedIn articles and annual industry conferences to strategy discussions in your organisation, you can bet AI is on the agenda in some shape or form.
Many organisations and business leaders are curious, even excited, about how AI can advance operations, reduce manual work and create opportunities for improved efficiency. But the real question is, where do you start?
In this blog, we’ll look at how organisations can start the transition from curiosity to capability, and how AI can work for your organisation to deliver practical results.
Why AI matters
AI is often positioned as something that organisations should take seriously. But why? At its best, the purpose of AI is to support people to be able to complete their work more efficiently. If used appropriately, AI can produce brilliant results in freeing up our valuable time to focus on work that requires decision-making, creativity and strong relationships, as opposed to repetitive admin.
Not all organisations are built the same, and we can’t use a one-size-fits-all approach when it comes to understanding why AI matters for every organisation. Organisations must look at their own individual environments to understand where they are spending precious time, money, and effort today that could benefit from enhanced tools or insights.
AI and Strategy
Curiosity alone isn’t enough to make AI work for your organisation. To build AI capability, curiosity needs action. That means being intentional and deliberate with what you want AI to look like in your organisation and what the success measures are.
AI should be considered as a key component of an organisational strategy. As part of strategic planning, leaders can understand and identify where AI can support with business initiatives and agree clear goals for its adoption, such as improving customer experience or advancing operations. This process enables organisations to prioritise initiatives and plan for resource requirements to create a roadmap for scaling AI capabilities over time.
In this way, AI initiatives can avoid becoming an isolated pet project and can align with wider business goals and governance.
Preparing your people, processes and technology for AI
Turning AI curiosity into capability requires more than just technology, we must consider the need for suitable organisational readiness. This means getting all our ducks in row to ensure that processes and people are adequately prepared to receive AI change. A useful first step is to host AI awareness sessions and workshops to share knowledge and help to remove the mystery surrounding AI. It’s important to make sure that teams feel part of the change to encourage open discussions and create the psychological safety where teams feel they can voice their questions and / or concerns.
Next, understand your data landscape. Whilst AI works best on quality data, many organisations face challenges with having consistent, accurate and accessible data. Without addressing these factors first, it’s likely the organisation will fail to deliver AI capability and instead fall victim to the classic ‘rubbish in, rubbish out’ problem. Putting in place clear data governance, sanitising legacy data sets and setting new ways of working with data policies and accessibility is key to lay a strong path to building AI capability.
Defining clear use cases is important to specify opportunities for where AI can deliver value and support strengthening the proposal to build AI capability in the organisation. Targeted use cases can also deliver quick wins, crucial to build confidence across teams and leadership and reduce resistance through measurable impact.
Finally, it’s important not to underestimate the importance of building a culture of flexibility and a ‘one team’ approach. For AI to land successfully in an organisation, this typically entails bringing individuals across multiple teams to work together and bring their knowledge and skills to the table. Effective cross-collaboration is needed to learn from failures collectively, avoid blaming others for perceived challenges, and embrace an iterative approach to keep refining the solution until it is fit for purpose.
An amalgamation of all of the above is necessary to get the cogs turning from curiosity to capability.
Turning AI Curiosity into Capability
Once you are confident that the organisation has been set up to succeed, the next step is to start testing and applying AI. As previously mentioned on the importance of an iterative approach, this can be applied as small pilot projects to enable teams to test AI in a low-risk, no-blame environment where the focus is to learn and refine.
Investing in AI learning and development is essential to ensure that you have the right mix of skills and technical expertise in house, particularly as and when AI is deployed on a larger scale. Not everyone in the organisation needs to be an AI expert or coding guru. For many roles, a basic understanding of how AI works and how to use it effectively is enough to deliver benefits. For example, a simple introductory upskilling session on prompt engineering is a practical first step in getting people comfortable with AI and what it can do for them. Whilst many of us are already experimenting with AI in some form, the quality of outputs often improves drastically once people understand how to structure requests using clear language. Getting the basics right through targeted training initiatives can have an immense impact on everyday work activities without the need for complex training programmes.
Integration is another key consideration on the curiosity to capability journey. AI implementations can alter roles, workflows and decision-making routes. Where applicable, AI must be embedded into existing processes. These alterations can create uncertainty and even resistance across teams. To effectively create and manage AI capability, change management and governance must work hand in hand. On one hand, a clear change management approach is necessary to understand the impact on individuals’ roles and to support teams through new ways of working with clear communications, tailored training and leadership alignment. On the other hand, governance provides clarity on accountability, escalation paths and how AI will be used in the organisation, both ethically and responsibly. Appropriate governance controls and policies also need to be in place to address potential AI limitations, such as bias, data privacy issues and security concerns. This also includes the ‘human in the loop’ checks are in place, to ensure that people have the opportunity to review AI outputs and challenge where necessary.
Considering change management and governance in unison supports organisation to integrate AI into day-to-day operations with confidence knowing that the appropriate controls and people focus is in place.
Final thoughts
Moving from AI curiosity to capability is a path of experimentation, learning and integration. Success is dependent on how well AI is integrated into new and existing processes, how clearly governance is defined and adhered to, and how effectively people are supported through the change. Capability doesn’t happen overnight, an intentional and considered approach to AI will support organisations to realise lasting value from AI.
Whether you are at the very start of your AI journey or looking to scale existing initiatives, we help turn ambition into action, ensuring AI becomes a capability your organisation can rely on, not just a concept to explore.
If you would like to discuss how Nine Feet Tall can support your organisation to build sustainable AI capability, get in touch with our team.
