For years, AI was like a silent engine running in the background. Most of us were unaware—unconsciously incompetent, you might say. We knew AI existed, but we didn’t fully grasp its potential or what it could actually do. Then, almost overnight, ChatGPT launched, and AI burst into the mainstream. Suddenly, it was everywhere, and we quickly moved from knowing little to realising just how much we didn’t understand. Let’s be honest—who had even heard of LLMs five years ago?
It’s unsettling. As we progress in our careers, we often feel less confident because the more we learn, the more we realise how much we don’t know. Those "unknown unknowns" become "known unknowns," and this gap is driving much of the anxiety surrounding AI. People are jumping on the bandwagon, trying to keep up, but quietly asking themselves, "What does this mean for me?"
Right now, everyone’s talking about AI, and most assume everyone else is already using it. But the reality is, many are still figuring it out—and that’s perfectly fine.
With investment in AI at an all-time high, you might be wondering what your AI strategy should be. Leaders are being asked by their boards, "What’s our plan for AI?" when there isn’t even a clear understanding of what AI means for their organisation.
Here’s the first step to closing the AI literacy gap: admit what you don’t know. It’s okay to say, "I’m still learning." You don’t need to be an AI expert. Just like you don’t need to know how a lightbulb works, you only need to know what it does, how to use it, and how to plug it in. AI is no different. Start with the basics—learn the fundamentals, explore use cases relevant to your business, and build from there. Get comfortable with what AI can do for you now, and bring in experts to help you see the bigger picture.
And don’t be intimidated by the so-called "experts" or the new wave of AI consultants. Even they are still learning. AI is evolving every day, and while they may know more, they don’t know everything. We’re all on this journey together.
How to Build AI Literacy in Your Organisation
Building AI literacy is essential for staying competitive. However, there’s no one-size-fits-all approach. AI literacy must be tailored to your organisation's specific goals and level of maturity. Whether you’re a business leader, an employee, or part of a tech team, understanding AI at the right level can drive significant value.
For business leaders, having a basic understanding of AI fundamentals is crucial. This includes knowing what AI is, its different AI strands, and how it can support strategic decision-making. Without this knowledge, you risk missing key opportunities and falling behind competitors.
Key focus areas for business leaders:
AI literacy isn’t just for leadership—it’s crucial for employees. Tools like Microsoft Co-Pilot, ChatGPT, and other AI-powered workplace tools are becoming essential for boosting efficiency. To truly maximise their value, employees need practical training.
Key focus areas for teams:
Even if your organisation partners with external AI providers, your technology teams should understand AI to better manage these relationships and, eventually, build internal AI capability.
Key focus areas for tech teams:
There are many AI training programmes available, but not all are suited to your specific needs. It’s important to choose training that matches your organisation’s current level of AI maturity.
How to tailor AI literacy:
Building AI literacy is crucial to unlocking the full potential of AI in your organisation. By ensuring that business leaders, employees, and tech teams have tailored, practical AI training, you can drive growth, efficiency, and innovation. Make AI learning a continuous journey, not just a one-off event, and see how it transforms your organisation.
1. What is AI literacy?
AI literacy refers to the knowledge and understanding of artificial intelligence concepts, tools, and applications. It involves knowing how AI works, how to use AI tools responsibly, and how to integrate AI into business processes for better outcomes.
2. Why is AI literacy important for organisations?
AI literacy is essential for staying competitive in today’s digital world. It helps business leaders make informed decisions, equips employees with practical AI skills, and enables technology teams to manage AI implementations effectively.
3. How can AI literacy benefit my organisation?
AI literacy enables organisations to leverage AI tools like Microsoft Co-Pilot and ChatGPT to improve productivity, streamline workflows, and make data-driven decisions. It also helps in developing long-term AI strategies aligned with your business goals.
4. What are some key areas of focus when building AI literacy?
Key areas include understanding AI fundamentals, using workplace productivity tools like ChatGPT, ensuring data protection, and training technology teams on more advanced concepts like Generative AI and machine learning.
5. How should AI literacy training be tailored?
AI literacy training should be customised based on your organisation's AI maturity level. It’s important to focus on practical outcomes, like creating AI use cases, and to ensure that training is an ongoing process rather than a one-time event.
6. Can AI literacy be developed in-house, or do we need external help?
You can develop AI literacy internally with the right training and resources. However, partnering with external AI experts or training providers can be valuable, especially in the early stages of AI adoption, to ensure you are using the most effective strategies.
A recent discussion with our Digital Transformation Leaders community proved insightful, revealing ideas and real-world experiences in developing a winning AI strategy.
Here are the key takeaways:
Link AI Strategy to Business Strategy: Ensure that AI initiatives align directly with strategic goals, enhancing specific business and customer outcomes rather than serving as a standalone technology initiative.
Create Leadership Accountability: Assign a C-level sponsor to ensure the strategic importance of AI within your organisation. Interestingly, there was a mix of organisational approaches to where AI strategy and capability were placed, ranging from centralised under a specific C-suite leader to decentralised across multiple divisions.
Educate and Create a Compelling Narrative: Educate individuals at all levels on AI fundamentals and digital literacy to set the right expectations and foster ideas for leveraging the technology effectively. Given the uncertainties surrounding AI, a robust change and communications management plan is essential to ensure consistent messaging. It's crucial to convey that the technology is often there to augment rather than replace human roles. The group consensus highlighted a significant need for education in this area.
Build AI Delivery Capability and Consider Strategic Partners: Establish a robust AI development capability, which should include a mix of core roles such as data scientists, full-stack LLM developers, AI researchers, and other product roles. Some organisations have developed a blended model of internal resources supplemented by external experts, while others are still exploring the extent of capability needed internally versus externally.
Enhance Data Readiness and Technology Infrastructure:
Focus on enhancing data management and strengthening technology infrastructure to support AI applications. Effective data management is crucial, especially for generative AI, which requires less data than traditional machine learning models. Often, it is necessary to update and better organise knowledge bases with policies, procedures, and documentation that support digital assistants and enterprise solutions like Co-Pilot. The group's discussions highlighted significant variations in maturity and focus across organisations.
Choose the Right Tools and Platforms: Select AI tools and platforms that align with your business needs and technical capabilities. Consider both proprietary and open-source solutions, and evaluate them for scalability, support, and community strength.
Develop a Pipeline with a Blend of AI and Automation Capabilities: For members of the group whose organisations have started their journey, generative AI has opened up numerous new use cases, bringing renewed focus and enthusiasm to existing AI.
Create a Mechanism to Make Informed Make vs. Buy vs. Use Existing Decisions: Carefully evaluate the strategic value of developing AI solutions in-house compared to purchasing off-the-shelf products. Leaders discussed various approaches, noting that some AI applications are easier and more cost-effective to integrate through enterprise solutions like Microsoft's Co-Pilot, while others necessitate bespoke development. We all agreed robust governance to make these decisions is key.
Establish a Robust Delivery Model: Develop a structured delivery model, integrating with existing change processes. This includes governance for the identification, prioritisation and delivery of AI initiatives. Ideally, AI isn’t treated as a separate pipeline but integrated with other change initiatives to ensure adequate allocation of investment.
Begin with Manageable Projects: Initiate AI deployment with projects that are straightforward yet have the potential to provide high impact and quick wins. Many found that starting with low-hanging fruit helps to build momentum and set the right expectations, aiding broader organisational buy-in for more complex AI projects.
AI as a Source of Competitive Advantage: If possible, consider how AI applications can provide a unique competitive advantage to your organisation. A few participants highlighted that their organisations view AI not just as an operational tool but as a core component of their competitive strategy.
The insights shared during our roundtable showed the variation in where organisations are on their AI journey. Overall, it was clear there is still a long way to go to maximise the benefits. The fundamentals of what is required to ensure success, such as sponsorship, change management, etc., are no different from any other large-scale change.
Automation / Artificial Intelligence is reshaping the way organisations operate, driving efficiency and freeing up valuable time. But the question remains - how best can we utilise these newfound productivity gains? Here are some possibilities to consider.
Automation often results in small time savings across many roles rather than substantial savings for a few. In these scenarios, think about consolidating these savings for broader benefits - this could mean new training opportunities, team collaboration initiatives, or collective process improvements.
The ideal choice depends on several factors:
Don't forget to consider the wider economic climate and your organisation's performance. For example, if your business is growing with new roles opening up, you could opt to reallocate tasks to existing staff rather than recruiting new team members.
Any option you choose will bring change, and change can be unsettling. Keep your team in the loop with clear, consistent communication. Develop a robust change management strategy to guide your organisation through the transition, involving HR where necessary - especially if job roles are affected.
Choosing how to use productivity gains from automation is a careful balancing act. By considering your options in depth, understanding your business context, and following a systematic process, you can arrive at a decision that best serves your organisation and its people.
There is an ongoing debate, and an important one, over the necessity of standardising business processes before automating them with technologies like RPA and Intelligent Automation. Logically, it makes sense that you would. Why automate an inconsistent process? Yet, of course, nothing is ever that simple, and we need to consider the bigger picture.
Firstly, are standardisation and automation mutually exclusive?
Secondly, in some organisations, by the time you’ve mapped and standardised everything, years have passed, and your competitors have automated already and are operating at a fraction of the cost that you are.
A different way of thinking about this:
Take a holistic approach to service and process design that combines standardisation, simplification, intelligent automation and technology enhancements to reimagine your services and processes.
Bring together a cross-disciplined team for the design activity. Mobilise a team that brings operational excellence, customer experience, intelligent automation and a technology perspective. Practice the art of the possible together in designing your future state.
Build a transformation plan that unlocks value early and often by sequencing improvements in the correct order. You may, for example, need to implement a workflow orchestration layer to enable a consistent process and data capture before you truly unlock the power of RPA and complementary Intelligent Automation capabilities.
Getting the basics right and considering your current level of maturity is important. However, it should not stop you from designing a future state that combines simple process optimisation with the technology and automation capabilities available to you today. Aside from the value you unlock, it will be hugely motivational for your people to visualise a transformational future state and a logical route to it.
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