We’ve been on an exhilarating journey in my previous blogs, where our caped superheroes of data, AI, and change guided us through the dynamic landscape of data integration and business transformation.
People often gravitate to one superhero, but in our story, we need all three to change the world – what is the superpower of data worth, without AI and the superhero of change?
As we gaze into the future, it's time to summon our inner superheroes and challenge the norms and ideals that have shaped our perception of AI. And let's not just talk theory; let's get practical about accelerating results.
In this blog, we'll don our capes once more and embark on a daring exploration, pushing the boundaries of conventional wisdom and daring to ask the tough questions.
As we plunge into the provocative realm of challenging the ideals of AI, where we'll confront assumptions, dissect misconceptions, and forge a path towards a bold new vision of AI's role in shaping our world.
Get ready to harness the powers of innovation, resilience, and critical thinking as we challenge the status quo and ignite a paradigm shift in our understanding of AI.
The adventure continues, and with our unwavering resolve, we'll explore the possibility and transformation potential, as well as how to revolutionise results.
So let’s challenge the ideals and explore the practical steps to accelerate results and value returns from data.
Today, we live in a world where Artificial Intelligence (AI) has emerged as a transformative force, promising to revolutionise industries, reshape business models, and drive unprecedented growth.
But how realistic is that? Is the reality driving value, or is just hype?
To turn the hype into reality, we need to make the most of the power trio: Data, AI and Change Superheroes.
In our quest for AI mastery, but more importantly realising tangible value – something that is missing from a lot of the work we see today – it is essential to recognise the Power Trio and not just rely on one superhero independent of the others.
While each possesses formidable strengths individually, it's their synergistic collaboration that unlocks the true potential of AI. Think of data as the foundation, providing the raw material for AI's analytical prowess.
AI, in turn, acts as the catalyst, transforming data into actionable insights and driving informed decision-making.
But it's the superhero of change that ensures these insights translate into tangible outcomes, guiding organizations through the complexities of implementation and fostering a culture of continuous improvement and a focus on measurable results and realised value.
Despite this, very few firms are set up well to support the three Superheroes, or even make them feel welcomed. Worse, organisations typically invite them in at different times and even have some turn into villains.
The Change Superhero brings the opportunity to realise value, fast. But also, if harnessed correctly, Change should not just be about realising the outcome, but also directing the input – a mastery at facilitation and Discovery.
Discovery is the process by which all value is derived and the entry to having a clear business question, results and monetisation of data – see our main page for blogs on how we do Discovery and where you can accelerate your journey with Discovery.
Change brings to bear the symbiotic relationship between humans and machines. While AI offers unparalleled capabilities in processing vast amounts of data and identifying patterns, it is human intuition and creativity that ultimately drive innovation. By fostering a culture of collaboration and trust between humans and AI, organisations can harness the collective intelligence of both to tackle complex challenges and drive meaningful results through effective change.
Change also enables the practical strategies for accelerating AI results – from charting a roadmap for success and a clear target for results. This involves defining clear objectives, identifying key use cases, and establishing metrics for measuring progress. It's crucial to involve stakeholders from across the organization in the planning process, ensuring alignment with business goals and fostering a sense of ownership and accountability.
Discovery as part of the Change process enables test and learn and clear prototype / pilot projects that serve as invaluable learning opportunities, allowing organisations to test and iterate on AI solutions before scaling them across the enterprise.
With effective change, we challenge the norms and ideals of AI, we have the opportunity to ignite a paradigm shift in our understanding of its role in shaping our world. By embracing practical strategies for accelerating results and harnessing the collective power of data, AI, and change superheroes, organisations can unlock new frontiers of innovation, drive meaningful change, and create a brighter future for all. The adventure continues, and with our unwavering resolve, we'll explore the possibility and transformation potential of AI, revolutionizing results one step at a time.
Here are the practical steps that management can take to extract value from data, AI, and change:
- Define the ideal future and clear objectives on how to get there
Start by imagining the ideal future you are aiming for and what the outcome is you want to realise and by when. Unless you can’t paint this as a story and clearly articulate picture, you should stay at this step until you can.
Clearly defining the business objectives and outcomes you want to achieve as an organisation and how data, AI, and change initiatives can realise this and make the difference. Check these objectives align with your organisation's strategic goals to ensure focus, but also that you are clear on what the measurable results are you are looking for.
2. Breakdown your future into near term outcomes and questions
Initially turn the outcomes you are aiming for into smaller outcomes that can be realised at month 1 / 3 / 6 / 12 and 24 months. For each set, articulate these as business questions you need to solve to realise the value milestones that aggregate neatly to your end ideal.
The joy of business questions, if articulated correctly, gravitate a team towards and galvanise them without knowing all the answers. If phrased correctly, they drive action and include measurable outcomes that are small enough to realise value against fast.
The key is to focus on the questions with the potential for high impact and quick wins first to build confidence and value early, then broaden out.
3. Construct the dream team
The cross-functional team comprising individuals from different disciplines, including data scientists, data engineers, software engineers, design thinking, business analysts, and change management experts. If there are gaps in these skills, there will be gaps in the results, or the scalability and re-use of what is created.
Foster collaboration and diversity of thought to drive innovation and problem-solving, and provide a common language and framework to use. In our case we created the Institute of Clever Stuff Conversational Analytics process to do just that.
4. Use the ICS Conversational Analytics process as an operating model for success
The Institute of Clever Stuff Conversational Analytics process is designed to foster collaboration, but also to start small and grow – do the minimum required to drive value and iterate from there. It is also designed to ensure a focus on results and inject innovation and first principles thinking at the appropriate times. It has been designed by our Superhero Trio to be a really effective collaboration model, but designed to get results whilst also being fun too.
5. Measure the results and test to scale for success
There is no point in starting big, designing a new data lake or even LLM without knowing the value. But also change waits for no person, change needs to realise results as we go, not just at the end. Conversational Analytics enables this and as soon as results are demonstrated, we can assess:
- the data readiness for scaling - the quality, completeness, and accessibility of your organisation's data. Identify any gaps or inconsistencies that may hinder AI and change initiatives to grow the solution more widely and scale up results, and develop strategies to address them.
- identify the requirement for broader data infrastructure – sizing what is needed and how much to invest in robust data infrastructure and technologies to support longer term data storage, processing, and analysis. What we did in the early iterations of Conversational Analytics was the absolute minimum to get going and realise early value. Now, if the value case is there, we need to ensure scalability, security, and compliance with data regulations to enable seamless AI and change
- develop the right governance framework – a data governance framework to ensure data integrity, privacy, and security. Define roles and responsibilities, establish data standards and policies, and implement processes for data quality assurance and compliance – but sizing to ensure it fits the value case and potential. We have seen both ends of the spectrum – no data governance at all, and governance so engineered it is hard to get going and deliver value.
- experiment and iterate - a culture of experimentation and iteration is key, encouraging teams to test and learn from AI and change initiatives. Start with small-scale pilots and prototypes, gather feedback, and iterate based on insights – Conversational Analytics is not a one-time process, it is about repeating the cycles and iterating to results and scale, but only as many times as you need to to optimise value.
- upskill through capability development - upskilling and coaching in these steps is key to equip employees with the knowledge and skills needed to leverage data and AI effectively. Provide hands-on training in data analysis, machine learning, and change management to empower teams to drive results and innovation. Conversational Analytics is designed to capture at each step the key elements of the story, the input, the outputs, the code and results. For us, these go into our platform Fortu.ai where we have over 350+ previous business questions and use cases captured for each step of Conversational Analytics. Not only the steps, but the actions, decisions, outcomes and results, so these can be repeated, re-used and matched to future solutions. Fortu.ai is our secret sauce in accelerating results through re-use and accessibility to previous work, code, outcomes and outputs.
- monitor and measure progress – adopt the KPIs and metrics from Conversational Analytics and Discovery to monitor the progress and impact of data, AI, and change initiatives. Continuously track performance against predefined goals, identify areas for improvement, and make data-driven decisions to drive continuous improvement – not just in the solution, but the Conversational Analytics steps too. This will help foster a culture focused on results and continuous improvement, where learning, adaptation, and innovation are embraced at all levels of the organisation. Encourage open communication, celebrate successes, and learn from failures to drive ongoing transformation and results.
By following these practical steps, designed by our very own super-heroes you will be in a great place to effectively harness the power of data, AI, and change to drive value and innovation.
So, we have explored some steps, but now how about putting us to the test.
Do you have a business question, use case or theme for where you can use data, AI and change to realise value?
Send it our way and we can share what Fortu.ai and our accelerators can do or have a conversation with us and our superheroes to share tips to drive value, fast.
If we think your question is compelling enough, you may qualify for a golden ticket – a half day Discovery to test to see what can be achieved and how to look to solve one of your killer questions…. for free.
