Key Takeaways from #Harvest event 9 October 2024

Generative AI (GenAI), and artificial intelligence (AI) in general, is no longer a promise of the future; it’s the reality of today. During the Harvest event on October 9th, 2024, it became clear that AI’s potential, particularly GenAI, is immense, yet many organisations are still standing on the sidelines.

Why? Not because the technology isn’t mature—it is. The real challenge lies in the myths and misunderstandings that continue to slow its widespread adoption. As DB Schenker’s Samuli Salmela explained in his keynote speech, these misconceptions prevent businesses from fully embracing the transformative power that GenAI, especially, offers.

As leaders, it’s time for us to move beyond the myths and recognise AI as a strategic partner, not just a tool. AI can bring profound improvements to our organisational processes and, ultimately, transform the entire business. The question is no longer whether AI is ready – it is. The real question is whether our companies are ready to learn, experiment, and utilise AI in the right way. The businesses that seize AI’s opportunities now will thrive, while those waiting for the “perfect moment” – that never comes – will be left behind.

Let’s explore the key takeaways and insights from the event by addressing the most prominent myths about GenAI and their practical implications for our organisations in overcoming them.

Myth 1: “AI is difficult and expensive”

This belief often stems from a fear of the unknown. In reality, AI has become increasingly accessible, with many easy to use  tools available. The real challenge lies not in the technology itself, but in our willingness as people to adapt. As leaders, we must reframe AI as an investment in both the company’s as well as the people’s future rather than a burdensome expense or a new thing that ends up benefitting no-one.

Myth 2: “GenAI makes mistakes, so it needs constant human supervision”

While it is true that AI is not infallible, neither are humans. The key is to view AI not as a replacement for human intelligence, but as a powerful complement to it. This symbiosis of human intuition and machine processing power can lead to unprecedented insights and innovations for both organisational, as well as wider, good. A need for human oversight in AI related projects depends greatly on the use case and its risk profile. As we learn to understand and trust AI more, we can relax the oversight.

Myth 3: “We don’t have good enough data to use AI”

Perfect data is a mirage. Instead of waiting for an ideal dataset, we should focus on cultivating a data-driven culture where continuous improvement is the norm. AI can actually help us identify gaps in our data and AI can also allow us to start utilising unused or previously difficult-to-use data assets. AI can also refine our collection processes, so again, rather than thinking of it as a technology, think of it as an added competence to your organisation and treat it accordingly. 

Myth 4: “Our data is not safe with GenAI”

In an age of increasing digital threats, this concern is valid. However, it should not paralyse us. Instead, it should motivate us to implement robust data governance frameworks. By doing so, we not only protect our assets but also build trust with our stakeholders. It is also important to use technologies that are secure and enterprise-grade, such as the Microsoft offering.

Myth 5: “AI doesn’t really think”

This myth touches on deep philosophical questions about the nature of intelligence. While AI may not “think” in the human sense, it can process information and identify patterns at a scale beyond human capability in certain cases. Our role as leaders is to harness this power while providing the context, creativity, and ethical considerations and judgement that only humans can offer. This is one of the reasons it is important to focus on your people at the same time as you focus on technology.

Myth 6: “It’s just hype. Companies aren’t getting real benefits”

Scepticism is healthy, but it should not blind us to the real-world impacts of AI. From predictive maintenance to personalised customer experiences, AI is already delivering tangible benefits across industries and will also require a new set of measuring benefits. As leaders, we need to look beyond the hype and focus on practical applications that can drive our businesses forward. AI is like any major technology disruption; its short term implications are overestimated and long term impact under-estimated.

Myth 7: “The models aren’t ready; it is better to wait”

In the rapidly evolving world of AI, waiting for perfection is a luxury we cannot afford. The most successful organisations will be those that adopt a “learn fast” mentality, embracing current AI capabilities, while staying agile enough to adapt to future developments.

Building a Human-Centric, Data-Driven Culture: The Key to Successful AI Adoption

It is essential to remember that adopting AI goes beyond technology—it fundamentally revolves around people. To fully realise AI’s potential, the entire organisation must be engaged in the journey.

Since we’ve been talking about AI like a colleague, we figured, why not ask Copilot  itself, which of these myths resonated the most? “The myth that struck a chord was, ‘GenAI makes mistakes, so it needs human supervision.’ While it’s true that responsible AI use requires oversight in critical fields like healthcare, where accuracy and ethics are non-negotiable, the idea that GenAI always needs human supervision is a bit outdated.

This requires cultivating a culture that embraces data and AI literacy, with human leadership at its core. Such a culture encourages curiosity and experimentation, valuing human creativity and judgement. In this way, employees see AI tools as enablers rather than fearing replacement. This approach creates an environment where both human and artificial intelligence can thrive, laying the groundwork for genuine innovation and growth.

But as any good farmer knows, harvesting is only the beginning. The true value lies in how we refine those fruits—transforming them into products, services, and tangible results that drive real organisational impact. And just as importantly, we must sow new seeds for future growth by budgeting wisely and investing in the AI capabilities that will shape the years ahead. After all, success isn’t just about this harvest; it’s about laying the foundation for future seasons of prosperity.

Authored by:
Kira Sjöberg, GOODIN, Sami Masala & Nino Ilveskero, AIThink & CoPilot, Microsoft. 

Understanding the Learning Stages People in Organizations go through

1. Beginner Level: Grasping the Basics

The journey begins with foundational knowledge. For data literacy, this includes understanding data types, basic data manipulation, and the significance of data in decision-making. For AI literacy, it involves an introduction to AI concepts, what AI can and cannot do, and real-world applications. At this stage, short, introductory sprints focused on key concepts help demystify complexities and lay the groundwork for more advanced learning.

2. Intermediate Level: Enhancing Skills through Application

Once the basics are understood, employees should start applying their knowledge to real-world scenarios. This could involve structured projects or challenges where learners manipulate datasets or build simple AI models relevant to their roles. This stage is crucial for reinforcing concepts and gaining confidence. Naturally understanding that not everyone will utilize data or AI on this level in their roles, but understanding the side of applying is important. Generative AI utilization is however another relevant application mode often in such cases. Organizations can support this through workshops, guided training sessions, and practical hands-on projects that encourage active learning. One example of this is the GOODIN and Splended Data Learning Sprint.

3. Advanced Level: Specializing and Innovating

As learners become more comfortable, they can move into specialized areas such as predictive analytics, machine learning, and advanced data visualization techniques for data literacy. For AI literacy, this might include deep learning, neural networks, or robotics. Advanced learners should engage in longer, more complex sprints that challenge their understanding and encourage innovation within their specific areas of interest. This demands a “fail fast” or “learning by doing” -type of learning culture in organizations and positive and encouraging management practices and commitment that enable failing for learning.

4. Expert Level: Leading and Mentoring

At the expert level, individuals are expected not only to be proficient but to lead initiatives and mentor others. They stay abreast of industry trends and continuously adapt to new technologies. Here, learning involves self-directed projects, leadership in sprints, and contributing to strategic decision-making with data-driven insights become real life value for business.

Implementing Learning by Doing: The Sprint Method

A learning sprint approach can be highly effective in progressing through these stages. Each sprint focuses on a specific skill or project, encouraging rapid learning and application. Here’s how it can work:

• Define Clear Objectives: Each sprint has specific, measurable goals to ensure focus and alignment with business objectives.
• Time-bound Challenges: Limit sprints to a few weeks to maintain urgency and engagement.
• Cross-functional Teams: Include employees from different departments to foster diverse perspectives and collaborative problem-solving.
• Reflect and Iterate: At the end of each sprint, gather feedback and reflect on lessons learned to improve the next cycle.

Supporting the Journey

Supporting employees through this journey requires more than just providing educational resources; it involves creating an ecosystem that promotes continuous learning and application. This includes access to the latest tools and technologies, opportunities for peer learning, and a culture that celebrates experimentation and learning from failure.

As they become more fluent, they will be able to leverage these skills to innovate and lead in their respective fields, ensuring the organization stays competitive in a data-driven future making sure the human drives the AI and not vice versa.

The missing link? Data literacy.

At GOODIN we have observed firsthand the transformative impact of nurturing data literacy within an organisations, particularly from a data governance perspective.

Data Literacy: The Foundation of Data Governance

Data governance is not just about managing data; it’s about managing the people who interact with that data. It is about creating an environment where data is not just available but is also understood, trusted, and effectively used. This is where data literacy comes into play. By educating employees across all levels of the organisation in the language of data, we empower them to make informed decisions, recognise the value of data assets, and adhere to governance protocols. 

Bridging the Data Knowledge Gap

The journey towards data literacy starts with recognising that different roles require different levels of understanding. For instance, your marketing team does not need to know how to run complex data models, but they should understand how to interpret data insights relevant to their campaigns. Tailored training and workshops can bridge these knowledge gaps, ensuring that each team member has the right tools and understanding to leverage data effectively.

Fostering a Data-Centric Culture

When people understand data, they respect it. This respect is critical for effective data governance. A data-literate workforce is more likely to recognise the importance of data quality, privacy, and security. They become active participants in maintaining the integrity of data, rather than passive users or reactive receivers that actually do not use the data available.

Moreover, as data literacy spreads, it cultivates a data-centric culture where decisions are made on a foundation of solid data – in conjunction with gut feelings, a type of internal and experience based ‘data’ that should not be underestimated either.

The Role of Leadership in Data Literacy

Leadership plays a pivotal role in this cultural shift. By championing data literacy and setting an example, leaders can drive the message that data is a valuable asset worthy of investment.

This is not just about allocating budget for training programs; it is about embedding data literacy into the fabric of the organisation’s ethos and this is where leadership has a central role.

The journey towards effective data utilisation is twofold: it’s about having the right technology and about ensuring your people are equipped and enabled to use it. Data literacy is not just a skill; it’s an essential component of a robust data governance framework. 

Data is in the end about telling stories and as telling stories is embedded in people, transforming an organisation’s culture increasingly towards one of storytellers helps also the development of data literacy as empathy is the superpower humans have and applying it to data creates the most wonderful stories!

Oh but How do I Get Going?

Imagine setting your sights on a luminous northern star – a vivid, inspiring vision of where you aspire to be say in, for instance, three years from now. With this beacon guiding your way, you take measured, thoughtful steps, one after the other, steadily building a future rich with empowerment and enlightened by data. Each stride forward is a meaningful progression, a step closer to a culture where data literacy enhances every decision, enriches every strategy, and elevates your organisation to new heights of innovation and insight.

Embarking on the path to foster data literacy within your organisation is a voyage of transformation and growth, not a race to an immediate finish line.  

We are of course happy to help you along your journey – please – just get in touch!

Picture this: a business leader, with a sprinkle of data curiosity, and a data wizzard, with a dash of business intuition, come together. They stand in front of a whiteboard, their ideas flowing and merging. It is in these moments that the real magic unfolds, beautifully blending “what can be done” with “what the business truly needs.”

Now, business leaders are the maestros of understanding market dynamics, the ever-evolving customer behaviour, and those subtle strategic moves that steer your company forward. You have that innate sense of “what is needed” – whether it is identifying market voids, understanding customer pain points, or sniffing out the next big innovation. And while you appreciate the might of data, its finer intricacies might sometimes feel a tad out of reach. You also may have design-thinking experts in your fold, bringing about the out-of-the-box and customer-centric approach.

And here is where the data champions step in. In our world that thrives on data, these professionals are not just number-crunchers. They’re storytellers, pattern-detectives, and visionaries. They hold the keys to “what’s possible” using their vast knowledge of analytics and algorithms. But without the bigger business picture, sometimes their solutions can feel like they are missing a piece of the puzzle. Together we can patch the gap between Business and Data. We learn the language of the other competence and we strive to understand and cooperate.

So, when these worlds collide – say, over a casual whiteboard session – something truly special emerges. The creative business leader lays down a challenge, and our data guru counters with data-driven solutions. By working hand in hand in an ideal world they sculpt ideas and solutions that might’ve seemed impossible in isolation and create solutions that resemble pieces of art.

This harmonious dance of insights and strategy opens avenues for groundbreaking products, smoother operations, and delightful customer experiences. It is a kind of alchemy – a magic rooted deep in teamwork, mutual respect, and combined expertise and the understanding of each others competence as well as the understanding of your own limits.

To wrap things up, merging business smarts with data insights is not just a nice-to-have—it is a game-changer in our dynamic, data-rich era. When we encourage this heartwarming meeting of minds, we are not just connecting dots; we are drawing the roadmap to future innovation where also the enablement of the utilisation of data in organisations is at the core.

And as the brilliant W. Edwards Deming once said:

“Without data, you’re just another person with an opinion.”

But remember, data without context is like a book without a storyline and data that is not available or easy to use is not valuable, so make sure your data is useful and your people know how to utilise it.

Let’s always aim for the best of both worlds!