In this article I would like to share some of the insights that I’ve gathered on AI over the years and also how we at Scopernia approach our AI methodology.
By doing that, I’m giving away part of our core knowledge, but I believe the urgency is big enough to do so - making the wrong decisions could harm both your business and society.
This post discusses the following topics:
Why you need a profound understanding of AI and a real strategic approach for your organization.
What questions do you need to ask yourself: the 3 horizons of AI.
A 5-step approach to create your own AI strategy: from ‘head in the clouds, to feet in the mud and back’.
Please do not hesitate to reach out if you have any questions about AI, our approach and how we can help you. Good luck!
The need for an AI-strategy
If you are reading this post, it probably means that you are interested in Artificial Intelligence. If that's the case, then you already understand the incredible speed at which AI is currently transforming the world. Every day, new players roll out their own AI models, aiming to compete in what is considered the new gold rush. Every day, existing players like OpenAI and Meta are releasing new features, new applications, and better versions of their AI models. I think it is absolutely justified to talk about a true technology arms race.
Therefore, if you're reading this post, you're likely seeking answers that go way beyond yet another demo of yet another mind-blowing feature.
I've given dozens of presentations in recent months, many of them on AI. I can hear you think: are you also jumping on the AI bandwagon because it’s popular? Reality is, in our company, Scopernia, we have been writing, speaking, and working around AI since 2017. That was 7 years ago, long before OpenAI launched ChatGPT and thereby started a revolution. In those 7 years, we had the opportunity to work with banks, retailers, and others, trying to answer this key question:
how will AI strategically impact our market and our organization
—a question that becomes increasingly relevant with the recent explosion of AI.
It is no doubt interesting trying to follow all the most recent applications and possibilities. It is fascinating to see how gold diggers are all rushing to this new Wild West of AI. Dozens of new GPT-wrappers (applications built on top of OpenAI’s GPT model with a specific application and use case in mind) emerge daily, all trying to sell you a $9.99 monthly subscription to improve your writing skills, help you with LinkedIn marketing, create storyboards for your videos, help find mental balance, and assist you with music theory or your gardening, among any of the zillion other things where AI can assist.
You can easily get lost in all of this daily AI-violence, without ever asking the question:
what does AI truly mean for my organization and, by extension, for society?
If you want to approach AI sensibly, you need to approach it strategically. And that is how we at Scopernia try to help our clients, by answering the strategic questions.
The AI-horizons
If you want to understand the real impact of AI, you should approach it with different horizons. The model below shows what I mean and I will dive deeper into each of the horizons.
Horizon 1: AI and everyday human skills
An epiphany that recently came to me about AI is that its real impact on people, the economy, and society will come from the many small things AI can do.
Yes, of course, hardcore AI is revolutionizing many industries and domains in an incredible way: AI is good at fraud detection, assisting with cancer diagnoses, accelerating the prospect of nuclear fusion energy (by predicting plasma behaviour and thus maintaining a stable fusion reaction for a longer period), and even discovering new antibiotics.
These and many more possibilities of AI are mind-blowing if you think about it, yet I believe the real impact lies elsewhere. According to Forbes, there are 1 billion knowledge workers globally, a number likely to increase in the coming years. What if AI is just good enough to take away 30% of their tasks or even jobs? We would be talking about hundreds of millions of people affected by this. Even if AI never becomes much smarter in the domains it already excels in, the economy of scale would do the rest.
The same can be said for humanoid robots and other robotic innovations we are currently observing. At the current rate of evolution, these robots will be capable of performing relatively simple tasks such as organizing and sorting laundry, managing stock inventory, cleaning up, and taking out the garbage.
While writing this post in a restaurant in Groningen, The Netherlands, I observed a robot autonomously transporting dirty dishes to the kitchen—a simple task that likely provides employment to millions of people globally. If we consider the effect on the billions of individuals in blue-collar jobs, the impact could be even more significant.
Now, turning our attention back to generative AI and the current state of affairs regarding its role in knowledge-based jobs or potentially replacing them, it is safe to say, without intending to be exhaustive, that AI already serves as a lifesaver in several domains.
LANGUAGE: write, rewrite, correct, translate, summarise, enhance, explain, simplify, …
MARKETING: copywriting, campaign ideas & management, SEA/SEO optimisation, …
CONSULTING: research, brainstorming, creative outlines, scenario planning, knowledge management, …
(PERSONAL) ASSISTING: email management, agenda management, event organiser, meeting reports, …
SUPPORT AGENT: first-line help, contact center, chatbot, 24/7 access to (company) knowledge, …
IMAGE CREATION: imaging for presentations, campaigns, stock photos, art, …
VIDEO CREATION: music videos, explainer videos, company videos, campaigns, art, …
AUDIO CREATION: music for corporate videos, campaigns, stock music, art, …
And of course so much more.
So, as a company looking to create a strategic approach towards AI, it is crucial to understand how these everyday small tasks can significantly impact your staff's activities. If this impact is significant, it will have a huge effect on your business and HR policy. Once you grasp the scale of this impact, referred to as the first horizon, you can begin to ask yourself key strategic questions about your organization and move on to the next horizon.
Horizon 2: impact on company strategy
To better understand the second horizon, I'd like to introduce a model that has been instrumental at Scopernia for over a decade. Initially conceived to highlight the differences between the digitization of an organization and its real transformation, this model is equally applicable to AI strategies.
A typical organization is distinguished from its competition by four key pillars:
PROPOSITION: what you sell, your solution or product
MARKET & COMPETITION: the space in which you operate, your competition, new challengers, …
CUSTOMER RELATIONSHIP: how you connect to your customers, your channel and go-to-market
ORGANIZATION: your internal organization and capabilities needed to operate
When you use digital tools or AI solely to optimize your channel and internal organization, you are focusing on the optimization and automation of the existing business model. However, this does not necessarily mean your business or market is evolving or transforming.
Real (AI) transformation begins when technology impacts the core of your business, including your products or business model. Often, organizations are not agile enough to embrace this change, allowing market challengers to innovate and leave them with outdated models, as depicted in the subsequent image.
To determine whether AI is merely optimizing and aiding in automation or if it's truly transformative, consider the following questions:
Proposition:
How might our product and proposition change due to AI?
Customer relationship:
What are the changing market needs?
What are the customer needs and expectations?
Market & competition:
What are other (existing) players in our sector doing?
Are new players emerging rapidly?
Organization:
Which tools are specific to our activity and what should everyone use as standard generic tools?
What should our strategic HR policy be, and what could be the possible (societal) impact of that?
How do we set up AI governance, and in which department do we place the ownership of AI?
How do we map out the risks, how do we manage them, and how do we ensure compliance?
Providing solid answers to these questions should allow you to answer the following two existential questions for your organization:
Does AI pose an existential threat to our sector and our activities within it, or do we perceive it as an opportunity instead?
and
How do we avoid shooting at everything that moves and becoming an AI-chaser? How do we focus our resources and attention on the right things?
This image summarizes all the questions from the second horizon.
Horizon 3: impact on society
By looking at the impact of AI on the first two horizons, it becomes clear that the impact on society will be massive. When you create a strategic approach as a company, you cannot escape your broader social responsibility.
The third horizon of AI, "Impact on Society," invites us to examine the broader societal implications of artificial intelligence. As organizations navigate the transformative waves of AI, it's imperative to look beyond the immediate benefits and hype to consider the long-term societal challenges and changes we are poised to face. This broader perspective is not just optional; it's a responsibility for those at the forefront of AI development and implementation.
Moving Beyond the Hype
The excitement surrounding AI's potential has led to a surge in media coverage, initial usage and investments. However, as we move beyond the initial hype, it's crucial to critically assess AI's real-world relevance, impact and ethical considerations. This means looking at the technology's applications with a discerning eye, recognizing the real use case and also its current limitations for your organisation. Don’t use AI because it’s here. Use it because it makes sense.
AGI Eating the World
The prospect of Artificial General Intelligence (AGI) — AI with the ability to understand, learn, and apply knowledge across a broad range of tasks — presents a paradigm shift in our societal structure. While AGI offers unprecedented opportunities for advancement, it also raises significant questions about human relevance and autonomy in a world where machines can outperform human intelligence. While this might create existential challenges on the longer term, I believe we should focus on more pressing issues that are already here today.
AI Eating Jobs
One of the most immediate concerns is AI's impact on employment. Automation and intelligent systems are already reshaping the job market, with certain roles becoming obsolete. This transition necessitates a rethinking of workforce development, education, and social safety nets to ensure individuals can thrive in an increasingly automated world. This challenge will demand significant ethical responsibility from both employers and policymakers. Will the focus of future development be on cost-cutting and process optimization, or will it prioritize purpose and improved service through collaboration between people and AI? This question is central to the discussions in several of my books.
Economic Singularity
The concept of the economic singularity means that we cannot infinitely continue to optimize production efficiency (in this case, by using AI and automation) at the cost of employment. Unless we crack the code of some kind of universal basic income, this evolution will potentially decrease purchasing power. The Economic Singularity might happen when we reach the point where goods and services are produced at the lowest possible price, yet where people no longer have enough purchasing power to afford them, leading to a collapse of the economy.
Confidentiality & privacy
Using AI, the imperative for businesses to uphold confidentiality while protecting personal and company-sensitive information has become increasingly complex. AI's capacity to process extensive datasets not only poses significant privacy risks to individual data but also threatens the security of proprietary company knowledge and trade secrets.
Copyrights
AI's ability to create and innovate brings copyright issues to the forefront. Determining ownership of AI-generated content and protecting intellectual property rights in the age of machine creativity are pressing challenges that require clear legal frameworks.
AI Hallucinations
AI systems, particularly those based on generative models, can produce "hallucinations" or outputs that are disconnected from reality. These inaccuracies can have serious implications, from misinforming decision-makers to perpetuating falsehoods.
AI Bias, Racism, Sexism
AI systems inherit biases present in their training data, leading to outcomes that can reinforce racism, sexism, and other forms of discrimination.
Fake News, Fake Reality
AI's capability to generate realistic but fabricated content — from deepfakes to synthetic media — poses significant challenges in distinguishing truth from fiction. This capability amplifies the spread of misinformation and erodes trust in media and institutions.
Responsible AI & Legislation
The call for responsible AI underscores the need for ethical frameworks and legislation that guide AI development and deployment. This involves creating standards that ensure AI benefits society while mitigating harm and protecting individual rights. In response to these challenges, new rules and legislation are currently in the making across various regions, including the EU, UK, and US, aimed at governing the use of AI technologies.
AI Escaping the Computer
The concept of AI "escaping the computer" refers to AI systems interacting with the physical world in autonomous ways, from wearables to autonomous cars, from humanoid robots to invisible IoT devices. This raises concerns about safety, control, and accountability when AI actions have direct physical consequences.
This third horizon of AI's impact on society urges organizations to remain vigilant and proactive in addressing these societal questions and challenges. It is important for them not to focus solely on their own tactical and strategic objectives.
The full picture
The image below summarizes the key considerations, questions, and challenges of the 3 horizons. Again, it’s imperative that organizations approach AI from all angles described in order to create an AI strategy.
A method to create your AI strategy
I’ve been in this business for around 30 years now, and I have to admit that this is the first time in my career that it has become difficult to follow the speed of innovation in the domain of AI. On the other hand, much of the experience from our previous digital and societal transformation work and methodologies comes in handy when creating a way to address the key questions and challenges.
One of the problems in finding the right approach is that you need to address this both practically (horizon 1) and strategically (horizons 2 and 3) at the same time. In order to do so, you need a methodology that switches from board and exco levels to more practical experimentation.
The image below shows the different steps in our methodology. Let me briefly explain what each step means and how it’s done.
STEP 1: DEMYSTIFYING AI
In the first session, we provide inspiration and present the current state of AI, including showcasing the many possibilities in general and in a specific industry. We perform a strategic impact analysis to understand the possible implications on the organization.
The aim is to inspire and inform the strategic leadership team and also to define the concrete lenses for the next step, the EXPERIMENT ARENA.
STEP 2: EXPERIMENT ARENA
This phase involves hands-on experimentation and validation of different AI tools to test and understand how AI can already be usefully applied to your business. The focus and areas of the experiments are set through the lenses from the previous step.
This phase can take several weeks or even months or can be condensed into a hackathon-style format. The people involved in this phase have a more practical (business and tech) profile, which allows them to evaluate for themselves what the current usefulness of AI already is.
The outcome of this phase is a concrete report that describes the current state of AI, how it can already be applied, and what the future potential is.
STEP 3: FUTURE VISION
This is the most exciting phase of the project. In this FUTURE VISION phase, the strategic leadership team tries to assess the conclusions of the EXPERIMENT ARENA to understand how the business will be impacted in the (near) future. As explained previously, this strategic vision should not only deal with the company or industry but also with the broader societal evolutions triggered by AI, as they can also influence your vision, strategy and operations.
STEP 4: AI STRATEGY
In this phase, the leadership team defines your own position in that future: your choices and priorities as an organization. Here, you answer all the questions from Horizon 2: how does AI impact your proposition, your market position, customer needs, and changes in the organization. This is the phase in which you set your AI goals and define your focus and priorities.
STEP 5: AI ROADMAP
Now that you know what AI means for your business and you have set your strategic goals, it's key to translate this into a concrete roadmap. This contains actions, AI governance & ownership, investments, pilots, tools, HR implications, partnerships, etc.
Let the real action begin."
Let us help you
This extensive blogpost contains all the necessary elements you need to get going in this exciting time of AI.
Still, I can imagine that you might need guidance and help to set this up and manage the different steps in the process.