In the grand narrative of human history, there have been pivotal moments that reshaped our societies and propelled us into new eras of progress. From the Agricultural Revolution to the Industrial Revolution, these milestones have marked significant shifts in the way we live, work, and interact with the world around us. Today, we stand on the precipice of another transformative moment: the AI Revolution.

As businesses grapple with artificial intelligence’s implications and potential to change industries, the question arises: What role do we, as tech specialists, play in this monumental shift?

Timspark initiated a conversation with Carl Eidsgaard, Head of AI Advisory & Solutions, to cover the most intriguing AI questions. From Carl’s perspective, the answer lies in understanding the phases of AI adoption and transformation and navigating their challenges and opportunities.

But there’s more to unpack, so keep exploring the full interview below.

About Carl Eidsgaard, AI consultant and Explorer

Hanna Strashynskaya (CMO at Timspark): Could you share a bit about yourself and how you got into AI?

Carl Eidsgaard: My name is Carl Eidsgaard. Originally from Norway, I’ve called Amsterdam home for the past seven years, working primarily in data analytics and AI projects with companies like Oracle and Microsoft. However, my fascination with AI spans over a decade, sparked by Ray Kurzweil’s groundbreaking book, ‘The Singularity is Near,’ which I encountered during my time in business school around 2012-2013.

Kurzweil’s vision of AI’s potential societal impact prompted me to pivot from a finance career to pursue opportunities in technology. I consider myself an AI explorer, rather than an AI consultant, navigating this new frontier with a blend of technical insight and a passion for demystifying AI for others. Despite over a decade of immersion in this field, I’m continually humbled by its rapid advancements and the profound implications it holds for society.

Carl: As for the technical side of things, the past 15 years have witnessed extensive research laying the groundwork for today’s AI landscape, a realm I’ve passionately explored for the past decade. Notable milestones, such as the creation of AlphaGo by Google DeepMind in 2013, marked significant leaps in AI’s capabilities, particularly in navigating complex scenarios like the game of Go. Unlike previous victories, such as IBM’s chess triumph in the 1980s, AlphaGo’s success showcased the power of machine learning and its ability to surpass human expertise.

Since then, the pace of advancement has exceeded my expectations. From the unveiling of ChatGPT in late 2022 to the present day, progress has been nothing short of astonishing. Witnessing this evolution every week invokes a spectrum of emotions, from grappling with existential questions to harboring boundless optimism for the future.

I see an evident potential for this to transform society and human civilization as a whole.

The impact of AI adoption on business

Hanna: What’s your plan for leveraging AI to shape our society and future? And how do you see yourself contributing to AI adoption and transformation in businesses?

Carl: That’s a fascinating question! Considering the rapid advancement of AI and its potential to render roles like mine obsolete in the future, I entered AI consulting fully aware of this eventuality. However, before reaching that point, it’s crucial to understand the various phases of AI adoption.

Currently, we’re in the informational phase, where businesses and individuals are exploring the capabilities of AI and its potential applications. The possibilities are vast, ranging from enhancing productivity to upgrading problem-solving approaches. This leads us to the next phase: adoption. Technologies like ChatGPT represent a significant leap, effectively serving as vast repositories of human knowledge accessible at our fingertips. Integrating such systems into our workflow can augment our capabilities, improving work quality and expanding our understanding of the world.

While the prospect of AI eventually autonomously handling tasks we currently do is on the horizon, there are significant stages of adoption and adaptation to navigate before that becomes a reality.

Hanna: How do you usually evaluate a company’s readiness for adopting AI?

Carl: AI adoption indeed varies greatly from one business to another, influenced by a myriad of factors, including cultural predispositions, existing infrastructure, and budgetary considerations. However, while each journey is unique, there are common traits indicative of readiness and progress in AI integration.

Firstly, I assess a company’s future-forward mindset. Are they cognizant of the seismic shift AI represents, not just for their industry but for society as a whole? Recognizing AI’s existential implications is crucial for embracing its potential and navigating its impact effectively.

Secondly, adaptability is key. Given the rapid pace of AI advancement, organizations must be willing to evolve and pivot as new technologies emerge. Finally, C-level buy-in is instrumental here; a top-down approach to AI adoption significantly enhances the likelihood of success by fostering a culture of innovation and agility throughout the organization.

Hanna: Starting from the bottom and working up can be an option, too, but it’s usually a longer route, correct?

Carl: Yes, it is. And things tend to become easier overall. For instance, securing a budget for a ChatGPT subscription is typically much smoother when done at the C-suite level for a global organization compared to being an executive with a single business domain, right?

Having buy-in from the C-suite definitely streamlines the process.

Common metrics for AI ROI evaluation

Hanna: As an AI explorer and consultant, how do you assist businesses in defining and measuring the return on investment for AI implementations?

Carl: When considering return on investment and objectives in AI integration, it closely mirrors the framework of a typical digital transformation journey. Aligning with established practices, organizations focus on setting clear goals, defining key performance indicators, establishing baseline performance metrics, and monitoring progress both in the short and long term.

However, there are notable distinctions in implementing AI compared to traditional analytics or ERP/CRM systems. Firstly, AI can automate much of this process through the utilization of large language models (LLMs) trained specifically for the organization’s context. This contextual analytics fabric enables a more tailored and efficient approach to data analysis and decision-making.

Secondly, the AI revolution unfolds both incrementally and in waves, necessitating adaptation to its evolving landscape. This concept of an ‘intelligence explosion’ implies exponential growth, which humans may struggle to navigate effectively. Planning for both incremental advancements and transformative waves is essential for maximizing the benefits of AI adoption while mitigating potential disruptions, such as job displacement.

Most common challenges of AI adoption

Hanna: What are the common challenges that companies encounter when integrating AI into their current systems and processes?

Carl: AI adoption manifests uniquely in each organization, yet certain domains commonly shape this process. Firstly, there’s the technical landscape and legacy systems, an area particularly pertinent in enterprise settings where outdated infrastructure still persists. Legacy systems can present hurdles to automation, potentially impeding the integration of AI-driven workflows.

Secondly, data quality and management emerge as critical considerations, especially for companies developing their own AI models. A foundation of clean, reliable data is essential for effective AI implementation.

Moreover, fostering AI literacy within the organization is paramount. Without a fundamental understanding of AI principles, companies risk underutilizing its potential. Investing in AI education or consulting expertise can bridge this gap.

Ethical and regulatory concerns loom large in the AI landscape, too. Prioritizing ethical AI practices not only safeguards against potential harm but also enhances trust and reputation. While regulatory frameworks are gradually emerging, they often lag behind technological advancements. Therefore, organizations must proactively address ethical considerations and compliance, as waiting for regulatory mandates may lead to obsolescence.

Hanna: Let’s delve into the ethical challenges that businesses may face during AI implementation. Could you provide some real-world examples from your experience?

Carl: When embarking on AI implementation, considerations vary depending on the specific context. For organizations developing their own models, such as ad or contextual analytics frameworks, several key factors come into play.

First, addressing biases and ensuring fairness in datasets is the first thing to do. This involves mitigating biases within datasets, perhaps by extending them with synthetic data to create a more representative sample. Additionally, taking advantage of system prompts in large language models can help refine interactions and mitigate biases in user interactions.

Bias in large language models isn’t as dire as it seems initially. These models are typically trained on baseline datasets, which are readily available. During this training, the model learns a wide range of parameters, ensuring a balanced understanding. However, challenges arise when incorporating specific datasets, as inherent biases within them may skew results.

Again, one effective method to mitigate this is through synthetic data, which mimics real data points but ensures diversity. For instance, in a recruitment-based system biased towards hiring white males in their 50s, diversifying the dataset before training can prevent the model from perpetuating this bias.

Privacy and ethics are equally significant concerns, particularly in the absence of firm regulations. Adhering to the principle of treating others as you would like to be treated yourself serves as a guiding ethos. Ensuring robust security measures, such as segregating AI systems into dedicated environments, safeguards against potential vulnerabilities.

Transparency and explainability are essential aspects of AI development. Thorough documentation of processes and solutions facilitates comprehension and enables seamless knowledge transfer among team members. Organizational education efforts ensure that stakeholders understand the workings of AI systems and can contribute effectively to their deployment and utilization.

Hanna: To broaden our perspective, let’s touch on the common concern about AI replacing jobs. How do we address this ethical challenge?

Carl: Let’s take a step back and address the notion of job obsolescence or worker displacement. Currently, we’re not at a stage where this is imminent; there’s still time for transition. However, in the timeline of AI development, we’ve already reached one significant milestone: the implementation of AI with cognitive capabilities, such as ChatGPT, which excels in generating text and is improving in areas like video generation. 

The next milestone is artificial general intelligence, or AGI, where models will match the capabilities of the best human operators. Beyond that lies artificial superintelligence, or ASI, surpassing human abilities across all domains.

This timeline provides context for potential job displacement, but it’s important to note that we’re not there yet. Before reaching that point, society must adapt to AI. Quality assurance by human operators will be crucial in ensuring these models perform as intended. 

While this transformation is significant, it’s essential not to fear being replaced. If managed effectively, this process could lead to a society far superior to what we can currently envision, with possibilities such as more time spent with loved ones.

Hanna: What’s your message to companies and employees worried about being replaced by AI?

Carl: Imagine having the freedom to pursue your interests without the constraint of needing to work for a living. Furthermore, consider the broader implications for humanity as a whole. Embracing technologies like AI could be pivotal in our journey toward becoming an interplanetary species.

Reflect on what truly brings you happiness and use that as a guide moving forward. Personalized AI models, such as a customized GPT, could be designed solely to ensure your well-being financially, socially, and politically. They might even handle tasks like voting on your behalf. Picture a world where everyone could live comfortably, free from financial worries. You could travel, explore new experiences, and rely on your personal AI to educate you whenever needed.

While this future might seem daunting to some, humans possess remarkable adaptability. Consider whether traditional notions of job satisfaction still hold in a world where personal AI can handle many tasks. The potential for such advancements is monumental, reshaping how we live and work in ways we may struggle to imagine today.

AI compliance with industry standards and regulations

Hanna: How can AI implementations be ensured to comply with industry standards and regulations?

Carl: Currently, we lack forward-thinking regulations specific to AI, but this is likely to change as political and regulatory bodies recognize the urgency. In the meantime, it’s crucial to prepare for impending regulations, which can be summarized in one word: ethics. Simply put, don’t engage in practices you wouldn’t want AI systems to perform on you.

Additionally, existing regulations like GDPR need to be considered and ensured to be compatible with AI implementations. This means maintaining compliance in how these systems are used and how data for them is stored, though the specifics may vary depending on the application.

On top of that, AI compliance involves a proactive approach. It means:

• Integrating regulations from the outset and fostering a culture of ethical development;
• Staying informed about evolving standards and incorporating them into AI design;
• Adhering to ethical guidelines and ensuring transparency and fairness;
• Implementing rigorous data governance and conducting regular audits to identify compliance gaps;
• Collaborating with regulators and industry bodies.

By doing all this, businesses can innovate sustainably and responsibly in the AI landscape.

Join the AI revolution to change your business

Summing up, as we stand on the cusp of the AI revolution, our role as facilitators of transformation is clear. By guiding businesses through the phases of AI adoption, assessing readiness, and navigating challenges, we at Timspark can help bring artificial intelligence projects to unexpected heights. It’s time to shape the future of business for generations to come.