Generative AI in Digital Transformation: Learning Kerv Series 1 Round Up

Published 24/03/25 under:

From the fundamentals of generative artificial intelligence (AI) to its transformative impact on businesses to what’s next to come in AI, we’ve explored a wide array of topics that are shaping the future of technology and innovation on series 1 of the Learning Kerv podcast. Thanks to our host Rufus Grig and Kerv guests!

Let’s take a moment to revisit what we’ve learnt on Series 1 of Learning Kerv.

The Power of Generative AI

Throughout this series, we’ve delved into the fascinating world of generative AI – a technology that is revolutionising the way we live and work. Generative AI, which includes machine learning (ML) and deep learning, enables machines to create content, make decisions, and solve problems in ways we could only have imagined.

AI is all about machines mimicking human smarts, handling tasks like decision-making and spotting patterns but ML takes it a step further by teaching machines to learn from data, making predictions and decisions without needing a step-by-step guide. Think of it as the secret sauce behind your personalised Netflix recommendations, self-driving cars, and cutting-edge healthcare solutions. Algorithms are like the recipes, while models are the delicious dishes you get after following those recipes.

Now, Generative AI has become part of our daily lives, from chatting with Alexa to having our emails drafted by smart assistants and it’s only going to get more advanced.

Enhancing Customer Experiences

Generative AI is revolutionising industries by automating routine tasks, providing deep insights through data analysis, and creating personalised experiences.

But Gen AI is not just about automation; it’s about creating meaningful interactions. In customer service, it excels in understanding and generating human-like text, analysing speech-to-text data, and summarising interactions, which is helping businesses improve customer satisfaction, also building loyalty and driving growth.

In the pre-interaction phase, AI can triage and recognise customer intent, providing relevant solutions and personalised responses, which reduces the need for human intervention. During live interactions, AI supports agents with predictive routing, real-time assistance, and language translation, ensuring queries are handled efficiently and accurately. Post-interaction, AI generates summaries and categorises interactions, aiding in data analysis and reporting.

By enhancing pre-interaction capabilities, supporting live interactions, and providing post-interaction benefits, Generative AI is transforming customer service, making it more efficient, personalised, and high-quality. As technology evolves, its role in customer service will continue to grow, driving innovation and improving customer experiences.

Want to know more around Gen AI in CX? Listen to the full podcast here ⬇️

Ethical Considerations

With great power comes great responsibility!

As we harness the potential of AI, it’s crucial to consider the ethical implications especially in real-world applications. Transparency, fairness, and accountability must be at the forefront of AI development and deployment to mitigate risks associated with bias and misuse.

How do you monitor AI? This involves distinguishing between information security and data privacy, as both are crucial in protecting sensitive information. Security focuses on keeping data safe, while privacy relates to how data is used and processed. Ensuring both is essential to protecting privacy and preventing any misuse.

AI systems must be safeguarded against security threats, as they can be exploited by malicious actors. Regulatory frameworks like the EU AI Act and the NIST framework in the US are evolving to address these challenges and ensure trustworthy AI practices. Additionally, the environmental impact of AI, particularly the energy consumption of data centers, necessitates sustainable practices to mitigate its effects. Organisations must adopt measures to optimise model efficiency and select environmentally responsible data center locations.

To ensure AI is ethical, it is also vital to address biases in AI models and implement ethical guidelines and accountability measures. This includes testing for bias, auditing for bias, and actively working to mitigate it. Frameworks from organisations like Microsoft and NIST can guide responsible AI practices. By focusing on security, privacy, sustainability, and ethics, we can harness the power of AI responsibly and ensure its benefits are realised without compromising our values.

The Importance of Data

Our podcast series has provided practical insights into how businesses can successfully integrate generative AI into their operations. We’ve shared real-world examples, lessons learned, and strategies for overcoming challenges. However, without quality data AI is rendered practically useless, as it leads to biased or inaccurate predications.

Ensuring accurate, relevant, and up-to-date data, along with proper labelling and classification, is essential for high-quality outcomes. Both labelled and unlabelled data are critical for training robust AI models, allowing them to learn and adapt to provide specific and unique results.

Although AI is accelerating at great pace, human involvement remains essential to ensure the correct data is used and the data is safe while being used and processed. Monitoring and reviewing AI outputs by humans help maintain accuracy, relevance, and security.

By focusing on data quality and security, businesses can harness the full potential of generative AI, driving innovation and efficiency. As AI technology continues to evolve, its applications will expand, offering even more opportunities for businesses to stay competitive and deliver exceptional results.

What’s next?

Agentic AI promises to be the next wave in the AI revolution, as it introduces autonomy, allowing systems to plan, decide, and act to achieve goals with minimal input.

Unlike generative AI, which is reactive, agentic AI operates through multi-step reasoning and long-term planning, making it particularly useful in dynamic environments that require continuous adaptation and strategic execution. This capability enables agentic AI to break down tasks, gather relevant information, make informed decisions, and iterate until reaching the optimal outcome.

The potential applications of agentic AI span across various industries, driving efficiency, reducing costs, and enhancing decision-making processes. For example, it can automate workforce optimisation by scheduling employees and allocating resources, handle customer service queries without human intervention, analyse financial data for investment decisions, and manage IT operations by proactively monitoring systems and installing updates. Agentic AI could even plan your next travel adventure by managing the entire booking process based on user preferences.

Agentic AI also provides less sustainability concerns than other AI developments, as it runs through optimised AI models that leverage specialised AI chips and techniques such as pruning and quantization. These methods reduce energy consumption while maintaining high performance, making agentic AI a valuable tool for both operational efficiency and environmental responsibility.

Looking Ahead

As we wrap up Series 1 of the Learning Kerv podcast, we want to thank our listeners for joining us on this enlightening journey. We’ve covered a lot of ground, but there’s still so much more to explore so stay tuned for Series 2! If you have any questions or would like to talk to us, please contact us today.

At Kerv, we’re committed to staying at the forefront of technological innovation and sharing our knowledge with you. Whether you’re an AI enthusiast, a business leader, or simply curious about the future, we hope our podcast has provided valuable insights and sparked your curiosity.