Artificial Intelligence (AI) and Machine Learning (ML) have become buzzwords in the tech industry, but what do they mean in the context of digital products? How are they being implemented, and what are the implications for product and tech leaders? To answer these questions, we turn to a recent discussion between Viktor, Tech Director, and Claire, Product Director, at Red Badger.
When it comes to the practical applications of AI, Claire points out several areas where AI has been effectively applied. "Virtual assistance, productivity tooling, news research, and creativity are some areas where AI has been effectively applied," she explains.
She gives an example of their work with an energy company, where they are using AI to help customers access relevant information from millions of help articles. She also mentions the use of AI in productivity tools like Notion or Miro, where AI can summarise meeting notes or categorise boards.
However, Claire also highlights the concerns about copyright and originality in the creative field. "I love the idea of being able to get something to help me with the 'what do I do now?' But equally, I completely respect the conversations going on at the moment about copyright and like, when does it become non-original?"
While AI has its benefits, it's not without its challenges. Viktor points out that AI models are text generators that don't deal well with facts. "You have to constrain them quite a lot to do what you really want," he says.
Claire adds, "AI should augment your value proposition, not be your value proposition. Any product that has AI in giant letters as the forefront of the value proposition is missing the point."
When it comes to implementing AI in your digital product, Viktor advises starting with existing services like OpenAI's API and then moving towards building your own models. However, he emphasises the need for a unique dataset for training the AI.
Claire stresses the importance of releasing your product incrementally and getting feedback from a decent percentage of customers. "Make sure the feedback loops are robust and you're making adjustments and evolving the product based on the feedback," she advises.
Viktor, the Tech Director at Red Badger, helps clients with their technical strategy and the tech decisions they're making. He supports the teams in implementing these strategies and has been closely following the developments in AI and ML for the past 15 years.
Claire, the Product Director at Red Badger, works with teams and clients to ensure that the strategies they are working on are effective. With a background in design, she brings a product perspective to the table and is keen on understanding and teaching the mechanics of AI and ML.
At Red Badger, they are constantly exploring the potential of AI and ML in digital products, and this conversation is a testament to their commitment to staying at the forefront of these technologies. As AI continues to evolve, so too will the ways in which we implement it in our products, and Red Badger is poised to lead the way.
Our expertise extends to utilising generative AI in product development, green engineering for sustainability, and sustainable enterprise software development. We're also pioneering in enhancing customer interactions through AI for customer service, conversational AI, and conversation intelligence. Discover how cognitive computing and AI automation are integral to our digital solutions, and explore the role of AI in emerging tech.
For a deeper dive, read our articles on AI Innovation, Customer Experience, Customer Innovation, Responsible AI, Ethical AI, Challenges in AI, Sustainable Growth, Responsible AI for Sustainable Growth, Generative AI, Digital Products, AI for Customer Experience, Green Engineering, and Sustainable Enterprise Software Development.