Data and AI product expert Tim Daines appears in our event series Delivered to discuss the opportunities and challenges in AI adoption.
The year 2023 will go down in history as the time when AI went mainstream. From ChatGPT to DALL-E, LLMs and other AI-powered solutions have become a part of everyday life for millions of people. With businesses jumping on the bandwagon, the market quickly got flooded with new AI tools.
However, regardless of this widespread interest and adoption, our research uncovered a concerning trend: most companies are underprepared for AI integration and lack the necessary capabilities to unlock its full potential.
Appearing as a guest on our show Delivered, data and AI product expert Tim Daines discussed some of the fears and opportunities around AI, sharing practical strategies for overcoming the challenges in AI adoption.
Is AI our Skynet or our savior?
In popular culture, there’s a recurring theme where advanced AI systems spiral out of control, leading to catastrophic outcomes like a war between humans and machines. While Tim doesn’t find these types of doomsday scenarios very likely, he acknowledges the growing anxiety around AI.
“I’ve had some clients saying they want to completely replace their workforce with generative AI in order to optimize costs. This is a reality, and I think it’s accelerating,” Tim warns.
However, while gen AI entirely replacing human labor can be concerning, it also has the potential to evolve our way of working. By automating boring and repetitive tasks, it frees up humans to focus on more complex problems. Still, when businesses explore the possibilities offered by generative AI, they must carefully consider the broader societal and ethical implications of these technologies.
A gap between eagerness and readiness
When we look how enthusiastic companies are about AI and how ready they actually are to integrate it into their operations, we’ll find a yawning gap. In fact, our survey of 1000 digital leaders found that 78% of them plan to invest in AI this year, yet 73% of them feel unprepared to do so.
Tim identifies a common pitfall in digital transformation, what he calls the ‘shiny toy syndrome’ – companies rush into AI initiatives without thinking about how they fit into their overall business strategy.
To bridge this gap, companies first need to assess their business goals, shareholder expectations, and understand their customers’ needs. The question they should be asking is: Will implementing AI genuinely benefit my customers, or am I just following a trend set by my competitors?
“Start with identifying the problem that you are trying to solve. Take it back to your business strategy, back to the human problem. Because, sometimes, AI may not be the answer,” Tim concludes.
Data plays a crucial role
Critical to the successful adoption of AI is the availability of high-quality data and the knowledge to effectively utilize it. Before jumping on the AI bandwagon, businesses must carefully consider what data they lack today that will be essential for their future success. As Tim emphasizes, it’s not just about having a minimum viable product, it’s about having minimum viable data.
Another significant roadblock companies face is a report-driven culture. Despite the hype around being “data-driven”, many organizations struggle to put this into practice. A truly data-driven culture means that employees understand how to use data to achieve their daily goals and can assess the quality and reliability of the data they work with.
“Companies claim, ‘we’ve digitized, everyone’s on screens, and they’ve got reports.’ That’s not digitization. People need to understand what they want to do with the data behind digital products,” Tim explains.
Furthermore, companies must prioritize secure and ethical data governance while recognizing that data management is not solely an IT problem but a shared responsibility across the organization.
All this requires a shift in mindset and the processes within an organization. This may not happen naturally, as it’s common for people to resist change, just like they did in the past when moving from typewriters to computers. Company-wide education can help overcome this resistance and alleviate anxiety around data and AI.
Kanban approach, agile mindset
Contrary to popular belief, building data and AI products using sprints is not possible because they’re simply too experimental and complex in nature. Instead, Tim advocates for the Kanban approach paired with an agile mindset.
When it comes to developing data and AI products, the timeline can vary widely, from weeks to months, depending on identifying, cleaning, and validating the right data. This differs from the traditional product lifecycle model, where products typically follow a linear path of growth, saturation, and decline. Unlike physical products, data and AI products have the potential to continuously enhance themselves with ongoing improvements in the underlying data.
Building a museum of customer insights
To better prepare for integrating AI, organizations should prioritize building a culture of knowledge sharing and innovation. This means giving employees opportunities to swap ideas, insights, and best practices, whether it’s over lunch or during dedicated knowledge sessions. But most importantly, it’s about recognizing and treating customer data as a valuable resource.
“We have museums and libraries because we want to learn about behavior and history. You can apply that to your business by learning about your customers and sharing those insights with your employees, treat it like a museum of history. That is the best way to communicate confidence, remove anxieties, start debates, start questions, start getting people talking to each other. And I think we may have somewhat lost that in corporations,” Tim concludes.