How we’re using AI to accelerate product discovery

But my husband, who at the time had more experience than me in using AI, suggested I was asking the wrong question. He recommended that I reframe it as: “Are there any scientific articles that prove placing a chopped onion in a room will help with a cough?”

This time, the response was far more credible and useful (and, it turns out, onions do really help).

I learned an important lesson here: although the potential for AI is enormous, when most people engage with it for the first time, it’s usually in a superficial way, often leading to poor results. In order to gain maximum value from the tools, and improve outputs, it’s worth learning the best ways to provide context and specificity, while also asking for an answer based on relevant sources.

Putting AI to the test

At Damilah, we’re very excited about the transformative potential of AI. We have, therefore, been exploring how it can augment our business activities and help deliver greater value to our clients. For example, our engineering teams ran a series of hackathons to calculate whether using AI tools could accelerate software development and improve quality (in a nutshell: yes, it can – hugely).

At the same time, we wanted to test whether AI could do the equivalent for our product discovery and inception processes – and if so, in what ways. So we ran an additional hackathon to examine this. In it, we asked three teams to work on a fictional brief, using a variety of AI tools, including ChatGPT and Perplexity (using Claude).

As with our engineering colleagues, the results were astounding.

We discovered that AI could significantly reduce the amount of time we spent on discovery – by anywhere from 20% to 50%, depending on the task and the tools being used – and with results that matched the quality of the work done without the assistance of AI.

And, most importantly of all, it revealed areas where AI could free us from routine, repetitive work, allowing our people to focus on higher-value activities.[

Accelerating and improving workflows

Specifically, we identified two primary ways that AI can accelerate and improve workflows:

1. Jump-starting a project: For example, when preparing for a client interview, we can use AI to generate an initial list of questions based on the context we provide. These AI-generated prompts serve as a springboard, helping us refine ideas faster, and ensure we’ve covered everything.
2. Enhancing existing work: In other cases, we can input a draft of some work we’ve already created, prompting the tool to polish and improve it, as well as asking whether we may have missed something. This approach allows us to benefit from AI’s ability to enhance clarity and suggest useful amendments and additions.

Another of our most impactful findings from the hackathon was the way in which we could use AI to accelerate the creation of wireframes with Figma. This aided our conversations with developers while showing similar levels of quality outcomes compared to when we use the traditional discovery processes.

And a real game-changer has been using AI tools for writing acceptance criteria. Traditionally, creating detailed, actionable user stories (which list the requirements that a developer has to meet) is time-consuming and mentally draining. Now, however, by giving a well-crafted prompt to an AI tool, we can generate acceptance criteria in a matter of minutes. This not only saves time but also ensures consistency, freeing our teams to focus on other priorities (more on that later).

AI as an enabler

Despite these extremely encouraging results, we aren’t getting carried away with AI. While it accelerates and enhances many of our processes – and we’re already using it to speed up workflows in live environments with clients  who have agreed to us using AI tools – we also believe there should always be a human involved every step of the way.

For us, quality is paramount, so our product owners will always review and refine the AI outputs to ensure they meet our standards and our clients’ needs.

We’re also conscious that an over-reliance on AI may lead to diminished problem-solving skills – a phenomenon akin to forgetting basic arithmetic because we’re accustomed to using calculators. To counter this, we view AI as an enabler, not the be-all and end-all, so will always ensure our people develop and maintain those key analytical capabilities. Above all, AI’s purpose is to enhance human creativity and decision-making, not replace them.

Furthermore, we’re wary of the common problem of ‘garbage in, garbage out’. That is, as I found out with my first experience of AI, it’s essential to take the time to learn how to craft well thought-through prompts and to train the model. AI tools can only become that valuable enabler and accelerator if we ensure we have the skills and patience to do this.

Focusing on value-creation

By learning to use AI in the most effective ways to perform routine and time-consuming tasks, we’re enabling many of our people to spend more time focusing on high-value activities, such as deepening their market understanding, engaging more effectively with stakeholders and shaping product roadmaps.

And, perhaps most excitingly of all, it means we can experiment with bold ideas that we perhaps wouldn’t have risked testing previously as we’d be concerned about the time it would consume. Instead, it enables us to ‘fail fast’ in our search for innovative solutions that genuinely solve our clients’ problems and help them to meet their objectives.

In fact, if I was a potential client looking for a software development partner, I’d always choose a firm that has already successfully established AI tools into its processes. That’s because their teams will be unburdened by all the mundane, repetitive work, and able to truly focus on building a highly creative partnership that delivers outstanding results.

To discuss how our AI-accelerated workflows enable us to deliver greater value for your business, get in touch now.

Iskra Ristovska, Principal Product Owner at Damilah