AI Design Sprint Hackathon

A blueprint for business and user-centric AI transformation

AI will change everything and will transform the future. We've all heard such claims.

On the other hand, AI seems to be overhyped, a fix-it-all solution for every problem. Bad customer experience, UX issues? AI will magically fix it. Sitting on a ton of data? Invest in AI, and you'll make sense of it. Marketing ineffective? Use AI to write your emails. Struggling startup? Add AI, and your valuation will skyrocket.

Hype of not, one thing is certain:  AI is capable to generate boardroom excitement about its potential. This is why every CEO, business executive, and decision-maker is contemplating AI’s implications for their business and wondering what steps to take next and where to start. FOMO - Fear of Missing Out - is definitely a thing here.

However, cutting through the noise to start an effective AI transformation is challenging.

How do you avoid superficial, surface-level implementations and instead focus on use cases that offer truly incredible potential for the business?

It’s becoming obvious that simply slapping AI onto your products or services without strategic thought is ineffective. I understand that businesses try to get something out the door quickly to look like they are ahead of the curve and appear innovative, but in most cases, these initiatives do nothing except get headlines or some engagement on social media.

A different approach is needed to drive meaningful, deep change—like completely rethinking current systems and navigating blue oceans. It may not be as flashy and attention-grabbing as AI-generated cat videos or DALL-E images; in fact, it may be about rather dull projects that will eventually compound into something transformative.

In this article, I will start sharing one such approach—there can obviously be more paths to AI adoption—that we are piloting with one of our customers, a large organization in a very traditional industry, with 15,000 employees and boasting revenues of $20 billion per year.

While designing the approach, we had a few constraints to work with:

• Duration not too long, so as not to lose momentum: Start to finish in less than three months.
• Minimum investment for maximum impact: Low six-figure cost, show proof of concept.
• Avoid surface-level implementations: Blue ocean, disruptive thinking.
• Not a back of napkin sketch: Tangible AI prototypes.
• Not just a workshop: Provide a path to implementation & scale.

With these constraints, the approach almost revealed itself.

How can you quickly develop and test new value propositions? Answer: design sprints.

What’s the best format to build multiple solutions under high pressure? Answer: hackathons.

And the last ingredient: high-quality AI guidance and expertise.

The result:

AI Design Sprint Hackathon - blending speed with rigor, emerging technology with customer centricity, and involving the organization at every level, top-down to bottom-up—from C-Suite to front-line workers and domain experts.

We have also set some stretch goals for our hackathon, as all proper goals should be:

1. Educate the C-Suite on AI, and secure their buy-in and sponsorship for future AI implementations.
2. Find relevant business opportunities for AI.
3. Develop and test three working AI prototypes for selected challenges.

In what follows next, I will share the step-by-step process. At the time of writing we have just completed the first step (see below) so the story is still unfolding, and this article will probably be edited a few times in the future.

Step 1. Educate the boardroom → AI Opportunity Mapping Workshop

They are already paying attention to AI, but in most cases, they know very little to make informed decisions. So the first step was to boost their AI knowledge. In many ways, this workshop was like attending a language school, but instead of learning a new language, we had a seasoned and experienced boardroom discovering the endless possibilities that AI offers.

Through real-life AI case studies, they could see that AI is more about augmenting people rather than removing or replacing them. Or about amazing new possibilities through automation. And, ultimately, it’s about innovation by imagining completely new business models.

The seasoned executives, with a lifetime of experience, who have seen it all in business were able to connect dots quickly. The newly gained perspective helped the C-Suite identify areas of opportunity within their products and services, core capabilities, and front and back office operations where AI could be used to augment, automate, or innovate. From everyday AI to game-changing AI, everything was on the table.

More importantly, they pledged support for some of the most promising opportunities. This was an important first step because without leadership buy-in, it would be nearly impossible to take an initiative like AI off the ground.

Format: Full-day workshop

Facilitators: John Vetan, Innovation strategist and Gianluca Mauro, AI expert & instructor

Participants: C-Suite, Senior & Executive Vice-Presidents of business units

Deliverables:  Opportunity statements for AI exploration

Status: Completed, June 2024

→ Request an AI Opportunity Mapping Workshop for your leadership team.

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Step 2. Understanding of underlying problems → Problem Framing Workshop

We are in a great spot now. Leadership is excited about AI. There is a strategic direction.

But vision without execution is worthless.

To move the needle, we need to get middle management on board. They run the day-to-day operations, are close to the problems, and it's their job to solve them. If they do not believe in AI’s potential, they can be the permafrost preserving the status quo and stifling innovation.

During this phase, we will work hand in hand with the innovation team to identify relevant stakeholders and experts who have a deep understanding of the underlying problems to be solved.

Let’s not forget that AI is “just a tool,” so it’s crucial to maintain focus and start from real problems.

This is where the Problem Framing Workshop shines. We will use it to engage these stakeholders, unpack their knowledge and expertise into clear problem statements worth solving. Stakes are high, so we’ll prioritize problems that really matter for the business.

Format: Full-day Problem Framing workshop

Facilitators: Decision-making expert

Participants: Middle managers, decision-making stakeholders

Deliverables:  Well-defined problem statements

Status:  In preparation, August 2024

→ Request a Problem Framing Workshop for your team

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Step 3. Rally the troops → The Bottom-Up Approach

To ensure the success of AI adoption, it's essential to involve the entire organization, from top to bottom. In the first two stages, we secured leadership support. Now it’s time to turn our focus to employees at all levels, especially those on the front lines who will be directly interacting with AI solutions.

We’ll host a town-hall meeting to raise awareness, build excitement about AI, and share the strategic direction along with the specific problems identified in the previous step that the organization aims to tackle with AI solutions during the upcoming hackathon.

The goal is to motivate employees to sign up for the hackathon based on their affinity for the challenges. This will ensure that the teams are not only excited to participate but also personally motivated and equipped with the expertise to solve the problems during the hackathon — ultimately increasing the quality of the solutions.

Format: One-hour town-hall meeting

Presenters: Internal innovation team, AI expert, DSA facilitator

Participants: Open to all employees

Deliverables:  Hackathon teams recruited

Status:  In preparation, August 2024

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Step 4. Build AI capabilities → Foundational AI training

No army goes to war empty-handed. It needs training, it needs weapons.

Similarly, we will not ask employees to go into an AI Hackathon without having the slightest idea about AI. That’s why we’ll engage selected teams in foundational AI training where they will learn how to use generative AI and build AI prototypes — skills that will be invaluable during the hackathon.

Format: Four-hour virtual training

Trainer: AI expert

Participants: Hackathon teams

Deliverables:  AI prototyping skills

Status:  In preparation, August 2024

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Step 5. Importance of Experimentation → AI Design Sprints

The stage is set!

We’ll tackle problems that matter. Teams of experts are assembled with the right tools in their hands and leaders supporting from behind.

It’s crunch time!

Guided by seasoned design sprint facilitators and AI experts, each team will tackle a challenge. During three intense days, following an AI-powered design sprint process, they will ideate, prototype, and test their AI solutions with real customers.

The main goal here is learning: understanding how AI works, how teams embrace it, what solutions might look like, and ultimately how customers will react to them.

Not every solution will work. But (good) failure is the price to pay for innovation.

Some will succeed.

Either way, it’s OK.

The learnings will be invaluable to inform the next steps in continuing the AI transformation journey.

Format: 3-day AI Design Sprint

Facilitators: DSA Design Sprint facilitators

Participants: 3 internal teams, each supplemented with 1 AI expert

Deliverables: Three working AI prototypes

Status: In preparation, September 2024

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