Implementing Generative AI

Recap | Direct, Transformative, and Incremental

The Three Categories of Disruption

In our previous article we discussed the three categories of disruption: direct, transformative, and incremental. The direct category contains companies that face direct risk of business model obsolescence and is characterized by a need to pivot your entire business model. An example for Gen AI is Chegg, a homework support company. Direct change, although challenging, shows a clear path as it requires complete change and rethinking of your business model.

On the other end of the spectrum lies incremental disruption, where the core proposition of your company will not be affected. A Gen AI example is UPS – some efficiency gains are possible, but their core (delivery) proposition is unchanged. For incremental-only change, the way forward is also straightforward: you follow and implement new tools as they emerge.

However, the transformative category is where it becomes complex. As discussed in the previous article, the majority of companies (65%) fall into the transformative category when adopting new technologies. In this scenario, they perceive a threat to their core proposition from competitors and new entrants who leverage GenAI for more compelling propositions. Here, you will have to keep the current business running, while innovating simultaneously to make sure you won’t be left behind.

Changing the business

Our clients tend to have many ideas on how to use GenAI in their business. However, in most cases, use cases are scattered across the organization and fail to ‘leave the basement’: Some developers are using Github copilot, or some marketeers are writing blogs with ChatGPT, but there is no coordinated strategy. This is similar to Digital 15 years ago, where companies had similar longlists of ideas – “let’s give all our sales reps an iPad” or “let’s add QR codes to our products”. Implementing use cases based on such a longlist can work for incremental-only change, but it is impossible to fully transform your business this way.

The transformation trap

However, many companies do fall in the trap of organizing for transformation just like they would for incremental-only change, testing use cases scattered across existing business. However, this way you will fail to test at speed as you will be burdened by existing processes, ways-of-working, guardrails (e.g., compliance and security), and habits - if you put 100 employees in a room and tell them to change their approach slightly from tomorrow onwards, nothing will have changed in two weeks as people fall back to their old ways.

It’s also impossible to organize transformative change fully outside your existing business, like you could with disruptive change, as there will be no way to scale without a link to existing business.

Instead, our experience from digital projects underscores the importance of transforming a specific segment of your business entirely, a concept we refer to as a Business Sandbox. 

Implementing Transformative change

The Business Sandbox

A Business Sandbox is a space for freeform experimentation within the existing business (the sand), but with clear boundaries (the box).

A successful Sandbox is designed to transform a specific part of your business towards a new vision – where multiple use-cases are tested and iterated to work towards that vision in quick succession. Successfully setting up a Business Sandbox requires three main elements.

Scope

A limited-scope, end-to-end part of the business should be identified for the Business Sandbox. This could be a geographic location, a specific product, a business unit, or a customer journey. The Business Sandbox typically contains anywhere from 10 to 20 employees and 10s to a few 100 customers. Testing at this level can generate proof-of-value at low risk quickly.

Organization

To ensure maximum speed, a small, dedicated cross-functional team with a ringfenced budget should be set up, focused on maximizing learnings across all dimensions: proposition, organization & ways-of-working, technology & data, and change management. This team should both a) have a ringfenced budget so the core business cannot divert funds for optimization activities and b) be exempt from existing KPIs and bonus structures, so they do not have to worry about anything but the success of the pilot.

Technology

The goal is not a perfect back end: Avoid allocating resources to complex automation from day one. Instead, you want to prove or disprove value fast and iterate quickly in the real business – not spend 12 months building a technical masterpiece that adds no value to the business. Once the business value is substantiated, strategize on scaling up and implementing the use case across the entire business.

Advantages of a Sandbox

Starting a transformation on a small scale through a Business Sandbox has several advantages:

• Limited risk through small scale operations: By focusing on a smaller part of the business, the risk of negative impact on your overall operations is reduced. This approach ensures that any potential setbacks are contained and manageable – you can call 100 customers individually if needed.

• Accelerated testing with focused teams: Having a limited and dedicated group streamlines the testing process. This allows for more efficient decision-making and fast progression through test phases, as fewer stakeholders are involved in the approval and feedback loop.

• Strategic scaling of proven solutions: By adopting the principle of scaling only initiatives that have proven to be successful, your business minimizes negative financial impact. Furthermore, this selective scaling approach ensures effective resource allocation.

• Quick development of convincing proof points: Operating on a small scale allows for the rapid creation of proof points that are essential to convince senior stakeholders and the wider business.

Sandbox for GenAI vision

In the past 15 years, SparkOptimus has successfully implemented Business Sandbox setups for large international players like Heineken, Henkel, and many others. This same structure is also ideally suited to successfully deal with Generative AI transformation of your core business. This transformation will result in changes to core business functions across all departments. Not only will the expected capabilities for functions change, but the whole way-of-working needs to be reinvented.

For instance, marketing departments will undergo a significant shift. The original model consisting of a large number of internal marketeers will potentially move to a leaner structure with several internal marketeers working in cross-functional teams with some GenAI specialists. These new teams will pivot from manual content creation to automated content creation, enabling highly personalized marketing.

So, within marketing, you set up a cross-functional team with all involved roles (e.g., content, customer insights, marketing), and include IT/data capacity, to allow a small team to explore and implement Generative AI use cases, aligned with the department’s vision. The Business Sandbox functions as an innovation hub, providing a space to test, refine, and validate new use cases.

In this sandbox environment, the team not only explores GenAI use cases but a wider scope of e.g., operational process improvements and classic digital improvements. By adopting this approach, the sandbox transforms into an innovation environment, fostering a culture of continuous learning—a crucial element for staying ahead in the swiftly evolving business landscape.

In the next article of this series, we delve deeper into Generative AI and its transformative impact on the future of work. We'll look at which jobs might change or disappear, and which new roles will become important, explore the impact of Generative AI on the future of work, breaking down what it means for different jobs and the skills that will be in demand. Get ready for a closer look at the opportunities and challenges that come with these changes.

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Next up in this article series

We have learned a lot through helping our clients over the years, and we’ll be sharing our key insights with you in a number of publications – see below the list of topics we will cover:

• Introduction to Generative AI – This article is the first in our series on Generative AI and focuses on the what & how of Generative AI

• Generative AI use cases – Exploring what Generative AI applications are out there already, and which we expect to be possible in the (near) future

• Implementing Generative AI – What is required to successfully implement Generative AI: tech & data, processes, and organization

• Generative AI and the future of work – How Generative AI will impact our work, which jobs will change or disappear, and which new roles will be required

• The importance of data in Generative AI – Why high quality data is (even more) important for Generative AI, and how you can get your data ready for Generative AI

• Tooling & prompting – What tools are available to implement Generative AI, and how can you write efficient prompt to leverage tools like ChatGPT in your work

• Generative AI risks/pitfalls/ethical/diversity concerns – What can go wrong and how to make sure to avoid it

Stay tuned!

We hope you’re as excited as we are and please let us know if you have specific topics or questions you would like us to share with you.

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