How to identify the right use case for autonomous transformation
Amir Ouki
Managing Director, Product
As an AI-native company, we’ve witnessed firsthand how organizations are navigating the complexities of getting started with transformational AI in a value-driven way.
Many of our clients are in the earlier stages of AI adoption, working with us to not only identify new areas of competitive advantage, but understand how different types of pilots are working (or not working) in their verticals at-large.
This is why we launched our Transformation Officer series—a platform designed to help leaders strategically leverage AI to operate differently and better, not just faster or cheaper.
Transformation officers are struggling with AI. Why?
Caught between abundance and uncertainty
A recurring challenge we’re seeing is that transformation officers face difficulties defining and prioritizing AI pilots that deliver real value. This issue is a common challenge across leadership teams. With the surge of AI vendors—from AI-native SaaS products to many incumbent platforms now integrating AI into existing features—decision-makers are overwhelmed by the abundance of options.
The sheer number of AI solutions available today can be paralyzing. Leaders hesitate to commit to anything beyond initial experiments, unsure of how to evaluate different vendors and solutions.
This often results in either analysis paralysis or a vicious cycle of endless testing without a clear path to long-term value, leaving leaders feeling like they’re falling behind while their competitors charge ahead.
Amir Ouki, Managing Director Product - BOI (Board of Innovation)
Proving AI’s value at an ecosystem level is hard
Another key challenge is scaling and proving the value of AI beyond isolated business units. Many organizations are running experiments that tend to remain siloed, failing to deliver measurable value at the operating model level.
Lack of framework
Without a clear framework for identifying high-value AI use cases, leadership teams struggle to identify a progression path with confidence, thus resorting to short-term wins rather than initiatives that drive lasting impact.
To move beyond this, leaders need a strategic framework that helps them pinpoint opportunities with the potential for long-term transformation, not just quick productivity gains.
The changing role of the Transformation Officer
The role of data, emerging technology, and innovation has rapidly evolved over the past few years, and with that, so has the role of the transformation officer.
In this autonomous age, change is happening at a much faster pace, driven by technologies like open-source LLMs, which are now far more accessible than before. As a result, transformation officers need to shift their focus from how to integrate AI to where it will create the most meaningful change.
It’s no longer just about implementing AI—it’s about determining where it can deliver the most strategic value.
This shift is critical for ensuring AI initiatives align with the company’s broader transformation goals.
AI-driven autonomy: the future of business
In the coming years, we expect companies to operate a lot more like software companies, with departments like marketing, finance, and innovation operating autonomously and leaders of the future managing AI-driven products, not processes.
While some leaders may not have a traditional – or any – technical background in software, they will take the lead on defining how this software operates, what problems it will solve, and the internal expertise that it will leverage to solve them. This decentralized, AI-driven approach will empower teams to take ownership of solutions that significantly enhance their workflows.
The goal of autonomous transformation isn’t to automate away jobs and replace humans.
It’s about using AI to transform key workflows so that teams can operate more independently and focus on adding new value.
Amir Ouki, Managing Director Product - BOI (Board of Innovation)
Mapping AI’s role in transformation
To harness exponential AI value, Transformation Officers need to set their strategy for previously impossible growth, not just productivity gains.
This means using AI not just to do what they were already doing in a way that is faster or cheaper, but instead to fundamentally evolve the way that their companies work, the goods and services they produce, and how they produce them.
Download this AI Strategy Matrix to help you imagine and map out where to play in the AI-powered, autonomous age.
AI efficiencies: driving incremental gains. In the lower-left quadrant of the matrix, we find AI efficiencies—where businesses use AI to improve their existing products, services, and operating models. This is where companies focus on delivering incremental gains, such as faster delivery times or lower costs, by enhancing productivity with AI.
AI Products and AI Systems: Moving beyond incremental improvements, businesses are largely exploring two paths: either transform their operating model while maintaining their existing products and services, or maintain the same operating model and use AI to transform their products and services, making them AI-native.
AI breakthroughs: comprehensive transformation. The upper-right quadrant is where AI breakthroughs occur—when both the operating model and the product or service are transformed by AI. This is the highest level of AI-driven change, and while few companies operate at this level today, it holds the most long-term potential. Achieving this kind of transformation requires significant investment and time, but the impact could be revolutionary.
The real value is in exploring how AI can help the organization achieve what was previously impossible. However, this process requires a clear alignment between AI initiatives and the broader transformation strategy.
Dive deeper into setting your strategy for (im)possible innovations with this webinar.
So how do Transformation Officers find use cases with AI at the org-level?
It comes down to aligning AI initiatives with transformation strategy.
Driving AI transformation requires a clear strategy. While small wins from business unit experiments are helpful, they’re rarely enough on their own. To scale AI and drive value across the organization, active involvement from transformation officers is critical.
Before diving into specific use cases, it’s important to distinguish between a productivity experiment and a transformation use case:
Knowing the difference between Productivity Experiments vs. Transformation Opportunities
Productivity experiments are smaller, task-specific initiatives designed to deliver quick wins. These might include automating routine tasks like data entry or customer service chatbots. The outcomes are typically straightforward to achieve and measured in terms of efficiency.
In contrast, a transformation use case is broader and more ambitious. It impacts multiple business units or even the entire organization. These use cases are focused on long-term, strategic goals; for example, creating predictive and preventative healthcare products from historical medical data. The goal here is to drive fundamental change, not just incremental improvements.
Both are valuable, but serve fundamentally different roles. Productivity experiments are usually driven by business units, where teams have intimate knowledge of their workflows. Meanwhile, transformation use cases require leadership from the transformation office, ensuring that AI’s impact is optimized at the organizational level.
Can productivity experiments lead to transformation?
In some cases, but certainly not all.
Often, you can start with a productivity experiment, which allows you to build AI capabilities and achieve quick wins. These experiments can serve as a way to validate AI technologies and test different approaches on a smaller scale. They act as an initial testing ground, helping you gauge your company’s AI maturity and potential. In some cases, these productivity experiments can even scale into transformation use cases.
Where we’ve seen this work well is when these experiments generate valuable insights that can feed back into the company’s data ecosystem, influencing other use cases, or when they result in significant changes within a business unit that ripple out to impact other areas of the organization.
Identifying transformation opportunities: a framework
The framework below will help transformation officers identify high-value, compelling use cases and then validate them. This is quite different from identifying a productivity use case, which is more focused on process improvements and automating repetitive tasks. A transformation opportunity, however, is centered on making the previously impossible possible.
This framework identifies the middle ground between your existing transformation strategy and different AI initiatives at the business unit level.
Areas driving high-value transformation use cases
There are three key areas that the majority of high-value transformation use cases tend to come from.
1. Strategic goal drivers: What are the company’s highest-priority strategic goals? How can AI solve our existing problems? Here you’re starting from the company’s highest-priority strategic goals and working backwards, leveraging AI as an enabler to achieve those goals. Instead of chasing AI as the end goal, the focus is on how AI can solve existing problems rather than finding new ones to fit the technology.
2. Customer value: How could AI bring direct value to end consumers? While much AI experimentation has been centered around creating value for the company or its employees, customer-centric AI transformation has lagged behind. A relentless focus on adding customer value is key to creating truly impactful AI-driven transformation use cases.
3. Achieving the impossible: How could AI help us do what we couldn’t do before?
Identifying how AI can help us do really what we couldn’t do, what we couldn’t do before.
Transformation use case framework
Use these three key areas, where Transformation Officers are uniquely positioned to drive value, as a starting point for the transformation use case framework.
This framework will help you identify actionable, measurable transformation use cases that deliver value both at the function and operating model levels.
Running these assessments with clients is typically a four-week sprint.
Highest priorities: identifying goals is generally straightforward, but prioritizing them is where clients often need help. We focus on the top goals related to long-term strategy, customer value, and innovation. We recommend prioritizing at least one goal in each area, ideally more. This helps us narrow down potential use cases in the following steps.
Obstacles for each goal & how AI could help solve them: identify the obstacles preventing you from achieving these goals. These aren’t necessarily barriers to AI adoption, but broader business blockers. For example, a carbon neutrality goal may be too resource-intensive, or a market expansion goal may be hindered by tough competition. Identifying these blockers helps us explore how AI could address them.
Feasibility and viability assessment: This critical step evaluates data availability, system integration, scalability, cost-benefit, compliance, ethics, and technical expertise. It’s key to determining which use cases are worth pursuing and which aren’t feasible given your organization’s maturity.
Transformation at ecosystem level: assessing whether the use case contributes to transformation at the ecosystem level. Does it generate outputs that could drive other use cases or open up new opportunities? Transformation use cases with high contagion potential are prioritized over those with limited impact.
After all assessments, you will have a strong foundation to drive AI strategy.
Outcomes for this type of exercise
- A transformation framework to originate and identify go/no-go for potential transformation use cases.
- Validation for why, how, and when to pursue transformation initiatives.
- Prioritization of use cases.
- A filter for decision-making for enterprise AI solutions.
- A focus on solving existing problems, instead of finding new ones.
A structured approach to transformation
By using a structured framework, organizations can effectively originate, assess, and prioritize AI use cases that align with their transformation goals.
This approach helps identify the most impactful opportunities, providing a clear roadmap for AI-driven transformation. Moving beyond isolated productivity experiments, companies can achieve sustainable, long-term value and position themselves at the forefront of AI innovation.
Want to discover how your business can capture the opportunity of AI? Let’s talk!
