The key to scaling AI from proof-of-concept to enterprise success in B2B is adopting systems thinking, which integrates the right user experience, value definition, and complementary non-AI features into the solution.
Introduction
In today’s tech landscape, Artificial Intelligence is often presented as the ultimate solution to every challenge. This can lead one to experience the ‘Law of the Instrument’ phenomenon. When AI is your powerful new hammer, you start seeing every problem as a nail. We rush to apply complex models and algorithms, believing they hold the key.
To be clear, there are many instances where AI is indeed the hammer, and the problem is a nail waiting to be struck. The potential for AI to deliver immense value is real. Yet, a curious pattern has emerged, particularly in the Business-to-Business (B2B) world. Despite promising Proofs of Concept (POC) or even AI capabilities, the sustained impact and adoption of these AI-native products remain disappointingly low.
Surveys from firms like McKinsey, IDC, and Gartner indicate that by 2025, while more than half of B2B organisations will have deployed at least one AI-native solution, only a handful will have successfully scaled them enterprise-wide.
Many initiatives never leave their departmental boundaries or advance past a small proof-of-concept. The difficulty of translating potential into sustained, widespread success is a complex problem that few organisations have solved.
The reasons are multidimensional, ranging from the high cost of scaling, lack of organisational appetite for new technology, misalignment with the larger roadmap of the organisation/teams, and historical baggage with AI, to name a few. One of the other critical factors is viewing the solution from the perspective of customers and end-users in a holistic manner, considering a day in their lives and where the problem really coexists among other forces, and not just AI.
This oversight results in a solution that lacks the right User Experience (UX) in the context of AI solutions, one that ties everything together to deliver cohesive value, not just an AI-native feature or product.
Piecing it Together: A Strategic Outlook on Complex Systems
For a holistic understanding, we need to move away from an “AI as a hammer” mindset and adopt a “jigsaw puzzle” mindset.
Systems thinking is defined as “…a way of exploring and developing effective action by looking at the whole rather than at separate parts.”
The success of an AI-powered solution can benefit highly from this outlook. Seeing AI as a hammer without understanding the adjacent pieces of the problem results in a short-sighted, technology-led solution that might not be people-centric.
Focusing on understanding the problem space as a whole, clearly identifying what is meaningful for users and stakeholders in their context by mapping all the other moving parts and challenges, before getting to AI, could transform the journey of AI adoption.
This approach can be seen as an onion diagram, with the layers below moving from the outermost layer to the core of AI solutions.
The moving parts of the problem
The definition of meaningful value
The user experience of AI solutions
1. The Moving Parts of the Problem
When presented with a puzzle, before assembling it, you must unbox it and lay out all the pieces to see if any patterns emerge.
The same is true for complex business problems faced by organisations. Acquiring the necessary understanding and simplifying complexity is not easy. However, this process helps demystify ambiguity and bring the required clarity for everyone on the journey going forward. It is an investment worth doing at the beginning.
There are tools available that are built on principles of systems thinking used in domains such as Service Design and Customer Experience, which can help break down complexity into simpler abstractions or data points: the same set of tools, different applications.
Laying out the bigger picture through the lens of these frameworks helps create the necessary contextual understanding. Especially in the B2B context, these journeys unravel immense domain, organisational, and customer or employee perspectives, which are key to understanding context prior to better decision-making.
By conducting activities like talking to stakeholders, observing employees and customers, doing user research, and mapping out the customer’s experience, you can get a complete, real-world understanding of business processes and the full journey of customers and employees.
These journey ‘visualisations’ can set up a foundation for everyone to understand the bigger picture, discuss and debate, and in the process simplify overall complexity. Through these artefacts, workshops, and presentations, the entire team can understand interdependencies across people, processes, and systems before moving to AI-driven solutions.
Throughout the course of such discovery sessions, the team can identify individual “puzzle” blocks and group them into large problem spaces or opportunity areas where interventions are needed across products, processes, communications, or technology.
The entire collaborative engagement draws out the bigger picture within the organisational context and, crucially, sheds light on all the missing blocks, the stepping stones of the journey, and identifies the right block where an AI-native solution fits best among its adjacencies.
2. Finding Meaningful Value for Everyone Involved
This brings us to the middle layer: defining meaningful value for everyone involved within the problem space and organisational context.
While problems exist within the organisational context, people are always at the core. People can include those in leadership roles, department heads, team leads, managers, employees, and end-customers.
Interestingly, each group has its own set of values, experiences with AI, definitions of success when a possible solution is implemented (AI or otherwise), and, most importantly, its perspective regarding the problem. To connect the dots across people, processes, systems, and domain complexity in the problem context, understanding these personas is crucial.
Focusing on one of these groups’ interests over the others can be catastrophic from an outcome perspective. These can result in a short-sighted view of the solution across the end-to-end value chain, a lack of traction in the long run, or worse, no adoption at all. There must always be a ‘balancing act’ between the interests of the organisation, stakeholders, and users, and the solution should be comprehensive.
One must carefully define the stepping stones (the roadmap of solution evolution) for months and quarters in a way that is best aligned with what each group aims to achieve through this problem-solving exercise. Each group needs to have clear visibility of forthcoming developments and alignment on why the roadmap appears as it does.
In addition to programme management, instilling confidence in everyone involved by demystifying what the solution could look like is an area where design can play a strategic role. Designers can leverage their skill sets to showcase vision concepts, future user journeys, and early-stage user flows to make everything more tangible.
It’s essential to clearly state the problem, define what success looks like (the “what”), and then figure out the practical steps to get there (the “how”). Being able to envision what the future journeys could look like, with sample screen wireframes, helps foster healthy conversation around scoping, prioritising, and refining the journey forward. Making the value tangible (through visual designs, wireframes, etc.) not only enables people to see what the outcome will be but also helps technologists and data scientists architect the right interplay of tech stacks, agentic vs. non-agentic AI, and complementary non-AI features.
The non-AI features are crucial. The right set must co-evolve alongside the AI models to ensure the AI solution fits perfectly as the missing piece of the larger puzzle.
Without this feature set, the user and the organisation might see the potential of AI, but will not gain the confidence to invest in growing the MVP further, as other questions remain unanswered.
3. The UX for AI: Design, Tune, and Govern
This brings us to the final layer: the core in the onion diagram, the desired user experience for an AI-native solution. This is where the designer’s role shifts, requiring deep thought about their expanded involvement across Design, Tune, and Govern parts of an AI native solution. Let’s dive into each.
Design: This involves creating a “sense of being in control” within the overall user experience. It means ensuring transparency in how the AI is making decisions (design for transparency), embedding enough feedback mechanisms throughout the journey (design for continuous improvement), and ensuring the human feels ‘in control’ of crucial decisions alongside AI (design for Human-in-the-Loop, HITL). Compared to traditional products, designing AI products requires UX designers to engage deeply with prompt definition and continuously iterate with domain experts to ensure each response is human-centric, humble, and maintains the right tone. The overall UX strategy should account for scenarios involving failures or surprises and handle them effectively to ensure there is no loss of trust in AI’s capabilities. Instead, it should foster a sense of empathy over time, accepting its hallucinations and mistakes.
Tune: AI is often perceived as a black box, with user expectations shaped by hyper-advanced experiences of models like ChatGPT and Gemini. A key part of the designer’s role is managing these expectations. The UX must thoughtfully handle and communicate non-functional requirements (such as response times) to build user confidence and patience, transforming potential frustration into trust. An experience in which the human feels the need to collaborate for better outcomes rather than fearing, “Will I get replaced?”
Govern: A designer’s responsibility extends well beyond launch; they become stewards of how the AI product behaves in the long run. Governing isn’t just about enforcing rules; it’s about cultivating trust. Designers must create interface mechanisms that make system decision-making transparent, surfacing confidence signals, clarifying boundaries of use, and disclosing the data sources behind responses. This empowers users to question, validate, and contextualise outputs rather than accept them blindly. By embedding transparency, accountability, and recourse into the design, they help ensure that the AI product evolves responsibly and remains aligned with organisational values.
Conclusion: AI’s Promise in B2B is Undeniable
But success isn’t about wielding AI as a hammer for every nail. It’s about solving the right puzzles with the right pieces, aligning problem spaces, workflows, and user needs into a coherent whole. Organisations that thrive with AI-native solutions will be those that treat AI not as a magic wand, but as one of the crucial parts of a carefully designed larger system of interconnected journeys.
