We have an amazing number of high-value Artificial Intelligence (AI) startups emerging in North America and Europe. In this blog post I talk about how these tech challengers can identify, engage and monetise early adopter customers, often well ahead of completeness of product or service. This also means enabling customer-funded R&D.
This blog post also applies to other tech startups - ranging from advanced materials, through cyber security to digital health. My work here applies to any business-to-business Value Proposition, where the high-value, high-touch sell still matters, since the buyside cannot know all and simply define a specification for an arm's length procurement exercise. Innovation means conversation.
Design Thinking, Applied
The starting point for AI startups is invariably a challenge of selling a solution that buyers do not yet understand, delivered by an organisation they have never heard of. In order to enable an AI startup to engage with the right early adopter customer, they cannot rely on formal procurement processes, but instead, must find another way to proactively engage with empowered buyers, from a cold start. The answer is: Design Thinking.
Inspired by the Hasso Plattner Institute of Design at Stanford University (d.school), Design Thinking simply means solving complex problems in a people-oriented way. As illustrated above, this is where I apply a method in five steps: Empathize; Define; Ideate; Prototype; and, Test. Then iterate, fast.
Early adopter customers are inherently highly-responsive to Design Thinking: invariably inquisitive and possess what psychologists call a "Need For Cognition (NFC)' - or in plain English, a person who loves solving problems! So, how can we discover, from afar, decision-makers who have this natural inclination towards problem-solving?
The answer to identifying would-be buyers with a high NFC is to embed a challenge inside the initial Value Proposition message. This means inviting the would-be early adopter customer's decision-maker(s) to engage in a 'what-if' conversation as the starting point for the initial Mutual Value Discovery Workshop. What follows could be an invitation to the decision-maker to engage in an instant exercise that rapidly proves value creation. Thereafter, each step of Design Thinking should progressively build-out the Return On Investment (ROI) arguments for a given Value Proposition.
Empathy is Key
In any initial conversation between AI startup and target customer, Empathize, as the first step of Design Thinking, is crucial to meaningful engagement with one or more empowered decision-makers and influencers. Here the AI startup must create a Value Proposition that clearly shows how every small step of engagement justifies a monetary commitment by the early adopter customer.
AI startups need to avoid expensive, long-winded buying and selling cycles, which eat up too much of precious Founders, Seed or Series A capital, It must be an engagement based on a 'pay as you go' model - not lengthy pre-sales cycles.
Large and mid-market enterprises are inherently conservative, risk-averse organisational cultures. This means that AI tech must demonstrate 'quick wins'. To help overcome this inherent barrier to the AI startup, building high-levels of receptivity and rapport between all buyside stakeholders is key.
This is where Empathy Mapping works well. This is a technique applied through a series of 'Mutual Value Discovery' Workshops, enabling high levels of receptivity and rapport. In turn, this leads to trust and, in a very pragmatic way, this will reveal greater clarity about the problem and motivation to learn about how the AI Value Proposition becomes the solution.
Given that the AI startup may be selling a highly conceptual (and often, incomplete) solution, it may be appropriate to combine 'Agile Design and Development' processes with this Empathy Mapping technique and Mutual Value Discovery Workshops. This may become customer-funded R&D.
During these Mutual Value Discovery Workshops, this first step of Empathize generates a deeper understanding and Statement of Work (SoW) for the next step of engagement, namely: Define. Empathize, as the first step of Design Thinking, can be monetised in its own right, as long as the early adopter customer can clearly see measurable value being created at this stage.
Defining The Problem
As the AI startup and early adopter customer moves onto the Define step, this continues the Mutual Value Discovery process, and where deeper receptivity, rapport and trust results in comprehensive insights captured, that truly and adequately describes the problem. This becomes the beginning of a 'mapping' to the solution delivered by the AI startup - and where relevant, validates and prioritises any customer-funded R&D efforts. Here the AI startup more accurately describes the solution - and where this is typically captured in a 'Statement of Work (SoW)' as 'Deliverables'.
Of course, an AI startup will have different 'Business and Pricing Models', including likely early stage 'Proof-of-Concepts' that map to the five steps of Empathize, Define, Ideate, Prototype, and, Test. A key point here is this: AI startups should hold back on hard-wiring its Business and Pricing Model, until the Mutual Value Discovery process generates one or more customer-validated ROI Models. The 'Deliverables' captured during the Empathize and Define steps provide a solid foundation to create, and progressively clarify a 'what-if' Business and Pricing Model - not only for an initial Proof-of-Concept - but also for any pre-agreed, ongoing 'Production Delivery'.
Enabling Rapid Ideation
Since AI startups are inherently breaking new ground in many different areas of business or citizen life, the third step of Design Thinking - Ideate - must encourage small steps to be taken, but faster. Again, this crucially depends upon good work done in the earlier steps of Empathize and Define. This must continue to demonstrate measurable value being gained along the way, with Ideate, Prototype and Test steps that follow Empathize and Define.
What matters at the Ideation step is not simply speed, but a step-up in the capture and a deeper validation of value being created. This is where 'ROI Modelling' is helpful: comparing 'Current State' ('As Is', without the AI tech) to 'Future State' ('To Be', with the AI tech added). Here the Business and Pricing Model for the AI startup is further validated in the real world - customer-by-customer, engagement-by-engagement. Eventually a pattern emerges, and then the Business and Pricing Model becomes progressively hardened and maybe, (but not always) openly published.
The ROI Model should have scoring related to an 'Economic Basis of Decision', but depending on the use case and environment for the AI startup. it may also allow for an 'Emotional Basis of Decision' scoring. This should also include 'Cost of Delay' and 'Cost of Doing Nothing', when comparing Current State and Future State value creation differences. This gives the would-be early adopter customer a comprehensive foundation for 'go/no go' decisions at each stage.
With the ROI Model captured, validated and continuously updated through Ideate, Prototype and Test steps, monetising each step of the Proof-of-Concept is more assured. Agreeing a solid case for the Business and Pricing Model moves the early adopter customer faster on the journey to a subsequent 'Production Delivery' outcome, complete with 'Customer Success' captured too.
Breaking the Hermetic Seal
Having worked with many startups across a diverse range of industries and technologies, I think that one challenge remains universal for the founders and executives, namely: how to engage with empowered decision-makers in the right large or mid-market enterprises, who are both willing and able to become paying early adopter customers.
The barrier here between AI startup and would-be early adopter customer may be described as the 'Hermetic Seal' that surrounds empowered executives who operate at the 'CXO-level' of a large or mid-market enterprise. So, breaking the Hermetic Seal is key. What does this require?
The key to executing the five steps of Design Thinking is building receptivity, rapport and trust between AI startup and early adopter customer. This may be described as three stages of my sales method called 'Demand Creation Selling': PiercePoint; ProofPoint; and, DecisionPoint.
Let's define each of these directional phases of the buying and selling cycle:
Creating a compelling message that resonates with an empowered, but hard-to-reach decision-maker. This is simply a focus on effective lead generation from a cold start: make the Value Proposition stand out in words and pictures.
Having aroused the interest of an empowered decision-maker with a PiercePoint message, the ProofPoint becomes the validation of claims made. This is where Mutual Value Discovery comes into play, as described above.
When engaging a buyer in Mutual Value Discovery Workshops, the AI startup achieves sufficient rapport, receptivity and trust to understand the path, people and politics towards a timely win with the early adopter customer.
Whilst the PiercePoint message is all about creating communications that resonates with hard-to-reach, empowered buyers, the ProofPoint validation of such as message must be built on a solid foundation of buyer-seller interactions and conversations. This is where a Mutual Value Discovery becomes key to reinforcing a compelling case for timely purchases: a solid ProofPoint validation underpinning success at the DecisionPoint for the AI startup's Value Proposition.
The founders and executives within AI startups need to understand that it is the people - not the product (or service) - that early adopter customers buy into. So, leading with a story about the founder(s) vision and core competencies is key to the initial Value Proposition messaging.
AI startups need early adopter customers who can be monetised at the earliest stages of a Proof-of-Concept. Often the AI startup will be asking early adopters to engage in what may be less than complete products or services. The AI startup should hold back on hard-wiring its Business and Pricing Model, until validation with one or more early adopter customers is achieved.
This always becomes a challenge of selling a solution that buyers do not yet understand, delivered by an organisation they have never heard of. So, what matters is empathy, rapport, receptivity and trust: where AI startups must remember that it's the people behind the technology - and again, be reminded that it is the people who matter most.
Design Thinking for AI is the method I use to achieve success for startups. Let's explore this further.
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