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Artificial Intelligence (AI) continues to attract significant interest from businesses eager to integrate it into their operations. However, misconceptions about AI's capabilities-often inflated beyond what data quality and context allow- can hinder successful adoption. Clients may not fully grasp the value of AI tools, which leads to poor implementation choices and inaccurate outcomes. This research addresses the question: How can the added value of an AI tool be effectively communicated to clients? Rather than highlighting technological potential, the study shifts focus toward understanding client needs. A survey among companies, combined with academic insights, informed the development of a more nuanced sales approach. The findings reveal that sales strategies must be adapted depending on the client's level of AI maturity. For those with defined AI goals, engaging high-level decision-makers helps clarify challenges and tailor solutions. For clients without specific AI plans, the emphasis should be on uncovering key business problems through guided dialogue before introducing AI as a solution. The study underscores the importance of "selling the problem" rather than merely promoting the technology. Effective communication strategies include using real-world examples, visual demonstrations, narratives that illustrate transitions from current to desired states, and discussions of potential risks to support informed decisions. Notably, Proof of Concepts (POCs) are not seen as effective primary tools for showcasing value. In conclusion, aligning AI sales strategies with client perspectives enhances trust and clarity. By emphasizing problem-solving and leveraging social proof, companies can better communicate the true value of AI tools and increase the chances of successful adoption.
Abstract: Artificial Intelligence (AI) continues to attract significant interest from businesses eager to integrate it into their operations. However, misconceptions about AI's capabilities-often inflated beyond what data quality and context allow- can hinder successful adoption. Clients may not fully grasp the value of AI tools, which leads to poor implementation choices and inaccurate outcomes. This research addresses the question: How can the added value of an AI tool be effectively communicated to clients? Rather than highlighting technological potential, the study shifts focus toward understanding client needs. A survey among companies, combined with academic insights, informed the development of a more nuanced sales approach. The findings reveal that sales strategies must be adapted depending on the client's level of AI maturity. For those with defined AI goals, engaging high-level decision-makers helps clarify challenges and tailor solutions. For clients without specific AI plans, the emphasis should be on uncovering key business problems through guided dialogue before introducing AI as a solution. The study underscores the importance of "selling the problem" rather than merely promoting the technology. Effective communication strategies include using real-world examples, visual demonstrations, narratives that illustrate transitions from current to desired states, and discussions of potential risks to support informed decisions. Notably, Proof of Concepts (POCs) are not seen as effective primary tools for showcasing value. In conclusion, aligning AI sales strategies with client perspectives enhances trust and clarity. By emphasizing problem-solving and leveraging social proof, companies can better communicate the true value of AI tools and increase the chances of successful adoption.
Keywords: AI Adoption, Sales Strategy, Client Communication, Value Proposition
1. Introduction
Artificial Intelligence (AI) is gaining increasing attention, and organisations are eager to integrate this technology into their operations. However, the power of AI is often overestimated because its effectiveness depends on the quality of the data on which it is trained.
Within this context, a dynamic develops between AI providers and their (potential) clients. While providers create advanced tools, they often find that convincing clients is complex. In their view, clients frequently lack a sufficient understanding of certain aspects of AI, leading to misunderstandings about what a specific tool can and cannot solve.
Selling an AI tool can be particularly challenging, especially when it comes to clearly demonstrating its added value from the outset. When potential clients fail to grasp this value, they may resort to an alternative AI tool that is ill-suited to the problem they are trying to solve. This increases the risk of receiving inaccurate outputs, which can lead to significant consequences.
This research is conducted to better understand how to approach and comprehend clients, enabling AI providers to tailor their sales processes accordingly.
That clients struggle to understand what is required for the implementation of AI is evident from a study conducted by the Boston Consulting Group and MIT Sloan Management Review (Stackpole, 2022). The research indicates that of the surveyed leaders considering AI adoption, only around 16% are recognised as Responsible AI (RAI) leaders. These RAI leaders achieve superior results because they view AI as an organisational challenge and prioritise developing comprehensive RAI programs. The study identifies that a significant reason many leaders struggle is their tendency to see AI primarily as a technical challenge, rather than addressing it as a (strategic) organisational issue. RAI leaders report feeling better prepared to manage AI-related risks and engage top management effectively, which are crucial factors for successful implementation.
The BCG study also suggests that many leaders may not fully understand the extent of AI's added value. This is likely because they often view an AI solution as a straightforward technical implementation, whereas in reality, AI can have a much broader, strategic impact on the organisation. This study aims to provide better insights into ways to enhance the interaction between AI providers and customers to improve the value AI can deliver to organisations.
1.1 Urgency and Relevance
For clients looking to leverage the benefits of AI, this research may provide a significant contribution. An optimised sales process from AI tool providers can help mitigate the associated risks. In light of the current "AI hype," this research is also relevant (Expert, 2024). All this leads to the central research question:
"How can the added value of an AI tool be effectively communicated to clients?"
To address this question and offer a well-supported conclusion, the following structure is used in this document:
* Methodology: This section outlines the approach used to answer the research question.
* Findings and Discussion: This section presents the research findings and compares them with insights from existing literature.
* Conclusion and Recommendations: This section draws conclusions based on the findings and provides practical recommendations for businesses.
2. Methodology
To clearly analyse the problem and generate actionable insights, this research was conducted using a combination of qualitative and quantitative research methods. The approach consists of two phases: developing a perspective (view of reality) and conducting a customer analysis to further investigate the pain points. Below, the approach to each phase is explained.
2.1 Development of the Perspective
The perspective was developed by combining various sources of information. Desk research and a literature review involved the use of scientific articles, theories from business administration and marketing, and other reliable resources to gain a comprehensive understanding of the topic. Practical insights were gathered by consulting the internal database of an AI company called Consono, a data management firm that offers a unique platform called Dynizer. Additionally, interviews with professionals in the field were conducted to gather insights into their views on AI and client approach.
From these sources, it becomes clear that the following factors must be considered when selling AI:
* Level of prior knowledge: How much does a client company know about AI?
* Level of commitment: Is the company truly dedicated to working with AI, or is it a necessity based on following the AI hype?
* Context and stakeholders: Which roles within the company are involved in implementing an AI project?
* Organisational development stage: For understanding the change process the client organisation goes through, we analysed a which stage of change the client is, using the Adaptive Cycle of
Resilience approach (Abcouwer et al., 2020; Takács & Abcouwer, 2020)
* How an AI technology provider sells an AI tool.
2.2 Customer Research: How and Why?
To better understand the four factors from this perspective, a customer analysis was conducted. Questions were formulated in line with the perspective. A combination of surveys and interviews was chosen for this analysis.
The survey was developed in collaboration with our research partners, Consono and Oracle (a leading technology company specialising in database management, cloud solutions, and enterprise software). The initial interview guide was reviewed by professionals from both organisations and subsequently pre-tested with two individuals from the target audience (healthcare and public sector). These pretests involved conducting full interviews to assess whether the questions effectively captured the intended information and to identify any missing elements. Based on the feedback, several questions were refined. The second interview confirmed the adequacy of the revised guide and was included in the final analysis.
Drawing from the validated interview guide, the survey was designed with mostly multiple-choice questions to maximise response rates and allow for comparability. The surveys were then distributed through business events, email campaigns, and LinkedIn posts, targeting organisations across various sectors. It was not essential which specific employee within a company completed the survey, as responses from different roles offered diverse and valuable perspectives.
To support and validate the survey results, semi-structured interviews were also conducted with a small group of potential clients during a live event. These interviews provided deeper insights into client needs and perceptions around AI adoption and served both to triangulate the survey findings and enrich them with qualitative data. The interview protocol allowed for structured questioning with the flexibility to explore specific themes in greater depth, depending on the respondent's role and context.
The survey garnered a total of 21 responses. This suggest that the results cannot be considered a formal market study. Distributing our questionnaires to contacts and clients of Consono and Oracle may have introduced biases within our research population. Acknowledging these limitations, the findings were compared with insights from scientific literature, upon which conclusions are subsequently based.
The survey was designed so that, depending on the respondent's answer to a certain question, different followup questions could appear. As a result, some questions were not answered by all 21 respondents.
2.3 Scope
This issue focuses on optimising the sales process for AI providers, targeting industries where data exchange takes place and large volumes of data are processed. This segment has been selected because the problem predominantly arises within these industries.
Most respondents come from the following sectors:
* IT/Technology
* Healthcare
* Government
* Consultancy
* Education
3. Findings and Discussion
In this section of the paper, we will address the factors that emerged from the perspective described above. In each paragraph, the survey questions related to the respective factor will be discussed.
The analysis will answer the following questions:
* Why was this question asked of the respondent
* What are the results?
* What conclusions can we draw from this?
* Does this align with scientific research?
Some survey questions will not yield relevant connections and will therefore not be discussed in this chapter.
This all leads to the following analysis:
3.1 Level of Prior Knowledge
3.1.1 Organisational or Technical Issue
As previously mentioned, a study by Boston Consulting Group and MIT Sloan Management Review (Stackpole, 2022) reveals that among the surveyed leaders looking to adopt AI, approximately 16% are identified as Responsible AI (RAI) leaders. According to the BCG researchers, a key reason for the challenges many leaders face is their tendency to view AI as a technical issue, whereas, in reality, it are the organisational factors that must be addressed at a strategic level.
To explore this, the following question was posed to all managers/leaders:
Do you consider your plans for AI more of an organisational or a technical issue?
* Organisational refers to the adaptation of acceptance, support, and the renewal of work processes within the company.
* Technical refers to the adaptation of systems or tools required for implementation and operation.
3.1.2 Looking at These Results, What Stands out?
The choice that an AI project is considered equally organisational and technical is clearly the outlier. Following that, the choice that respondents view an AI project more as an organisational issue comes next. These results show that 84.2% (57.9% + 26.3%) of respondents see an AI project (in part) as an organisational issue. This does not align with the research from Boston Consulting Group and MIT Sloan Management Review (Stackpole, 2022).
Although no general conclusion can be drawn in comparison to these results, this does not mean that the findings are irrelevant. On the contrary, it can be concluded that it is valuable to identify whether a client views an AI project as a technical or organisational issue. This is not always obvious, as clients may have different views on the nature of the issue.
3.2 Level of Commitment
3.2.1 Pilot/Experiment/Proof-of-concepts
Before companies want to implement a new innovative idea, it is common to conduct a pilot/experiment/proofof-concept (P°C). To investigate the outcomes and the level of success of this, the following question was posed to respondents who indicated they had conducted an experiment or pilot: What were the results of the experiments or pilots you conducted in the field of AI? (open-ended question)
The follow-up question to this was: How successful was this experiment or pilot? 1 being not successful, 10 being highly successful.
3.2.2 Interpreting These Results
What stands out is that the average success rating is 7.5, and the results from the frequency table indicate that AI experiments mainly provide value and learning experiences (see the top table). However, a limited group still faces obstacles with AI. From this, it appears that most AI experiments are successful among these participants.
Looking at scientific research, this conclusion is not surprising. According to scientific articles, proof-of-concepts (P°Cs), pilot projects, and experiments conducted within the AI project world are often considered successful. (Kutz, Neuhüttler, Spilski, & Lachmann, 2022)
However, it is important to note that a successful P°C, pilot, or experiment does not necessarily indicate a successful AI implementation project.
AI technologies that are actually implemented in practice are rare. Most AI projects fail. Estimates suggest that the failure rate is around 80%-almost double that of IT projects within companies a decade ago. (Bojinov, 2023) (de Bellefonds & Charanya, 2024)
3.3 Stage of Development
The development stage of a company is crucial for understanding the change processes organisations undergo. We used the theory of The Adaptive Cycle of Resilience (Abcouwer et al., 2020; Takács & Abcouwer, 2020) to illuminate these processes. Since organisational change is not the core topic of our research, this theory fulfilled our requirements, and we did not further explore the available change management theories (Lauer, 2021) (Smith, Jones & Brown, 2005) (Hiatt & Creasey, 2003). The model identifies the different phases of resilience that a company can experience. However, it is difficult to determine from the survey which phase the respondent's company is in. Given these limitations, the following can be examined:
To investigate whether companies want to work with AI and are therefore resilient, the following question was posed: Does your company have plans to do something with AI?
3.3.1 Interpreting These Results
More than half of the companies are already using AI, while 30.4% plan to implement it in the future. This indicates that a significant number of organisations are either already applying AI or considering its use, highlighting the growing relevance of this technology.
This also aligns with scientific research, which shows that more than 80% of companies worldwide are already using or considering implementing AI. (University, 2024) (Chauhan, 2024).
3.4 Context and Stakeholders
The context of certain roles. Not everyone can explain AI in the same way. This paragraph will explore whether there are correlations between certain stakeholders and how they view AI or become involved with AI.
3.4.1 Opportunities and Threats per Context
To investigate the correlation between the perception of opportunities and threats, and the role, the following question was asked: Could you mention the opportunities and threats of adopting AI as you see them? (openended question).
Looking at these results, what stands out?
Since not all roles provided multiple responses, only the CEO, IT Manager, and Project Manager are analysed.
* CEO: CEOs approach AI from a strategic level, seeing opportunities in improving efficiency and innovation. At the same time, they remain cautious about risks such as loss of control and ethical issues. Their focus is therefore on value creation and risk management.
* IT Manager: IT managers see AI as an opportunity for efficiency and quality improvement. At the same time, they perceive threats related to cybersecurity risks, the need for human control, and low accessibility for employees with less digital skills.
* Project Manager: Project managers primarily view AI as a tool for improving efficiency, gaining new insights from large amounts of data, and delivering services faster with fewer errors. However, they are concerned about ethical issues, the risk of inaccurate data leading to incorrect conclusions, and the impact on employment. This reasoning can be linked to what is needed to maintain a project.
As far as our research shows, this approach has not yet been explored in full detail
3.5 Who do the Respondents Consider the Best Contact Person?
To investigate which role customers believe is most suitable for assessing the needs and opportunities related to AI within their organisations, the following question was presented to the respondents: "Whose opinion within your company do you find the most and least important when an AI project is being carried out? Please rank the roles in order of importance (with the most important at the top)."
The answers to this question resulted in the following outcome: How often each role was ranked in positions 1- 2-3:
How often a role was ranked in position 1:
Table 6 indicates the position where the role was most frequently placed.
3.5.1 Interpreting These Results
According to these respondents, the CEO and CTO are the most important roles within the company for an AI project. This is evident both from the number of times they ranked first and the chart. There is no direct scientific source that addresses this specific topic for comparison. However, there is a source on how to identify the decision-maker within a company best. This focuses primarily on the role and function within the company, the company's needs, and the specific challenges that need to be solved. (Shekhar, 2024) (Redlawsk & Lau, 2009) (Payne, Bettman & Luce, 1998)
3.6 How a Provider of AI technology Sells an AI Tool
3.6.1 Explanation of Customer Needs
Different sales techniques can be chosen to explain the added value of AI for its sale.
To investigate what customers truly need for the explanation of AI, the following question was posed to the respondents: "How can an AI provider best explain the added value of their solution to you? (multiple answers possible)."
The answers to this question resulted in the following outcome:
3.6.2 Interpreting These Results
The results show that customers primarily seek an explanation that emphasises the practical side of an AI project. This includes:
* Most notably: through concrete examples and success stories from similar companie
* By providing insight into potential risks and how they are managed.
* With a visual demonstration or trial version of the AI solution.
* By explaining specific benefits, such as cost savings or efficiency improvements.
What stands out is that detailed explanations about the technical functioning and implementation of the AI solution were barely selected. Only two respondents indicated a need for this technical explanation, both of whom were an IT manager and a CEO/director.
Interestingly, three IT managers and six CEOs/directors filled out the survey, but only one of these two groups chose to request technical details. Based on this limited group of respondents, this suggests that a technical background or high leadership role does not necessarily mean one desires technical details. Therefore, no direct conclusion can be drawn that roles with a technical background or leadership position automatically require an in-depth explanation of how an AI tool functions.
A scientific study by Gartner (no date) confirms the finding about what kind of explanation is desired in such projects. It shows that it is most effective to explain what these technological solutions can mean for the client. Preferably, by providing social proof in the form of customer success stories and real-world examples. Additionally, it's crucial to outline a realistic approach that leads the buyer from their current situation to the desired future state (Gartner, no date). In short: sell the solution, not the technology. Preferably to those who view AI as strategically important. Provide evidence that the solution works and sketch a realistic implementation path.
4. Conclusion and Recommendations
Based on the findings, the following conclusions and advice can be compiled for companies offering AI tools.
4.1 Type of Customers
An AI tool often impacts multiple processes within a company. Research and interviews with professionals reveal two types of customers:
* Customers with concrete AI plans: These customers have already crafted clear plans for implementing AI and approach the AI provider with specific goals in mind.
* Customers without concrete AI plans: These customers do not have established plans for AI or are uncertain about its use, necessitating the AI provider to proactively engage with them to explore potential opportunities.
4.1.1 Customers With Concrete AI Plans
For the first type of customer, the CEO, CTO, or data manager are often the appropriate points of contact. These customers already have plans in place, so the AI provider only needs to focus on explaining how the AI tool can assist in realising those plans. The challenges the customer is facing are already clear.
4.1.2 Solution Selling
For this type of customer, it is recommended to identify the challenge the company wants to solve and demonstrate how the AI tool can address it using solution selling. Avoid providing a detailed explanation of the technical side of AI and instead focus on:
* Examples and success stories
* Moving from the current situation to the desired outcome
* Visual demonstrations
* Providing insights into potential risks
It is advised against conducting Proof of Concepts (POCs) to clarify the functioning of the AI tool. Research shows that around 80% of AI projects fail after a POC. Furthermore, a POC extends the sales cycle, leading to inefficiencies and a higher risk of implementation failure.
It's important to note that the examples, risks, demonstrations, or scenarios may vary per company or industry.
4.1.3 Customers Without Concrete AI Plans
For the second type of customer, identifying the appropriate contact person is more difficult. Ideally, urgency should be fostered among the operational staff and managers within the company so that they can ultimately persuade the CEO or the board of directors. Therefore, it is crucial that there is no sales-oriented communication with the CEO or board in the early stages of the process. The chances of AI project failures increase when unrealistic or unfeasible projects are proposed. For these customers, it is essential to first raise awareness of the issues within multiple layers of their organisation.
This process begins by answering the "why" question: why does this problem exist, what problems are there, and why is it important to solve them? Only then should it become clear how the AI tool can solve this problem and what the specific solution entails.
It is crucial that the AI tool is not presented as a solution until the "why" question has been answered. The customer must first fully understand what the problem is, and then the AI tool should be presented as the solution.
This method is inspired by Simon Sinek's Golden Circle model (Sinek, 2009) (Straker & Nusem, 2019)
4.2 How to Approach of Customers
4.2.1 The Questionnaire
When the customer has not or limited AI plans available we should help them to explore the opportunities for AI application. To help customers identify the problem (and thus answer the "why" question), a questionnaire has been created. This questionnaire helps the salesperson clarify where there are opportunities for the AI tool/application:
* Which processes take the most time?
* How much workforce is being spent on these processes?
* Does data play a significant role in these processes?
The salesperson does not need to ask these questions literally, but it is recommended to follow this approach during the conversation.
4.2.2 Solution Selling - Dealing With the Outcomes
Once the organisational problem that needs to be addressed is clear, solution selling comes into practice.
Based on these questions, solution selling will provide:
* Examples and success stories
* From the current to the desired situation
* Visual demonstration
* Insights into potential risks
And avoid Proof of Concepts (POCs), as we have seen before.
4.3 Flowchart
The theory does not always align perfectly with practice. Therefore, to ensure that this information is in line with real-world practices, it is recommended to answer and record the following questions in the CRM system for each sale, whether successful or unsuccessful:
* In which industry is this company active?
* Who is the right contact person within the company?
* Was the solution-selling methodology used in the sales process?
* Have certain success stories or examples proven to be more effective than usual? If so, which ones?
This will continuously improve this document and potentially uncover connections between specific industries and the best ways to approach them for a sales opportunity. To capture this approach, a flowchart has been created to visualise the research outcomes.
4.3.1 Why Does the Flowchart Have two Endpoints?
Because the process leads to different outcomes depending on whether the customer eventually shows interest in the AI tool or not. If the customer is not interested, a different route is followed than if they are.
Once the customer understands the added value of the AI tool, it is crucial to make them aware of the nature of the issue they are facing. This primarily involves an organisational issue. Research shows that customers often perceive AI as a technical problem, while its implementation heavily depends on organisational adjustments and collaboration within the company.
5. Ethical Declaration
For this research, information has been collected that in some cases may be traceable to specific individuals. However, this data has been processed anonymously and has been used solely for research purposes. All respondents were informed in advance about the purpose of the study and how their data would be used. They gave their consent for participation.
No personal data has been shared with third parties, and all data has been handled with care and in accordance with applicable privacy and ethical guidelines.
AI Declaration
This research makes use of ChatGPT, an AI tool from OpenAI, to support the writing process by improving translation, enhancing readability, rephrasing content, and shortening sections of text. The content has been entirely determined by the author, with ChatGPT serving as a supportive tool. All generated output has been carefully reviewed and adjusted where necessary.
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