1 Introduction
What factors constitute organizational AI readiness to guide the AI adoption process?
2 Theoretical Background
2.1 Innovation and Technology Adoption
2.2 Organizational Readiness for Change
2.3 AI Specifics for Adoption and Readiness
3 Method
3.1 Data Collection
3.2 Data Analysis and Identification of AI Readiness Factors
4 Organizational AI Readiness Factors
Factor | AI characteristics | Organizational necessity | |
---|---|---|---|
Strategic alignment | AI-business potentials | AI functions are highly versatile and broadly applicable | AI-business potentials ensure that AI adoption is beneficial and suitable for the organization |
Customer AI readiness | AI use requires an understanding of the complexity and lack of transparency of learning algorithms | Customer AI readiness enables internal or external customers to appropriately use AI-integrated offerings | |
Top management support | AI’s inherent complexity poses change not only within but across organizational levels which requires top management commitment | Top management support signals AI’s strategic relevance to the organization and fosters AI initiatives | |
AI-process fit | AI-based systems are more precise if processes are structured and provide standardized data input | AI-process fit through standardization, reengineering, and implementation of new processes facilitates AI adoption | |
Data-driven decision-making | AI-based systems are fundamentally data-driven and require openness to incorporate such insights | Data-driven decision-making fosters AI adoption because both utilize data and statistical methods to gain insights | |
Resources | Financial budget | AI-based systems require high investments to tailor assets and capabilities to the unique context and data | Strategic allocation of the financial budget for AI adoption supports the overcoming of initial obstacles and uncertainty |
Personnel | AI adoption requires a broader spectrum of different roles and know-how for core business use | AI specialists and business analysts with AI know-how facilitate AI adoption | |
IT infrastructure | Deploying AI poses high workloads and data storage requirements | IT infrastructure enables AI-related activities and AI integration | |
Knowledge | AI awareness | AI's underlying concepts, e.g., machine learning or the autonomy of data-based decision support, are hard to grasp. | AI awareness ensures that employees have adequate understanding and expectations toward AI |
Upskilling | AI-based systems in core business require every employee to have a basic understanding of AI | Upskilling enables employees to learn and develop AI or AI-related skills | |
AI ethics | AI-based systems are at risk for biased learning and unethical outcomes | AI ethics comprise measures to prevent bias, safety violations, or discrimination in AI outcomes | |
Culture | Innovativeness | Employees' fear of AI-induced job loss threatens proactive innovativeness | Innovativeness increases employees' willingness to change the status quo through the application of AI |
Collaborative work | AI deployment relies on integrating different perspectives, i.e. domain, data, and IT | Collaborative work enables employees to work in teams and combine different skills | |
Change management | Employees' lack of understanding and fear of AI threaten the acceptance of AI-based systems | Change management helps employees to understand and cope with AI-induced organizational change | |
Data | Data availability | AI-based systems learn through different data types and large data amounts | Data availability within the organization fuels AI solutions |
Data quality | AI-based systems achieve better results the higher the quality of the data they learn with | Data quality ensures accurate AI outcomes | |
Data accessibility | AI personnel require access to relevant data sources for deployment | Data accessibility facilitates AI experts to easily prototype and develop AI solutions | |
Data flow | Initial and continuous training of AI-based systems requires smooth and automated data flow | Data flow between its source and its use ensures high data accessibility to AI experts |
4.1 Strategic Alignment
"In professional life, I would say that there is actually no area in the value chain in which AI cannot be used to fundamentally change something. […]. Just think of […] your business cases. Where are things that are not good so far, or are too expensive, or rather need optimization under economic aspects?" (E10)
"That is why it ultimately means that the end-user must be prepared, i.e. ideally be taken along, must have a clear expectation or vision of what the (AI) system will be able to do in the first minimal viable product variant. […]. Let's put it this way: the more it can do and the better it can do what is promised, the higher the acceptance." (E08)
"The use of AI can only work if it [AI adoption] is driven from above and [must] therefore start with the board members." (E10)
"Next, of course, are processes. If I don't have proper processes […] and if my process allows many individual steps, where a person decides, 'go left, go right…?' So, if a certain degree of standardization is missing, then it is also a very strong hurdle [for AI]." (E24)
"I coach board members who want to turn their division or […] their company into a data-driven company. And for me, data-driven means above all […] creating value with data. And, of course, AI is a form of value creation." (E10)
4.2 Resources
"Because it's just so often that I can't say ‘Hey, we're going to start with the topic of AI and make X million budget available and play around a bit and see what comes out of it’, that's just not how it works." (E09)
"So, having people in the organization that are interpreters of the technology, that understand what the business needs [are] and can translate that into technology needs, and vice versa, […] that whole cycle […] has to be created." (E03)
"Communication technology has come far enough to transport even large amounts of data quickly and we also have hardware that can hold and process this data. […] And these are the drivers that make it technically possible for us to go beyond what was [previously] possible." (E15)
4.3 Knowledge
"It [creating awareness] simply has to be a continuous process of change, because ideally this awareness is then known throughout the company. People know what you can do with it, what you can't do. And AI should then essentially be a tool […] to create a solution." (E09)
"From the company's point of view, it will be necessary to make upfront investments and train people, train employees, and further qualify them." (E23)
"You have to be aware […] that the data also contains a kind of prejudice or bias. […]. And if you just stubbornly apply algorithms to any amount of data, then […] decisions will be made but they may not be entirely correct." (E15)
4.4 Culture
"There are simply more innovative employees who are more open-minded [and] find it easier to try out something new. […]. Who simply playfully face it for once without reservations. Early adopters […], if you like." (E12)
"On the one hand, I'm driving the technology forward, as IT. […]. On the other hand, I naturally need the business departments, i.e. the users of this technology." (E12)
"The challenge then often lies in the dialogue with the employees. Taking them by the hand and reacting to their individual situation. […] This is a change management task that requires sensitivity." (E24)
4.5 Data
"First of all, many people always underestimate the amount of learning data required." (E12)
"Even if I have built the biggest or the best machine learning model, if bad data comes in, the result will be bad too. […] ‘Shit in, shit out’ is the basic principle behind it." (E08)
"There is preparedness […] in the sense of they have to have ready access to data. That data has to be manageable and manipulatable by these AI systems." (E03)
"So, on the development side, I am responsible for data pipeline, […] providing the right data, in the right format, in the right population, in the right quality to our machine learning engineers." (E13)
4.6 Emerging Insights Beyond the AI Readiness Factors
"There are organizations like Apple and Amazon that are deeply involved with AI, its creation and utilization in a very complex way and getting very significant results from it. So if organizations understood it better and had more resources to explore it they would be capable of generating some fairly significant returns from the investment into AI but most organizations don't really know what’s its use, how to get started, what to expect from use cases etc. So, it's still very much an exploratory process for many companies." (E03)
"It would be good if you had something like a guideline, a checklist, or something where you can determine (a) what degree of maturity the companies have today to be AI-ready and (b) what potential is there specifically in companies for the use of AI. And I think that, from my experience, this is completely dependent on the industry, target customer, product, or whatever." (E19)
"If I don't have anything to do with AI in my core business yet, what do I have to do? First, I must find small use cases where I can demonstrate the advantages of the technology. Then I must think about how I can make this visible in the organization. Then I must think about how I can build up the competencies. Then I must think about how I can spread this throughout the organization so that it ultimately leads to a continuous change process. These are relatively easy things to write down on paper, but in real life, it looks a bit different." (E09)
"There is my directive that we […] learn how to use AI. Not at the interface of customers and intermediaries but in internal processes. I would not like to have our learning curve at the expense of our customers or intermediaries if the AI is not yet working well." (E12)
"I think people have to experience some kind of epiphany […]. And I believe that these aha experiences […] is what ultimately makes it so that the acceptance then increases. That means also, in the whole chicken-and-egg principle, is what I develop actually what is accepted?" (E07)