Marketing professionals’ expectations for supportive AI learning environments

For successful AI implementation, marketing professionals must learn new skills and competences. A recent interview study carried out at Haaga-Helia shows that they have already started to develop their AI skills and are actively experimenting with AI solutions as part of their everyday jobs. To execute implementation processes that lead to superior business outcomes, business managers are expected to provide cultural and leadership support for marketing experts’ AI learning and experiments. They can do this by offering tangible resources, appropriate training, and constant encouragement.

Developing learning environments and lifelong learning

In the rising era of AI, business leaders need to evaluate how technological transformation affects the organisation and especially their people (Durth, Hancock, Maor and Sukharevsky 2023). Along the same vein, Brassey, Christensen, and van Dam (2018) state that a knowledge-based and digital economy relies on skilled workforce as a more important asset than ever.

Based on these trends, organisations need to invest in both technology and human learning to implement AI successfully into their business and marketing practices. The possibilities of AI are extensive, as there are thousands of applications that can be used in marketing and sales (King 2022). It will be of crucial importance how well organizations can lead employees from AI experimentation to systematic usage expansion.

The current research is designed to clarify the state of AI learning and implementation in selected pharmaceutical and health product companies in Finland. The findings provide insights into how organisations can enhance AI learning through their leadership approach. In this article, we will focus especially on two research questions that were originally presented in a master thesis published at Haaga-Helia University of Applied Sciences in 2024.

1. How do marketing professionals currently develop their AI skills?
2. How does the learning environment (organisational culture and leadership) affect skills development?

The results and discussion of the original master thesis and the present article will be used as background research and needs analysis material for a recently launched research, development and innovation (RDI) project SHUTTLE – Sharing Future Learning Environments in Higher Education and Lifelong Learning that Haaga-Helia executes with three other European universities.

Marketing professionals’ learning experiences

The results presented here are based on an interview study with seven marketing experts. The informants worked at different companies in various marketing roles: digital marketing manager; digital marketing specialist; customer marketing manager; brand and category manager; brand manager (2); marketing manager.

According to the research findings, six out of seven informants use AI as a part of their work, and four of them reported that they have full access to internal AI tools provided by their organisation. All seven marketing professionals indicated interest in developing their AI skills further. Six informants said that they had attended AI trainings, and five mentioned that their employer had organised training that they had been invited to attend.

Informants mentioned several self-directed learning methods that they use for AI skills development, including following relevant social media channels, reading expert articles, using Google search, and using generative AI as a teacher and coach.

Our company has organised a couple of different trainings […] personally I have probably gained most of my prompting skills from social media and YouTube.

All in all, the findings indicated that these marketing experts had started to build their AI skills and that most of them had attended some form of AI training offered by their organization.

When marketing professionals listed obstacles to their AI learning, a lack of time to develop their skills during work hours was often mentioned. This presents a practical problem, as the possibilities and motivation to develop work-related skills during one’s free time most likely varies between individuals. Additional obstacles to AI skills development were a lack of guidance and guidelines, too generic trainings, and some people’s negative attitudes towards AI (e.g. resistance to change).

I am a bit confused about how little AI has been discussed, actually no one from our local team has raised the topic. 

I must say that the use of training resources relies heavily on an individual employee’s initiative and motivation, at least at our company.

The interviewees said that they had shared at least some experiences and information about AI-related learning with their colleagues. In general, they preferred sharing experiences with people who have an open mindset and share a similar knowledge level. All in all, the interview results suggested that employees who have both knowledge of AI and an open mindset were capable of sharing experiences, and they also tended to initiate discussions about AI independently in various contexts.

When asked what encouraged their AI learning, most informants highlighted their personal motivation to learn. They felt that they were capable of learning how to use AI tools. When asked to compare their own AI usage level to that of their department or team, informants felt that the level of AI knowledge and skills can vary between individuals, from non-users to early adopters.

Clearly some people are early adopters and have skills to do some fun stuff [with AI]. I have familiarized myself with the topic a bit, but I believe that most colleagues are on the same knowledge level as I am.

Nearly all informants felt that AI learning is beneficial for the company and for themselves personally. They suggested that by supporting AI learning companies can improve work commitment, carry out more tasks internally, and help people work faster. However, informants also expressed some concerns related to their personal capabilities to learn new things at a fast pace.

Marketing is transforming at enormous speed now […] therefore, I have a feeling that it is hard to keep up and that I should know more. I should have the skills.

If you do not keep up with the development, you are out. As AI is not going anywhere […] it is growing and probably increasingly integrating into all work.

Overall, the findings indicated that marketing professionals were motivated to learn and felt that sharing experiences with others had a positive effect on their learning. The informants also felt that their AI learning was not yet fully supported by their organizational culture or leadership practices.

Marketing professionals expect support for their learning process

The recent technological developments have resulted in a wide adoption of AI solutions (Kshetri 2023). The interview data showed that marketing professionals have growing expectations towards the organizational culture. Managers are expected to be able to create and share favorable learning environments for all employees in this era of rapid AI transformation.

Interviewees indicated that the topic of AI learning was to some extent relevant to their management. The positive evaluations were based on experiences that management had encouraged employees to use AI, the topic had been discussed, tools were provided, and guidelines for AI implementation had been set. However, informants expressed a growing need for appropriate organizational support and constant, human-centered, encouragement.

We are encouraged to learn, and words of encouragement also originate from top management. However, the only tool that we are provided at this moment is good luck…

It can be concluded that the experience of how well the organisational culture, leadership practices, and individual managers support AI learning varied between workplaces. The following citation describes a positive example of an AI learning environment, a shared learning platform.

The company provides a learning platform […] that everyone at the company can benefit from. There are several courses and learning modules available. […] there is also AI-related content, and individuals can use the training platform based on their own needs.

The marketing professionals interviewed indicated that at least one of the following would support their AI learning: encouragement and possibilities to test different AI tools; presentation of practical usage cases in marketing; localized trainings; and increased resources (especially time).

Best practices for developing digital mindsets and AI competences

AI is transforming organisations’ competence needs, and constant learning and development of new hard and soft skills is demanded from employees in the era of AI (McKinsey 2023). Venkatesan and Lecinski (2021) propose that organizations should manage their technological transformation by focusing on people, processes, and culture (among other business-related aspects such as profit).

Garvin, Edmondson and Gino (2008) state that management has the power to encourage discussion and offer resources, and these can improve employee learning. Similarly, Höyng and Lau (2023) point out that leadership practices that encourage AI usage can facilitate AI readiness and contribute positively to participants’ overall tech-readiness. The key element is to remove concerns and convince employees of the benefits of new and emerging technology. In the same vein, Jöhnk et al (2021) write that simply by providing information it is possible to increase employee’s acceptance for change. Our interview study findings are in line with these theoretical views, and informants expected management to initiate AI discussions and to provide positive examples of how AI technology can be used in practice.

Tehrani, Ray, Roy, Gruner and Appio (2024) identify eight dimensions of AI readiness: informational, infrastructural, environmental, participants, process, customers, data, and technology readiness. The research behind this article focused especially on environmental readiness and participants’ readiness. Tehrani et al. (2024) argue that in terms of the organisational environment, leadership practices and the organisational culture are the most important elements of AI readiness. The related elements that improve AI implementation are collaboration, willingness to learn, openness and tech-friendliness.

Tehrani et al. (2024) also point out that participants’ AI readiness requires increases in the behavioral and psychological readiness of employees, managers, and stakeholders to capture the added value provided by AI. Employees’ behavioral and psychological readiness can be evaluated with four different elements: acceptance, trust, knowledge, and skills. This means that if employees have the right skill set and they trust technology, their AI readiness – and that of the organisation – grows. If technology is not trusted and employees do not have technical or business understanding of AI, the adoption process is likely to be challenging.

As AI implementation is a relatively new phenomenon, many companies are struggling to increase their employees’ knowledge level and shape their attitudes. According to the interview data, marketing professionals are acutely aware of the need to develop their knowledge, skills, practical experience, and attitudes. The results suggest that the time is ripe for concrete organizational steps and robust leadership developments that support and accelerate employees’ AI learning journey on both behavioral and psychological levels.

Neeley and Leonardi (2022) conclude that in technological transformation, learning new technological skills is not enough. Employees must also be motivated to use their skills to develop new opportunities. In other words, they need to possess a digital mindset. Such a mindset is best developed by fostering behaviors and sets of attitudes that allow people to notice how AI usage in its many forms generates new opportunities. According to Neeley and Leonardi, the digital mindset is developed by increasing 1) the level of confidence that an employee has in their own ability to learn and 2) the extent to which an employee thinks that the transformation matters.

Leaders and managers with a digital mindset themselves can be especially competent to build a resilient and growth-oriented workforce. Managers could therefore critically assess their own and their team members’ digital mindsets with the goal of developing their personal and shared digital mindsets further. In a best-case scenario, people trust their own learning capabilities and are inspired by the change.

Companies that are implementing AI may also find that people with digital mindsets are easier to get involved in implementation processes. However, an important challenge for organisations and managers to tackle is to consider how to involve and motivate people with weaker digital mindsets in AI learning in ways that are mindful of their personal needs, attitudes, and expectations. According to Bankins et al. (2024), when people feel confident of their own skills, they are likely to become more open to new collaborations and experiments with AI-based solutions. Neeley and Leonardi (2022), on the other hand, argue that leaders should first promote the AI buy-in phase and only secondly the confidence of employees. According to them, people are likely to become more confident when they see the value in developing their digital competencies.

If the organization sets up learning environments that help employees to witness positive examples of using digital technologies in different beneficial ways (e.g. through training sessions, encouraging communication, sharing stories) they gain more confidence in their abilities to learn. This view is supported by Oreg, Vakola and Armenakis (2021) who argue that when managers succeed in creating a trusting and supportive organizational culture, employees are likely to grow more participatory and supportive during the transformation process.

Durth et al. (2023) state that generative AI can be presented as a powerful tool even for individuals who may at first perceive it negatively by highlighting how AI technology can augment skills development (e.g. in coding) or facilitate upskilling processes by helping individuals to learn new skills faster. The interview data contained many examples where marketing professionals expressed their need to witness and discuss positive practical examples and success stories of AI implementation.

Mikalef, Islam, Parida, Singh and Altwaijry (2023) remind us that leadership training can lead to several benefits in organisations. Competent leaders and managers who are aware of AI possibilities are also able to make judgements and decisions that support operations, competence development, and employee well-being, for example. This can then lead to improved allocation of time and budget.

As skills development and experimentation with new technologies take time, managers might find it challenging to strike a balance between value-generation and flexibility when AI solutions are experimented with and integrated into work practices. Nevertheless, it is worth bearing in mind that companies that invest in transformation and change, continuously and early on, will be in a good position to create motivating learning environments and new competencies that both support people’s learning and well-being and bring competitive advantage.

Lifelong learning in a rapidly changing environment

From the perspective of higher education and employee competence development, the pace of technological development seems rapid, and lifelong learning becomes more important than ever. It is expected that the learning path continues after studies. Therefore, it would be beneficial that universities and organisations deepen their co-operation to support employee skills development. Moreover, each of us can enhance experimentation and experience sharing and act as a role model for others who need support and motivation to learn.

The findings of this study contribute towards understanding the needs and expectations that business and marketing professionals have for learning environments that support the creation of digital mindsets and meaningful AI learning journeys. In addition to developing organisational support structures and leadership approaches, research could additionally be devoted to investigating ways in which learners themselves could lead themselves and their peers towards more courageous and innovative AI experimentation, knowledge-sharing, and collaboration across disciplines, organizational boundaries, and cultures.

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