From AI opportunities to an actionable roadmap: Vendre develops its AI strategy with Finnish AI Region
Finnish B2B sales company Vendre wanted to understand where AI could create real business value. Together with Finnish AI Region (FAIR) and experts from Haaga-Helia University of Applied Sciences, the company turned scattered ideas into a prioritised AI strategy and a phased roadmap. The roadmap is now guiding the planning of its new CRM system.
Dmitry Kudryavtsev | 20.10.2026 | Photo by Vendre
Vendre, formerly Bookers Group Oy, had several ideas for using AI but lacked a clear direction. The timing made the question pressing. The company was about to renew its customer relationship management (CRM) system, and the choices made there would shape what AI could do for its business.
Rather than jumping straight to a tool or a pilot, Vendre decided to build a strategy first. It worked with Finnish AI Region (FAIR), a European Digital Innovation Hub (EDIH), and an expert team from Haaga-Helia University of Applied Sciences. The work began with an AI maturity assessment and diagnostic advisory, which identified where AI could add value and what foundations it would need. Two workshops and an expert analysis then ranked the opportunities by expected impact and implementation effort.
The result was a prioritised AI strategy and a phased implementation roadmap. Because the work was completed before the CRM requirements were fixed, the roadmap could be aligned with the CRM renewal from the start. AI is therefore being planned as part of a broader development programme rather than as a separate initiative.
According to Juha Tikkanen, IT Director at Vendre, the collaboration provided exactly the kind of direction and structured approach the company was looking for. The next step is to evaluate a potential proof of concept once the CRM project’s requirements have been defined.
Building a shared direction for AI
Vendre is a Finnish company specialising in outsourced B2B sales, appointment setting and customer acquisition. In the maturity assessment and diagnostic advisory, carried out in April 2026, FAIR’s experts and Vendre reviewed the company’s current use of AI, its development objectives and the areas where AI could support its business.
Rather than moving immediately towards a particular technology or proof of concept, the teams concluded that the company would benefit first from a coherent AI strategy. This became the central objective of the next stage of the collaboration.
The timing was significant. Vendre was preparing to renew its CRM, and the strategy work offered a way to align future AI capabilities, processes, data requirements and platform decisions within that broader development programme. Defining the AI direction before the CRM requirements were fixed meant that the two could be planned together rather than in sequence.
A process-driven and multidisciplinary approach
The strategy work was carried out for FAIR by an expert team from Haaga-Helia University of Applied Sciences. It combined preparation, two face-to-face workshops and internal expert analysis. Before the first workshop, Vendre provided information about its main business processes. This allowed AI opportunities to be examined in relation to the company’s actual operations rather than at a general level.
Haaga-Helia assembled a multidisciplinary team combining expertise in AI, business development and sales. Since the main process under examination was sales, the sales expert’s knowledge of best practices and current trends was particularly important, helping the team relate AI opportunities to both the realities and the future development of sales work. The team also agreed on an overall methodology for examining where AI could support or transform the selected processes.
This preparation kept the work focused on business needs rather than individual technologies. Instead of beginning with the question “Which AI tool should the company use?”, the teams asked where AI could create value, who would benefit and what would be required to move forward responsibly.
Mapping opportunities and assessing their expected impact
The first workshop, held on 12 May 2026, brought together Vendre’s knowledge of its customers, services and operations with Haaga-Helia’s expertise in AI, business development and sales.
The workshop began with Vendre’s AI vision, objectives and expectations, and with the relationship between the company’s services, customers and business processes. Against this background, the participants mapped potential AI applications to the selected processes and identified additional opportunities where relevant. Drawing on current sales practices and trends, they considered not only how AI might improve existing activities but also how sales practices and customer expectations are developing. The AI opportunities were drawn from the company’s own brainstorming, existing best practices and suggestions generated with an AI assistant.
The participants then prioritised the opportunities according to their expected business impact, creating a shared view of which appeared most relevant and valuable. The workshop concluded with an initial look at requirements, prerequisites and next steps. It deliberately did not settle the final implementation order: its purpose was to establish business priorities that could then be examined from technical and organisational perspectives.
From workshop findings to a roadmap
After the workshop, the Haaga-Helia team assessed the implementation effort associated with each opportunity. They looked at how technically feasible each opportunity was, how the opportunities depended on one another and how complex they would be to implement. They also considered the organisation’s readiness, the integration requirements involved and what responsible and sustainable use would require in each case.
This two-stage prioritisation was central to the methodology. The workshop established the expected business impact of the opportunities, and the expert analysis assessed the effort and readiness required. Combining the two made it possible to compare potential value with the work involved and to determine a realistic order for future development, without yet designing detailed applications.
The results were brought together in an AI strategy report and roadmap. The report set out the objectives for applying AI and the opportunities identified, and showed how each opportunity connected to the company’s business objectives and could contribute to its sales processes. It prioritised the opportunities based on both expected impact and implementation considerations, outlined possible implementation approaches and brought these together in a phased AI implementation roadmap.
During the second workshop, FAIR’s experts presented the roadmap to Vendre and discussed its implications, priorities and possible next steps with the company.
The roadmap proposes progressing in stages. The first phase focuses on establishing the necessary foundations. The second concerns the development of an AI-driven insight layer. And the third considers more advanced AI support for sales operations.
The roadmap remained at a strategic level. Detailed company-specific priorities, data, technical information and implementation plans are not disclosed in this publication.
Results and value for Vendre
The most important result of the advisory is a structured basis for decision-making. Instead of a disconnected collection of ideas, Vendre now has a prioritised view of its AI opportunities, their dependencies and a possible sequence for implementation. The company can evaluate future AI investments against agreed business priorities, compare opportunities by expected impact and implementation effort, and understand which organisational and technical prerequisites need to be in place before implementation begins.
The strategy work identified several kinds of business opportunities, including making better use of customer interaction data, supporting sales management with richer insights, enhancing client reporting and increasing the value generated from existing sales processes. By connecting these opportunities with Vendre’s objectives and business processes, the roadmap gives future AI initiatives a clear link to business value.
Combining Vendre’s knowledge of its operations, customers and objectives with Haaga-Helia’s expertise in AI, business development and sales helped ensure that the roadmap was neither purely technical nor based only on general business ambitions.
As this was a strategy advisory rather than a technical implementation project, it did not produce a deployed AI application or measured productivity gains. Its value lies in helping Vendre make informed investment and development decisions: knowing which opportunities to pursue first, what they depend on and what must be in place before implementation. This reduces uncertainty before selecting a proof of concept and helps ensure that future AI spending is directed where it can create the most value.
Customer perspective: the right direction and structure
According to Juha Tikkanen, the FAIR project has been highly successful from Vendre’s perspective. The company achieved the objectives it set out to accomplish and gained valuable insights, a clear understanding of AI opportunities and a practical roadmap to support its future development efforts.
The findings are already feeding into the planning of the company’s new CRM project. “The AI-related opportunities and priorities identified during the strategy phase are being taken into consideration as part of our planning and development activities,” Tikkanen explained.
This allows AI requirements to be considered together with the company’s future processes, information needs and system environment, with the roadmap serving as an input into its wider digital development.
This integrated approach is consistent with research showing that successful AI-powered CRM depends not only on the technology itself, but also on effective data management, system integration and organisational alignment (Alnofeli et al., 2025).
Lessons learned
Several lessons from the collaboration may be useful for other SMEs exploring AI.
Dos
- Start with business objectives and processes. This keeps AI development connected to recognisable business needs rather than individual technologies.
- Use multidisciplinary expertise. Combining AI, business development and domain expertise, including current practices and trends in the field, makes it possible to examine opportunities in their real operational context.
- Assess business impact and implementation effort separately. First identify which opportunities could matter most, then examine how demanding and feasible they are to implement.
- Prioritise and build a phased roadmap. Some opportunities depend on foundations that should be in place before more advanced initiatives begin, and a phased plan helps focus limited resources.
- Connect AI planning with wider digital development. The link between the roadmap and Vendre’s CRM project shows the value of coordinated planning.
- Do not begin with a particular tool simply because it is available.
- Do not attempt to implement every AI idea simultaneously.
- Do not assume that a high-impact opportunity is automatically ready or easy to implement.
- Do not treat existing information or systems as automatically ready for AI use.
- Do not launch a proof of concept without defining its users, purpose, expected value and success criteria.
Wider relevance
The Vendre case shows that valuable AI support does not always begin with building a prototype. For an SME, creating a shared understanding of priorities, prerequisites and development stages can be an important result in itself. A strategy-first approach helps a company avoid choosing a technology before it understands the problem to be solved, and can reveal connections between AI opportunities and wider initiatives such as the renewal of core business systems.
The case reflects a wider challenge in moving from AI experimentation to strategically focused investment. In the Nordics, technical preparedness is relatively strong, while strategic preparedness has declined, leaving many organisations uncertain about where to invest and how to prioritise AI initiatives (Deloitte, 2026). In Finland, more than half of organisations remain in the assessment and preparation phase, while AI adoption is still often fragmented and experimental rather than systematically linked to value creation and strategic choices (Demos Helsinki, 2026). These findings reinforce the need for structured approaches to identify, assess and prioritise AI opportunities according to business objectives.
The approach used in the Vendre case is consistent with existing frameworks for AI opportunity identification and prioritisation. Brakemeier et al. (2020) provide a structured method for AI use-case identification and prioritisation; the National AI Centre (2026) links opportunity identification to mapped business processes, pain points, AI fit and business impact; and Futurice’s AI Business Value Toolkit progresses from strategic domain selection through value-stream mapping to use-case prioritisation (Virkkunen et al., 2025). Despite differences in terminology, these approaches follow a similar pattern: business objectives → process mapping → AI opportunity identification and mapping → assessment → prioritisation.
Prioritisation should be followed by implementation planning, since high-value AI opportunities may depend on data, technology, skills, governance or organisational capabilities that are not yet in place. IBM Txture’s 5A approach, the AWS Cloud Adoption Framework for AI, ML and generative AI, and the AICapDev approach extend prioritisation into readiness, prerequisite and dependency assessment, supporting the progression prioritised opportunities → prerequisites and dependencies → implementation roadmap (Amazon Web Services, 2024; IBM, 2026; Kudryavtsev et al., 2025). This is also consistent with the Vendre case, where business-impact prioritisation was followed by assessment of technical feasibility, dependencies, organisational readiness and implementation complexity before the roadmap was developed.
Other companies can follow the same path. They can assess their current AI maturity, select relevant business processes and bring together company, AI and domain expertise. From there, they can identify opportunities, prioritise them by expected business impact, and evaluate the implementation effort and prerequisites each one requires. The result is a phased roadmap that guides the choice of a focused proof of concept once the necessary prerequisites are in place. This staged approach reduces the risk of isolated experiments and helps ensure that future AI investments support wider business development.
The AI strategy development approach used in this collaboration is also part of the GAIK GenAI Toolkit, which helps companies develop and implement generative AI solutions through reusable guidelines and building blocks (Kudryavtsev et al., 2026). Within the toolkit, the approach supports strategic decision-making by helping companies decide which use cases to prioritise, where to begin and which prerequisites to address before moving towards implementation.
The case also illustrates the wider role of EDIH advisory services such as FAIR. Their value lies not only in recommending specific technologies but in helping SMEs structure their thinking, connect business and technical perspectives, identify dependencies and make better-informed decisions about future investments.
Next steps
Vendre is now analysing the roadmap while defining the scope and requirements of its new CRM project. Once these have been clarified, the company will be better positioned to evaluate potential proof-of-concept initiatives. At that stage, Vendre and FAIR can consider which opportunity offers the strongest combination of expected business value, feasibility and organisational readiness.
As Tikkanen put it, “We will be in a better position to evaluate the most valuable next steps and will be happy to continue the discussion.”
SMEs interested in assessing their AI maturity or developing an AI strategy of their own are welcome to contact FAIR to discuss how its advisory services could support them.
Author
Dr Dmitry Kudryavtsev
Senior Researcher, Consultant
Finnish AI Region
dmitry.kudryavtsev@haaga-helia.fi
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