A practical guide to the potential of AI

By Niklas Sandgren, Data Scientist, Cepheo.

 

 

Artificial Intelligence (AI) has transitioned from being a futuristic concept to a being practical a tool that is ready to be deployed in your organization with just a click.

 

Generative AI, such as ChatGPT or Copilot, can significantly enhance operations and decision-making processes. However, it’s crucial to understand how to get started effectively. While artificial intelligence can easily be deployed, successful value-adding adoption is a completely different thing.

 

For mid-market organizations, the focus should be on using advanced models and integrating them with existing business data. Many AI applications do not require a hefty data platform investment, but rather strategic planning and cultural readiness.

 

Key considerations before you begin

 

Before integrating AI into your business, there are two fundamental pillars that should be considered: culture & education, and infrastructure.

 

1. Building an AI-ready culture

 

Adopting AI is not just a technological upgrade, it is a transformation of how your organization thinks and operates. The first step is to ensure that both leadership and employees are aligned in their understanding of AI’s potential.

 

Bottom-up engagement: Encourage AI usage among employees by fostering an environment of curiosity and safety. Allow staff to experiment with AI solutions to improve their tasks and workflows. Internal champions ‒ employees who take the lead in adopting AI ‒ can serve as role models, driving AI adoption across departments.

 

Top-down leadership: Leadership plays an equally important role in AI adoption. Strategic discussions at the executive level must prioritize AI as a tool for achieving business goals, not just a trendy technology. Allocate resources ‒ both financial and human ‒ to AI initiatives, ensuring that projects are aligned with business objectives. Agile decision-making is key to scaling AI efforts efficiently.

 

Ongoing training and development: Continuous learning is essential. Workshops, whether internally led or conducted by consultants, should be arranged to familiarize staff with AI concepts and tools. Knowledge sharing across departments will help to integrate AI smoothly into everyday processes, allowing for iterative improvements.

 

2. Infrastructure: The building blocks of AI deployment

 

Your organization's existing infrastructure is a critical factor in AI readiness. Fortunately, most companies, particularly those with modern, cloud-based ERP-systems, already have a strong foundation to build upon.

 

Leveraging existing platforms: AI adoption is similar to building with Lego blocks. By utilizing existing tools like Microsoft Azure, organizations can start by defining their business problems and then layering AI capabilities on top of their current systems. For your first AI use case, you can most often start with a minimalistic data foundation you feed with just the data you need. With recent improvements in data tooling, setting this up will not be prohibitive for the project itself.

 

Scaling efficiently: While starting small is often advisable, scaling AI across the organization requires a robust infrastructure. As AI models become more tailored to your specific needs, having an infrastructure that allows for efficient deployment and fine-tuning is critical. The data and processes that feed into AI models will determine their effectiveness, so make sure they are well-structured and relevant.

 

Focus on business needs, not the technology

 

A common pitfall for businesses exploring AI is getting caught up in the technological hype. Instead of focusing on the latest and greatest tools, decision-makers should zero in on the specific business needs AI can address. Whether it’s streamlining operations, enhancing customer service or providing data-driven insights, AI should be a tool to meet clear, measurable business objectives.

 

Experimentation is key. Begin with small pilot projects that address a specific problem. Once you have validated the results, scale the solution across other areas of the business. The goal should be to learn, adapt and iterate quickly.

 

What to avoid: Common pitfalls in AI adoption

 

When adopting AI, it's tempting to aim for grand, company-wide initiatives involving numerous stakeholders. However, this can lead to unnecessary complexity and delays. Start with a small, focused team and a clear use case. Overly ambitious projects that involve too many people or that try to tackle too many areas simultaneously can stall progress and dilute results.

 

Moreover, don’t lose sight of the fact that AI is only as powerful as the data and processes that support it. The value of AI increases with the quality of your data. Do you want AI to have a large-scale impact on your business? Ensure data quality and efficient processes and avoid being held back. Prioritize getting these foundational elements right before moving forward with large-scale AI deployment.

 

Take the first step

 

AI offers mid-market companies an unprecedented opportunity to innovate and optimize their operations. By focusing on cultural readiness, leveraging existing infrastructure and aligning AI initiatives with concrete business goals, you can unlock AI’s full potential in your organization.

 

Start small, experiment and scale as you gain confidence and experience. With the right approach, AI can transform how your organization operates, providing a competitive edge in an increasingly digital world.

 

About the writer

 

Niklas is a Data Scientist and AI specialist, leading Cepheo’s AI initiatives across the Nordics. His expertise in artificial intelligence will help you explore how to use AI to drive efficiency, innovation, and growth in your organization.