Artificial intelligence (AI) and large language models (LLMs) have revolutionized our perception of technology and its business applications, offering unprecedented benefits such as process automation, enhanced decision-making, and personalized customer experiences. However, before leaping onto the AI bandwagon, organizations should consider several key factors.

In this post, Kodan’s senior software developer, Mika Nevalainen, and service designer, Ville Yli-Knuutila, discuss what organizations should keep in mind before implementing AI solutions.

Need and value for artificial intelligence and MLL

Firstly, the fundamental question any organization should ask is whether there is a genuine need for AI. Will AI deliver the necessary value to address that need? It is crucial to identify the specific problem or opportunity where AI can provide a tangible benefit.

Without a clear purpose or a problem to solve, investing in AI might not yield the expected return on investment. Consider the early days of the social media and mobile boom: organizations felt compelled to launch social media campaigns and mobile applications without a clear strategy. Indeed, the medium is the message, but there still needs to be substance in the message.

Once the need is established, consider what form the AI solution should take. Would a service targeted at end-users suffice, or is a more customized approach necessary? Options range from Software as a Service (SaaS) to Platform as a Service (PaaS), or even a bespoke web service. Some solutions, such as BastionGPT, are pre-tailored to a client's domain, offering a more focused approach.

If your expertise on the matter is insufficient at this point, consider contacting us for advice and ideas.

Strategic goals for artificial intelligence

Is the AI initiative aligned with your organization's strategic goals? Whether it involves improving customer satisfaction, reducing operational costs, or driving innovation, the AI solution should directly contribute to achieving these objectives.

Sometimes, a centralized "in-house" service might better serve strategic purposes, but it is also worth considering cloud services like Azure OpenAI to leverage their extensive resources and capabilities.

Infrastructure and security

Understanding how the AI service will be structured is vital. Will it be a single instance or a collection of multiple separate instances? What kinds of architectures, services, and standards do they utilize themselves? Where, for example, do the databases for different GPT services' prompts and learning materials reside? Who has access to them?

The protection of data and management of user authentication and authorization are also paramount. Organizations need to ensure that their AI solutions comply with data protection laws and are secure against breaches.

For example, a complex illustration of how Azure's services could be utilized for this purpose is seen below.

Sustainability, maintenance and quality assurance

AI services can be significant resource consumers. It's essential to consider how to minimize this impact, aligning with Green ICT principles. Additionally, maintaining AI services throughout their lifecycle, including monitoring and updates, is crucial for long-term success.

Finally, ensuring the quality of AI services is critical. How will you reliably test the services? What aspects need testing to guarantee that the service meets the required standards and delivers the expected value?

Think before you AI

Adopting AI in your organization is not a decision to be taken lightly.

It requires thorough consideration of the need, value, strategic alignment, infrastructure, security, sustainability, and quality assurance. By carefully evaluating these aspects, organizations can make informed decisions that ensure their AI initiatives are successful and deliver tangible business benefits.

If you need any help with AI implementations and ideas, let us know. We are here to help.