Before you add AI to your business, ask yourself why

Authors: Hassan Saketi & Maria Murto

How AI Is reshaping the way organizations work

Artificial intelligence refers to algorithmic and computational systems capable of performing tasks that would otherwise require human intelligence, including pattern recognition, prediction, natural language processing, and decision-support (Russell & Norvig 2021). AI applications range from predictive analytics and automated customer services to generative systems capable of producing text, images, or code. Particularly for SMEs, AI can offer significant opportunities. In contrast with large corporations, SMEs often operate with limited human and financial resources. AI instruments can help automate repetitive tasks, analyze large datasets, and support strategic decisions that would otherwise be extensive expertise or infrastructure (Arroyabe et al. 2024; Toivonen 2026). Organizations increasingly are using AI to support decision-making processes, automation, and enhance productivity in areas including education, healthcare, design, and business operations (Dwivedi et al. 2023). Recent advances in generative AI and large language models have accelerated this trend considerably. These systems can produce reports, marketing materials, customer responses, and analytical summaries within seconds—capabilities that have extended AI adoption well beyond technical departments and into everyday professional practice (Dwivedi et al. 2023; Schwaeke 2025).

However, despite these benefits, the rapid diffusion of AI also introduces new organizational risks. When adoption occurs without clear guidelines, governance structures, or a realistic understanding of the technology’s limitations, the potential for unintended consequences grows substantially (OECD 2019; Taeihagh 2025). If the automatization with AI does not work out as wanted, the organization might even have to hire people back or go through change negotiations (Ihalainen 2026). Information security is also a major risk for all organizations when starting to integrate AI into systems (Sundberg & Rinne 2026).

Understanding chaos in AI adoption

In the context of digital transformation, “chaos” does not refer to complete organizational breakdown. Rather, it describes a condition in which technological systems introduce confusion, inconsistency, or loss of control within established processes. This form of operational disruption typically arises when new technologies are introduced faster than organizations can adapt their structures, policies, and competencies to accommodate them.

In AI adoption, this dysfunction tends to manifest in several identifiable ways. Overreliance on automated outputs is among the most common. When professionals accept AI-generated results without adequate verification, incorrect conclusions can spread rapidly through an organization’s workflows and decision-making processes (Floridi et al. 2018; Bender et al. 2021). Fragmented adoption presents a related challenge. When individual teams or departments deploy AI tools independently and without coordination, the result is often a patchwork of incompatible workflows, inconsistent data practices, and duplicated effort. Organizational cohesion suffers, and the anticipated efficiency gains are eroded (Papagiannidis et al. 2025).

A third source of risk lies in the inherent limitations of generative AI systems. Large language models generate responses based on statistical patterns rather than verified knowledge, meaning that their outputs can appear highly credible while containing material inaccuracies—a phenomenon known as hallucination (Bender et al. 2021). Recent research suggests this is not merely an occasional technical error but a structural characteristic of probabilistic language models (Sun et al. 2024).

In these circumstances, the organization does not lack technology. It lacks the strategic framework necessary to manage it responsibly.

Preventing chaos through responsible AI integration

Avoiding AI-driven operational disruption requires organizations to move beyond the question of which tools to adopt and toward a broader question of how AI will be governed, integrated, and evaluated against strategic objectives. Four principles define a responsible approach:

1. Establish clear governance frameworks. Organizations should define which AI tools are appropriate for specific tasks, how outputs will be validated before use, and how accountability is distributed across teams. The European Union’s emerging AI governance standards offer a useful reference point for organizations developing internal policies (European Union 2024).
2. Maintain meaningful human oversight. AI should function as a tool that augments professional judgment, not one that supplants it. Decision processes, particularly those with significant organizational, financial, or reputational consequences, should retain a human review stage (Schneider et al. 2024).
3. Invest in AI literacy. The ability to use AI tools effectively is inseparable from the ability to evaluate their outputs critically. Organizations that invest in developing this competency across their workforce are better positioned to identify errors, recognize bias, and exercise informed judgment about when AI-generated content can be trusted (UNESCO 2021).
4. Coordinate adoption organization wide. A unified AI strategy ensures that tools are deployed in alignment with broader organizational goals and that different teams operate within a shared framework. This coordination is essential to preventing the fragmented workflows that undermine both efficiency and coherence (OECD 2019; Taeihagh 2025).
5. Distrust is a decisive factor for the intention to not use AI (Ledesma Chaves et al. 2026). The AI strategy therefore should incorporate measures to strengthen trust in automated processes, as well as to invest in a smooth and intuitive user experience.

AI offers opportunities, but only strategy turns them into value

Artificial intelligence offers significant opportunities for innovation, efficiency, and data-driven decision-making helping instruments across industries. However, these advantages do not emerge automatically. It is needed to be aware, when AI is adopted without a clear and consciously strategic framework, organizations risk transforming technological innovation into operational chaos. Creating own strategy for AI can prevent the risks related to the adaptation of AI in organizations. Keeping up with extremely fast development requires resilience and abilities to manage change.

It is essential for modern organizations to understand the mechanisms behind this chaos, knowing how it emerges, how it affects performance, and finding how it can be prevented. Professionals can ensure that AI functions not as a source of disorder but as a powerful tool for sustainable innovation if they do so through strategic governance, human oversight, and continuous development of AI literacy.

References

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Authors

Hassan Saketi is an international business student at LAB University of Applied Sciences. He is interested in artificial intelligence and believes that businesses should understand and control AI, not the other way around.

Maria Murto is an RDI specialist and a project manager at LAB University of Applied Sciences. She works as a project manager in mAInIO – The potential of generative AI as a business enhancer/enabler project.

Illustration: https://unsplash.com/photos/robot-arm-playing-chess-against-a-human-opponent-Jzlv_k-oeiE (Unsplash Licence)

Reference to this article

Saketi, H. & Murto, M. 2026. Before you add AI to your business, ask yourself why. LAB Pro. Cited and date of citation. Available at https://www.labopen.fi/lab-pro/before-you-add-ai-to-your-business-ask-yourself-why/