Overview of the risks

You want to use AI in your company.

Where to begin? That’s the question we asked ourselves at Pawa. It led us to reflect on the risks of deploying AI in a business. We’ll come back to the choices we made in another piece.

Let’s look here at the deployment risks. This reflection may influence how you approach your first uses of AI.

Poor data quality

Risk: AI depends on reliable data. Incomplete, biased, or outdated data leads to erroneous predictions.
Consequence: Ineffective decisions, loss of trust in the system.

Security and confidentiality

Risk: AI may process sensitive data (customers, employees, trade secrets).
Consequence: Legal risks (e.g., GDPR), data leaks, reputational damage.

Legal or ethical non-compliance

Risk: Not complying with privacy laws, copyright, or algorithmic fairness.
Consequence: Regulatory sanctions, lawsuits, boycotts.

Lack of understanding of AI

Risk: Underestimating AI complexity or treating it as a "black box".
Consequence: Poor configuration, unrealistic expectations, project failure.

Difficult technological integration

Risk: Incompatibility with existing systems (ERP, CRM, etc.).
Consequence: Delays, cost overruns, limited performance.

Resistance to change

Risk: Resistance from employees or managers to AI integration.
Consequence: Partial adoption or sabotage, loss of human expertise.

Uncertain return on investment

Risk: Expensive deployment with no measurable short-term gain.
Consequence: Stakeholder disappointment, premature abandonment.

Dependency on external providers

Risk: Relying on an external AI solution without full control.
Consequence: Loss of autonomy, technological dependency, increased costs.

The revelation

We had our revelation while thinking through these risks.

1. Then we experimented.
2. We invested in developing strong internal expertise.
3. We experimented and developed solutions that we later abandoned.
4. We started over.
5. These are necessary steps toward serious learning.

We not only learned to use AI, but also to manage projects that use AI.

We then applied AI to clear and well-defined use cases, with high-quality, scoped datasets.

These experiments helped us mitigate risks such as:

• Poor data quality
• Security and confidentiality
• Lack of understanding of AI
• Resistance to change (internally at Pawa)
• Uncertain ROI
• Dependency on external providers
• Lack of governance and oversight

We deliver AI inside Pawa

We then considered our clients’ context. For them, technology is not an end in itself — it's a tool that must work and inspire confidence.

We based our thinking on the known fact that Pawa is already deployed in client environments, with excellent, protected data that belongs to them and is managed ethically.

Moreover, the Pawa team handles the complexity of AI to deliver only its benefits through the product.

By integrating AI features inside Pawa, we confine AI to specialized processing in a controlled environment.

By adopting this approach, we knew we were reducing these risks:

• Poor data quality
• Security and confidentiality
• Legal or ethical non-compliance
• Lack of understanding of AI
• Difficult technological integration
• Resistance to change
• Uncertain ROI
• Lack of governance and oversight

The benefits of AI without the risks and perils

We adopted an approach that allows our clients to benefit from AI in an environment they already know (Pawa), without having to face the risks (the Pawa team handles those), ensuring that uncertainties are minimal.

We handle the complexity.

Fewer risks.
Faster return on investment.
Less resistance to change.

A step into the future — without the risk of stumbling.

What’s next...

A second article will follow soon, where we will examine the different AI models and the risks associated with each.

See you soon!