Artificial Intelligence thrives on data. Data feeds algorithms, supports predictions, and enables automated decisions. However, when data is unreliable, unrepresentative, or poorly governed, AI ceases to be a competitive advantage and becomes a risk.

As organisations accelerate their adoption of AI, it becomes clear that the challenges go far beyond technology. Ethical, regulatory, cultural and organisational issues come into play. 

In this context, the role of the consultant becomes essential in helping companies navigate this complex landscape.

The main challenges in using data in AI

Before addressing advanced models or intelligent automation, it is essential to understand the obstacles that hinder the success of artificial intelligence in organisations. Many of these challenges stem from the data itself and how it is managed.

Some of the most common obstacles are:

• Data quality, consistency, and representativeness: Duplications, missing values, or unrepresentative datasets compromise the reliability of models and decision-making.
• Privacy and data protection: Regulations such as the GDPR or LGPD require a balance between personalisation, anonymisation and legal compliance.
• Algorithmic bias and discrimination: Historical data may reflect existing prejudices and generate unfair or biased results.
• Scalability, cost and performance: Large volumes of data require robust infrastructure and efficient optimisation strategies.
• Governance, transparency, and accountability: The traceability and explainability of models are essential to ensure trust and respond to audits.
• Data and model security and integrity: Attacks, dataset manipulation, and misuse of information pose growing risks.

Trends and solutions

To respond to these challenges, new approaches that place trust, quality, and sustainability at the center of AI strategy are gaining relevance.

Here are some of these trends:

• Data-Centric AI: focus on improving data rather than just algorithms
• Synthetic data: generation of artificial data to reduce privacy risks
• Federated learning: distributed training without direct data sharing
• MLOps and AIOps: standarisation of model lifecycle and pipeline automation
• RAG (Retrieval-Augmented Generation): integration of generative AI with internal knowledge bases
• Data and model observability: continuous monitoring of metrics and alerts

The role of consultants in companies

In an increasingly complex technological ecosystem, consultants play an essential role in connecting data, technology and business, ensuring that AI generates real and sustainable value.

• Diagnosis and strategy: assess maturity and define a roadmap aligned with the business
• Governance and compliance: implement policies and ensure regulatory compliance
• Data quality and preparation: support data cleansing and enrichment
• Technology and architecture selection: recommend appropriate and scalable solutions
• Training and cultural change: promote workshops and encourage a data-driven culture
• Continuous monitoring and auditing: establish metrics and ensure transparency