What is your biggest concern with deploying AI in your data and analytics workflows?

Deploying AI in data and analytics workflows offers transformative opportunities but also presents significant challenges. Besides below , My biggest concerns would be - Alignment with Business expectation , Value delivered and the cost.

1. Data Quality and Bias
AI models are only as good as the data they are trained on. Poor data quality, missing values, or biased datasets can lead to inaccurate or discriminatory outcomes, undermining trust and adoption.

2. Lack of Explainability
Many AI models, especially deep learning ones, are often "black boxes," making it challenging to interpret how decisions are made. This opacity can be a barrier in regulated industries or situations requiring high accountability.

3. Integration Complexity
Embedding AI seamlessly into existing data architectures, like ETL pipelines, data warehouses, or analytics dashboards, can require significant effort, especially when legacy systems are involved.

4. Scalability and Performance
AI workloads can be resource-intensive, and scaling them to handle enterprise-level data can lead to latency, cost, and infrastructure bottlenecks.

5. Ethical and Compliance Risks
Ensuring AI systems comply with data privacy regulations (e.g., GDPR, CCPA) and ethical standards is a major concern. Missteps here could lead to reputational damage and legal repercussions.

6. Stakeholder Adoption and Trust
Non-technical stakeholders may struggle to trust AI-driven insights over traditional methods unless clear value, reliability, and alignment with business goals are demonstrated.

7. Model Drift and Maintenance
Over time, models can degrade as data distributions shift or as organizational needs evolve. Keeping models updated and relevant without disrupting workflows requires ongoing attention.

8. Lack of Skilled Talent
Developing, deploying, and maintaining AI systems demand expertise in data science, engineering, and domain-specific knowledge, which can be a bottleneck if resources are scarce.

Addressing the Concerns:
Implement robust data governance frameworks to ensure data quality and compliance.
Prioritize explainable AI and choose interpretable models where possible.
Invest in scalable cloud-based architectures and MLOps frameworks to streamline deployment and monitoring.
Engage stakeholders through education, prototyping, and demonstration of clear business value to build trust.
Establish ethics boards or AI governance teams to guide responsible AI deployment.

Data security and data literacy.

Data security and organizational readiness

There are two concerns per me. First one is the data infrastructure and quality. Most organisations still use monolithic and advanced technology in tandem for various needs/process. Hence it gets tricky during the integration and implementation phase opening whole new set of vulnerable systems/spots.

Second is the data quality and privacy. Most organizations do not have a set AI council which has a broad responsibilities. In many cases if it exists it does only for the sake of a ticking a check list addressing no real problems but living in artificial harmony with the current ecosystem. Establishing proper end to end governance throughout the lifecycle is critical.

The biggest concern I have is the expectation to use AI as a solution in search of a problem. It is important to have a business value driver for deploying AI in data analytics workflows. 
All the other valid concerns related to people, process and change management can fall into place through once there is a defined well recognized value that the organization needs.
Data quality, security and other data management enablers are huge challenges that need resourcing dedicated time e.g. time needed to define quality, monitor, fix in upstream systems. Without a value driver, it's been tough to get dedicated time of business areas to work through these challenges.