Ask Our AI Experts: An AMA With Our Tech Leads

Q1: What Are the Most Common Mistakes Companies Make When Starting an AI Project?

While the promise of artificial intelligence is immense, its successful implementation depends on avoiding several common mistakes that can cause a project to fail from the start.

• Vague or Misaligned Objectives: Often, projects fail when business objectives and AI deliverables are not closely aligned.
• Underestimating Data Challenges: Insufficient or poor-quality data is a leading cause of project failure. Rigorous data engineering is non-negotiable.
• Proof of Concept vs Production: If you over-optimize PoC environments without planning for real-world deployment, scalability, and monitoring can lead to operational failures.
• Ignoring Infrastructure and MLOps: Lack of robust deployment, CI/CD, and monitoring pipelines results in fragile systems.
• Stakeholder Exclusion: When business, IT, and end users are not involved early on, the result is technically sound but commercially irrelevant solutions.
• Early Evaluation: If you do not collect feedback on the AI system’s prediction output at an early stage can lead to errors.

Q2: Should We Use Open-Source LLMs or Stick with Proprietary APIs like OpenAI or Anthropic?

The choice depends on your technical requirements, risk tolerance, and business context:

• Open-source LLMs such as LLaMA, Phi, and DeepSeek are great for teams needing deep customization, regulatory control, and cost efficiency on a large scale. However, they require significant technical investment.
• Proprietary APIs provide quick deployment, best-in-class performance, and managed infrastructure, but at the expense of transparency and long-term cost.

Q3: How Do You Measure the Success of an AI Implementation?

A strong AI evaluation framework generally includes the following technical and business metrics:

• Align Metrics with Business Goals: Set clear, measurable objectives such as increased revenue, reduced churn, improved productivity and fewer defects.
• Choose Technical and Business KPIs: Accuracy, F1-score, latency, uptime, model drift, conversion rate, cost savings, customer satisfaction, and operational efficiency.
• Continuous Monitoring & A/B Testing: Use dashboards for real-time tracking. Implement A/B tests to compare model variants against KPIs.
• Qualitative Feedback: Gather user and stakeholder feedback to capture nuances not reflected in quantitative data.
• Iterative Improvement: As business requirements change, you need to periodically assess and revise metrics and models.

Q4: How Can Your AI Strategy Be Future-Proof Against Rapid Tech Disruptions?

Staying ahead in AI requires more than just cutting-edge models but it demands an adaptive strategy that evolves with changing technology, data, and business needs.

To make your AI approach truly resilient, you should be able to test models quickly, relying on solid, reliable data and automated ways to assess their performance. Here’s how to build that resilience:

1. Think Modular and Flexible

• Design your AI systems like building blocks. Let different parts (models, data flow, connections to other programs) be easily swapped or improved without messing up the whole system. This way, you can use new technologies quickly without completely redesigning the system.
• Tools like Docker and Kubernetes help you deploy and scale your AI services across different environments.

2. Invest in Continuous Learning and Model Adaptation

• Implement automated retraining pipelines to keep models current as data and business contexts evolve.
• Use techniques like transfer learning and foundation models to accelerate adaptation to new tasks or domain s.

3. Embrace Open Standards and Interoperability

• To ensure long-term adaptability of GenAI systems, develop modular, interoperable architectures and use open tools whenever you can.
• Create APIs and data schemas for interoperability, allowing easy connection with future AI and outside partners .

4. Establish Robust MLOps and Governance

• Install end-to-end MLOps pipelines for versioning, testing, monitoring, and rollback of models in production.
• Integrate AI governance frameworks to ensure compliance, auditability, and responsible AI practices, ensuring adaptability to new regulations and ethical standards.