Technology Strategy and AI in Engineering Workflows – Ep 109

What is a technology strategy on AI, and why is it important in engineering?

Technology strategy on AI integrates AI tools into business strategies to enhance efficiency and client outcomes in engineering. It ensures firms use AI not just as a tool but as a strategic enabler for innovation and improved project delivery.

• Aligns AI capabilities with business goals
• Improves decision-making and workflows
• Enables differentiated client outcomes

How does AI impact engineering workflows today?

AI optimizes inputs and automates routine tasks within workflows, allowing engineers to focus on higher judgment tasks. It facilitates backward optimization and supports complex orchestration of processes, but requires human oversight for open-ended decisions.

• Automates repetitive calculations
• Enhances optimization and design options
• Requires human judgment for non-deterministic tasks

What are some limitations of AI in engineering judgment?

AI struggles with judgment criteria, which involves evaluating if a question or answer is appropriate and aligns with the client’s needs. While AI excels at orchestrating defined workflows, it cannot replace human intuition and context in ambiguous or creative decisions.

• Poor at assessing plausibility without proof
• Lacks human contextual insight
• Best used for structured, closed problems

Why is separating IT and business application teams important for technology strategy?

Separating IT and business applications ensures IT focuses on infrastructure, security, and maintenance while business teams drive innovation and adoption of software tools aligned with client and firm outcomes. This specialization improves both technological efficiency and strategic deployment.

• IT handles deployment and security
• Business apps focus on client outcomes
• Prevents dilution of innovation efforts

How can firms manage AI risks and data confidentiality?

Firms should implement enterprise AI solutions with controlled environments, manage usage via single sign-on, and segment personal from business AI activities. This protects intellectual property, ensures compliance with contracts, and mitigates inadvertent data leakage.

• Use enterprise-level AI accounts
• Apply single sign-on (SSO) controls
• Separate personal and business usage

What practical first steps can firms take to adopt AI in their workflows?

Start by automating language-based tasks such as proposals or contract editing with AI skill templates. This approach lets teams manage AI-assisted workflows safely, building familiarity and identifying process improvements before scaling more complex AI applications.

• Identify simple language-based workflows
• Develop AI-driven templates and skills
• Use AI to assist but not replace human judgment

Why is curiosity a critical skill for adapting to AI in engineering?

Curiosity drives individuals to explore and understand new AI tools despite initial challenges. It helps them develop valuable questions and avoid passivity, enabling continuous learning and effective use of AI technologies in evolving workflows.

• Encourages persistent learning
• Helps overcome complexity and frustration
• Fosters proactive problem-solving

How does AI affect liability and responsibility in professional engineering?

AI does not replace the professional responsibility engineers hold for their work. Liability frameworks and insurance underpin the value of human judgment, ensuring engineers remain accountable for decisions that AI supports but does not determine.

• Engineers retain ultimate accountability
• Insurance and licensure govern liability
• AI is a productivity tool, not a decision-maker