Governance System for AI-Adoption at the Team, Organizational, and Industry Levels

In one of our recent newsletters, we discussed System Governance, emphasizing that its broader scope goes beyond data and AI governance. Some of you reached out and asked how it would work throughout the whole ecosystem. The short answer is that you need system governance at the team, organizational, and industry levels to succeed in your AI journey. 

Team Level. At the team level (AKA micro level), your employees use AI systems in their daily jobs to automate their repetitive tasks or perform entirely new things that weren’t possible before.  

While they’re enhancing their productivity using AI technologies, one of the downsides is the potential risk of over-reliance on AI’s outputs and insights. For example, imagine a structural engineer using an AI system to design and optimize a structural system and accepting the output without verification because of the tight project schedule. In this case, who’s responsible if the building collapses later due to structural system failures? That’s why system governance is important. 

At this level, System Governance focuses on providing essential training and documenting dos and don’ts for AI usage. For example, structural engineers must review all AI-generated outputs, including calculations, models, and designs, to confirm their accuracy and relevance to the project specifications. A structural engineer should not approve or stamp any engineering documents generated by AI technologies without full verification. 

Organization-Level. At the org level (AKA meso level), AI adoption extends to all or multiple other departments and divisions within your organization, becoming a primary part of your broader workflows and operational strategies.  

For most organizations, this AI integration and adoption is ad-hoc if they don’t establish a comprehensive framework for evaluating and using AI technologies. Like a football team, if each player has their own strategy and tactic for the game, there is no way the team could win the game. Lack of a framework would cause chaos in your AI journey, and your organization might not achieve its business objectives. This results in wasted time and resources! 

System governance at this level involves creating and enforcing SOPs for AI-related processes and providing channels for reporting incidents and suggestions to ensure the alignment of AI integration with your organizational business objectives and culture. In addition, company leaders need to transfer the lessons learned from AI integration and adoption from teams to the organization level by encouraging knowledge-sharing and collaboration. AI adoption at this level is a cultural shift towards embracing AI and enabling a more data-driven and innovative organizational environment.  

Industry-Wide Level. At the industry level (AKA macro level), AI’s adoption goes beyond each AEC company and extends to the entire AEC industry. At this level, different companies need to work together to deliver projects to their clients. If one company is extremely efficient and fast because of leveraging AI-powered technologies at the organizational level, but the other companies are not fast or efficient enough, the full benefit of efficiency and productivity gain will not be achieved at the industry level or the whole ecosystem. It may not even be beneficial for the company that adopted AI technologies because the entire ecosystem is still not optimized for that purpose. 

For example, imagine a client project where an electrical engineering firm uses AI technologies to design ten times faster; however, the architectural firm responsible for design coordination must wait a long time to receive the mechanical design from a less efficient engineering firm. In this case, no efficiency gain for the client and the industry as a whole. Like a gearbox in which two gears can run fast, but because the other five gearboxes are slow, the car cannot get to the speed that it’s supposed to go. It may even break. 

At this level, AEC companies need to partner and collaborate with other companies to scale AI innovation beyond their organizational boundaries, especially as developing AI solutions become more accessible and easier for all. If we really want to advance the industry, we need to work together to make it happen. As an old saying says, “If you want to go fast, go alone; if you want to go far, go together.” 

How do you ensure successful AI adoption and implementation in your teams, company, and the entire AEC industry?