What about making AI work ?
What happens if you fail to extend AI governance beyond the technical team.
Your data scientists and IT departments are usually responsible for new technologies. They deliver AI solutions and do everything possible to protect customers, the business and society.
But with every project, despite their contribution, the business teams are still frustrated by the results/output produced by AI.
Your capabilities are limited to deal with the full range of risks generated by AI, which means you narrow the scope of AI applications and focus only on the least risky AI initiatives.
Now, imagine the benefits to your company if you work differently on AI
#1 - AI not considered as "just another IT tool"
Senior executives and the board of directors are aware of the nature of the technologies involved in AI and do not view AI as "just another tool."
At their level, they understand how AI can change the business model, what role it plays in the digital transformation journey for the long term and ensure that the AI strategy is in line with mission and value of the company with respect to all stakeholders (employee, customers, shareholders, regulators, government and citizens).
Where appropriate, they also have the knowledge to challenge stakeholders by ensuring that solutions are providing value and properly implemented.
#2 - Top management involved in responsible AI
Even if they are not experts in the AI field, senior management executives understand interactions between data and algorithm in your environment and are aware that AI can potentially harm individuals and lead quickly to financial, operational, and reputational risks for your company.
As a result, they are able to react quickly in case of threats, trigger effective actions and make responsible decisions.
They can quickly manage and report any potential compliance/regulatory issues to the supervisory board.
#3 - Accelerate AI conditions for success
As with any other project, by involving cross-functional teams from the beginning, you accelerate ownership and enhance conditions for success.
Legal and risk professionals work with data science teams to help understand bias and safeguard data protection.
Systematic collaboration across business, legal, compliance and audit along the lifecycle will secure the development process and accelerate the go-live.
#4 - Realize economies of scale by sharing same language on AI
You are able to share common terminology around AI and started to implement common standards around AI : shared definition of AI, common AI policies, inventory, risk assessment, code of conduct, MLOps standards and tools, AI outsourcing processes , regulatory- compliance assessements, ...
You don't recreate the nuts and bolts for every new AI initiative and make use of existing practices and learn from each other !
You are establishing the foundation to build up an AI sustainable culture in your organization !
Don't fall into the trap of thinking that AI governance is only for the IT department!
Otherwise the future of digital transformation in your company is at risk!
AI governance is a cross-functional job for the entire organization.
And like any other transformation project, it always includes communication, education and change management across the board.