Five Questions Before Scaling Enterprise AI
Getting AI into an organisation is becoming easier - Being ready to
Getting AI into an organisation is becoming easier. Being ready to operate it at scale is not.
Before asking, "How quickly can we scale AI?", I think leadership teams should be able to answer five more important questions:
1) What business outcome are we actually trying to improve?
Reason: AI should solve a meaningful business problem, not become a capability looking for one.
2) Who owns the outcome, the decision and the risk?
Reason: Technology can run the platform. It should not automatically own the business consequence.
3) Can we trust the information the AI depends on?
Reason: Weak data, unclear ownership, poor permissions and sensitive information issues do not disappear with AI. They become more important.
4) What must remain under human authority?
Reason: AI action, human authority and accountability need to be designed together.
5) Is the organisation genuinely ready to operate AI at scale?
Reason: That means more than technology. It includes people, processes, architecture, governance, security, integration, monitoring and change.
A successful pilot proves that something can work. It does not prove that the enterprise is ready to operate it safely, consistently and sustainably.
This is where strong Enterprise Architecture can add real value: connecting strategy, capabilities, information, technology, governance and delivery so leadership can make better decisions. Before approving the next wave of AI investment, here's an idea:
an executive team should be able to sit around one table and answer these five questions clearly. If it cannot, the organisation may not yet have an AI scaling problem.
It may first have a strategy, information, governance, decision-rights or operating-model problem.
The businesses that scale AI well will not necessarily be those that move fastest. They will be the ones that are clear about where AI creates value, who remains accountable, and what needs to be true around it.
Keen to hear people's views on this. If you've been through this before, which of these five questions do you think organisations are finding hardest to answer?