Boards and AI - what questions to ask?
Last week we explored how Boards and Executive teams must adapt to technological change and we got an unprecedented amount of feedback from you on the topic. For those who missed it please See a link to that blog post here
One theme that came up repeatedly in the feedback was around the challenge, and opportunity, posed by AI.
Many of you will have consumed technical and operational material produced on the topic but with this feedback in mind, and input from our panel of experts, here’s some practical advice from your peers to keep in mind.
Balance Performance and Conformance (again!)
In one of our earlier blog posts we talked about the balance Boards, and executive teams, must strike between managing for performance and conformance.
That tension and balance applies strongly to AI.
On one hand Boards must be thinking about how AI will impact their company, their industry, their partners and their market and how to implement strategies and programs to sustain competitive advantage. With AI this will often require (safe) experimentation, new partnerships and challenging established ways of working and operating models.
On the other hand Boards must be sure there are adequate risk management structures and systems, conformance, in place to manage this novel threat.
Balancing these two drivers will require Board members to acquire new skills, seek inputs from experts, spend time with key internal leaders and carefully consider the succession management matrixes for Board positions, even the current composition of the Board itself.
After Strategy comes Structure
Assuming your organisation is able to design and implement a compelling strategy modern management theory tells us the right structure(s) must follow.
This is absolutely true for AI initiatives.
A number of our clients remarked on how strongly the dilemma around structuring for Data Analytics echoes in the current debate.
Should all AI resources be placed in a ‘center of excellence’?
Should AI resources be split across IT and OT organizations?
Who owns specific AI initiatives for a specific Business Unit - the centralized team (if applicable) or the BU itself?
More than many debates this one may be answered in application - can the business access sufficient AI resources to even consider a decentralized model? For many organizations outside Financial Services and IT a centralized model is likely the most prudent option.
Talent
Our panel shared the other big theme worth pursuing relates to Talent - specifically how will the organization recruit, retain, develop, build, buy the required human capacity to win with AI.
Too many organizations simplify this to mean ‘how will we recruit the AI specialists we require?’ when an even bigger challenge for most organizations is ‘how will we develop AI capabilities in all our people?’.
Another challenge with strong echoes of the early days of Data Analytics as reported by many clients is the fear of becoming ‘hostage’ to concentration risk. This risk may apply to the scarce, senior resources leading AI initiatives, it may apply to tool and platform choice, even the partnerships entered into during early stages of AI exploration.
Even for BoardPac, a technology company at its absolute core, many of these challenges are still present on a daily basis. And that’s partly why we’re so proud of our latest product release; QME AI.
We’ve been able to product the world’s first AI enabled board portal and are incredibly excited to share more news on our upcoming releases with even more sophisticated capability in the coming months.
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