Applied research into the human side of AI: leadership, adoption, governance and how teams come to trust AI-supported work.
More than half of organisations describe their AI use as a chaotic free-for-all, four in five say it is being built in silos, and three in four admit governance has not kept pace with adoption. The models work. What is missing is leadership, trust and a way to govern the work.
Whether or not a firm has a strategy, its people are already using AI. Two thirds of executives believe their organisation has already suffered a data leak through an unapproved tool, and almost all accept that generative AI makes a breach more likely. Banning it does not work. Governing it does.
Staff adopt consumer tools quietly, outside any policy, permission or review.
Sensitive documents are pasted into tools with no control over where they go or how they are used.
Without shared method or review, output quality varies by whoever happened to prompt it.
Project Lighthouse studies the human system around AI: how leaders sponsor it, how teams come to trust it, how it is governed, and how adoption actually takes hold in serious firms. The question is not whether the technology works. It is what has to be true for people to rely on it.
Younger staff already trust AI more than their managers for some tasks, yet only about a third say their manager is an AI champion, and skills and training gaps are the single biggest barrier to responsible adoption. The result is a gap between what people will use on their own and what leadership is ready to stand behind.
Start with the tools already in the building, earn trust through real use, and turn pilots into lasting change.
What sponsors actually do, in the open, that makes a team adopt rather than resist.
Which systems earn trust first, and how trust spreads from there.
The policy, permissions and review that turn shadow use into sanctioned use.
How showing reasoning, sources and confidence changes whether people rely on an output.
How juniors reach senior-quality output, and how senior time is freed for judgement.
How to tell real adoption from activity, and value from usage.
Adoption follows leaders. Where a senior sponsor uses the system in the open, teams follow; where AI is delegated downward, it stalls.
Begin with high-volume, lower-stakes work, prove the output, then earn the right to the harder decisions.
Trust comes from seeing the working, the sources and the confidence, not from a black-box answer.
Clear policy, permissions and review turn quiet shadow use into sanctioned use, and remove the fear that blocks adoption.
Count better, faster, more consistent decisions, not logins or prompts. Usage is vanity; uplift is the point.
Firms working with us through Lighthouse leave with an honest picture of their own adoption: where AI is already being used unofficially, where trust is missing, and what leadership, governance and change support it would take to make AI a normal part of how the work is done.
As systems take on more of the work, trust and governance become the constraint on value, not model quality. Most leaders already believe responsible AI improves returns; far fewer have turned that belief into operating practice. Closing that gap, between principle and practice, is the work of the next few years.
Research into workplace systems, hybrid operations and the future of organisational performance.
Research into investment reasoning, underwriting systems and decision confidence.
Model flexibility, integration and the architecture of safe deployment.
Project Lighthouse is open research into the human side of AI: leadership, trust, governance and how adoption actually takes hold. We publish the questions we are wrestling with, the patterns that recur across real deployments, and the principles that hold up under pressure. We do not publish client data, or the parts of the method still being worked out. It is research, not a product: the questions are the interesting part, and the answers are earned in the work.