There’s a big difference between experimenting with AI models and successfully embedding solutions in your business. Put simply, adoption happens when people see the value in using a tool. That might be because it amplifies their impact by freeing up time for higher-value tasks or because it allows them to do something they couldn’t do before.
In our experience, there are three critical factors that determine whether AI is successfully adopted and delivers lasting value:
1. Align projects with strategy
AI solutions work best when they are part of a coherent data and analytics strategy, backed by leadership and aligned with business goals. When leaders establish the purpose and stay engaged, adoption spreads faster and deeper.
In one engagement, the leadership team opened workshops themselves, explaining why AI matters to their business. That early signal made a real difference to how engaged teams were and how deeply the solution became embedded in day-to-day operations.
Alignment helps establish the importance of using any new AI solutions and ensures they serve an organization’s broadest goals as well as its tactical needs. For example, one of our clients in the consumer health sector implemented a natural language interface that allowed users to interrogate its library of existing consumer studies. This met an immediate tactical need by making it possible for analysts to find existing insights without commissioning new studies, but it also supported a core strategic priority around improving the customer experience.
That alignment secured leadership support and helped turn this AI tool from a pilot project into something integral to daily work.
2. Control the magic to build trust
The general perception of AI might be that it’s “magic,” but it’s important organizations feel that this magic isn’t out of control. Without that trust, users will default to their old ways of working, and the organization can quickly lose momentum. Transparency, quality, and security are the essential ingredients of trust.
Adding citations and links to sources for generated responses allows users to verify the accuracy of a generative model’s output. This provides assurance that responses are drawn from the right data. Another way to build transparency is to design a solution that presents existing dashboards to users as part of its response.
Transparency helps, but there’s no substitute for quality. If users are constantly verifying outputs, the timesaving and convenience benefits are diminished. And if results frequently need correction, trust erodes fast. We need to find ways to minimize hallucinations and ensure repeatability. This can be addressed in part by modifying the creativity ‘temperature’ of our models – a cooler temperature means the model will focus more on recalling information as it appears and less on generating new text. Focusing on quality requires extensive user testing and a gradual rollout, with careful attention to the relevance, helpfulness, and consistency of the model’s output.
The third element of trust is security. No organization should even start without security and governance in place. Internal data must remain protected and not be used for external training. Getting these controls right early avoids costly delays and builds confidence with stakeholders. This is the foundation everything else rests on. Ethical considerations and the risk of bias must also be addressed, further underlining the importance of strategic alignment across the organization.
3. Build in flexibility and keep costs under control
It’s easy to see early enthusiasm as a sign of success, but longevity is the true measure of adoption. We always ensure our solutions are either modular or flexible enough that, for example, we can swap out Chat GPT for Claude when it makes sense for our clients’ business case. This allows us to leverage the right tool for each use case and control costs.
The right model will depend on the nature of the required outputs and the relative cost of those outputs. Providers vary widely, and it only makes sense to pay for the most sophisticated models when they’re truly needed. Project requirements and strategy will evolve, so solutions need to be flexible enough to switch to alternative models when necessary.
When we built the Indexing and Querying Quarterly Reports solution for Oxford University Endowment Management (OUem), we monitored the cost per token to ensure the project remained sustainable. We also designed the solution so that if we need to change the model, we don’t have to start again, meaning it can continue to deliver value over time.
Final thoughts
AI adoption isn’t about technology alone. It’s also about trust, value, business focus and strategy. Organizations that get these fundamentals right can integrate AI into the fabric of how they work.
