Why are some businesses generating measurable value from AI while others remain surrounded by pilots, rising costs and impressive demonstrations that never reach daily operations?
In this episode of AI at Work, I speak with Brad Hairston, Director of Strategy at SS&C Blue Prism, about the operational and cultural foundations that separate productive AI programs from expensive experimentation.
Brad spent 30 years in consulting before joining SS&C Blue Prism around seven and a half years ago. He now works within the company’s Customer Zero program, which deploys SS&C’s automation technology internally before it reaches customers. Brad says the program has helped SS&C grow revenue by approximately one billion dollars without adding headcount.
We discuss why AI programs should begin with the business outcome rather than the latest model. Brad explains why companies making progress connect their automation investments with corporate strategy, build on existing robotic process automation and create reusable governance, security, orchestration and measurement practices.
Brad also challenges the idea that AI agents will replace every deterministic automation. Rules-based digital workers remain useful for predictable processes, while AI agents can support work that requires reasoning and adaptation. Combining both approaches can also provide greater control over cost.
Our conversation examines what should happen before an AI agent receives permission to make payments, update customer records or initiate business processes. Brad recommends defined roles, limited permissions, human approval for higher-risk decisions, complete audit trails and an orchestration layer connecting agents with people, APIs and digital workers.
We also discuss how companies can give employees access to no-code automation while maintaining common standards and oversight. Brad describes the federated model used inside SS&C, where individual business units build automations through shared platforms, templates and governance.
For leaders feeling overwhelmed by daily announcements from OpenAI, Anthropic, Google and other providers, Brad offers simple advice: take a breath, return to the business problem and begin with a process where the outcome can be measured.
Is your AI program building reusable capabilities with every deployment, or simply adding another experiment to the pilot queue? Please share your thoughts with me.
