AI companies could reduce risks associated with emerging technologies by treating responsible innovation as an ongoing organisational capability rather than a one-off compliance process, according to research from UNSW Sydney.
In a news release, UNSW Business School Professor Tania Bucic, who co-authored the research with Babson College Professor Emeritus Gina O’Connor, said conventional safety processes can struggle to account for how technologies behave after they are released and used in new environments.
“The problem usually isn’t a lack of good intentions, or even a lack of frameworks. Most safety processes are for risk compliance and are built as checkpoints: a system is tested, signed off and released,” Prof. Bucic said.
“But emerging technologies like AI keep evolving after launch – in the hands of users, in new contexts, and at a pace and scale no one tested for.”
The research, published in the Journal of Product Innovation Management, introduces the concept of “Responsible Innovation Orientation” (RIO), which the authors describe as a firm-level capability that enables organisations to continue learning and adapting as new risks and consequences emerge. UNSW lists the paper, Responsible Innovation Orientation: A Dynamic Capability for Commercialization of Emerging Technologies, among Prof. Bucic’s 2026 publications.
The researchers conducted 50 interviews with 23 senior figures across 17 organisations in the US, Australia and Europe, covering sectors including pharmaceuticals, food, chemicals, banking and consumer goods, as well as technologies such as artificial intelligence, biotechnology and the Internet of Things.
The research identifies four interconnected competencies that underpin RIO: anticipation, reflexivity, inclusion and responsiveness.
Anticipation involves considering how a technology could behave in different circumstances before problems arise. Reflexivity involves assessing whether commercial opportunities align with an organisation’s stated values, while inclusion involves bringing relevant external and internal perspectives into decision-making.
Responsiveness focuses on monitoring how technology is used after release and acting when unintended consequences emerge.
“For AI developers, anticipation means asking not only ‘what is this model designed to do?’ but ‘what could it do once it’s connected to other systems, given more autonomy, or used by people with different intentions?’” Prof. Bucic said.
The researchers also argue that responsible innovation needs to extend beyond technical development into commercial decisions, including how products are released, scaled, accessed and integrated with other systems.
“A model can pass every pre-release evaluation and still behave in unexpected ways once it is given tools, connected to real systems and deployed by millions of users,” Prof. Bucic said.
According to the research, organisational culture also influences whether these capabilities can operate effectively. The authors identify leadership commitment, a purpose beyond financial return, psychological safety and shared learning as conditions that can support responsible innovation practices.
“In AI companies, the people most likely to notice a problem early are engineers and safety staff – often well before it reaches leadership,” Prof. Bucic said.
“Whether they speak up depends on if raising a concern is valued or seen as slowing the business down.”
The research suggests companies incorporate responsible innovation considerations into existing commercial processes, such as product development and investment reviews, rather than treating them as a separate ethics exercise after key decisions have been made.
“The message for leaders is that responsible innovation is not a brake on commercialisation – it’s a capability that makes commercialisation more resilient,” Prof. Bucic said.
“They built anticipation, reflexivity, inclusion and responsiveness into everyday commercial decision-making, and enabled by and sustained by culture.”
