Responsible AI champions transparency, ensuring that business leaders understand how the AI they leverage works.
Many companies recognize the importance of responsible AI, with 84 percent of leaders saying that it should be a priority – according to 2024 Orange research.
Yet, according to the same study, fewer than one in every five businesses believe they have implemented a responsible AI program with a “good level of maturity”.
As such, many organizations depend on their technology vendors to be open about the use of AI within their applications and share best practices.
Nevertheless, many tech providers – particularly in the contact center - put forward “black-box AI”, where decisions and data usage are hidden from users.
Increasingly, buyers will discount these vendors as their internal “responsible AI” initiatives garner momentum.
Why Does Responsible AI Matter?
As suggested, some companies leveraging AI solutions will already expect their suppliers to follow practices that align with internal policies, however half-baked.
Over time, this expectation will increase, especially as brands become more cautious about sharing proprietary data.
Such caution has risen following allegations against various tech providers – including Dropbox and GitHub – of passing customer data onto third-party AI vendors.
Adjacent to these data security concerns are evolving regulations, with governments acting faster to regulate AI than with previous data scandals like Cambridge Analytica.
As these regulations come to the fore, enterprises need AI providers that are responsible, transparent, and adaptable to evolving standards.
Consequently, providers must embrace responsible AI to achieve long-term competitiveness.
The Seven Steps to Responsible AI
Contact center vendors may wish to follow these seven steps to ensure their AI programs are responsible and future-proof.
These come recommended by Ben Cave, Product Director at evaluagent.
- Enable Custom Transcription Services
Most contact center vendors will offer out-of-the-box transcription services.
However, the customer will ideally be able to choose which transcription engine they deploy on the back end, giving them full visibility of which company is parsing their data.
- Model Minimization
Using AI unnecessarily is expensive and wastes energy, especially when brands pull on large language models (LLMs) trained on all the public data under the sun.