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.
As such, brands should offer smaller, custom-built AI models, tailored for the various use cases across their platforms.
- Vector Mapping
Contact center vendors should build out their deep search capabilities so that they can scan all customer conversations and draw insight without turning to an LLM.
After all, this reduces the end-user dependency on expensive third-party AI services, allowing the provider to better control customer costs.
- Prompt-Level Optimization
Most vendors focus their generative AI (GenAI) investment on training proprietary models. Yet, prompt engineering is equally critical.
After all, that prompt is compatible with any LLM. So, if the customer wishes to change the back-end LLM, it will still deliver results with high accuracy. That leads to point five…
- Candidate Models
A candidate model architecture allows customers to swap out the LLM behind a GenAI use case. So, ideally, the customer may choose from a set of predefined models for a use case, or – alternatively – they can plug in their own hosted, trained, or public options.
In a sandbox environment, the customer may then test the models – alongside the prompt – to spot which option delivers the best results across use cases.
- Private Instance Back End
Enterprises with their own proprietary LLM or internal AI models should be able to run platforms like evaluagent on their back end by simply plugging it into their contact center application.
As a result, these organizations don’t need to worry about who is viewing that data because it’ll only be accessible to the people within the business.
- Reportable Reasoning
Responsible AI dictates that the user should always know why an autonomous AI model has taken a particular action or reached a specific conclusion.
As a result, businesses may audit and adjust AI reasoning. That’s critical for compliance and fairness.
evaluagent: A Vendor Following These Steps
The steps laid out above will support a contact center vendor in delivering enterprise-ready, future-proof, and adaptable solutions.
evaluagent, the prominent contact center quality assurance (QA) and conversational analytics vendor, has built out its platform following these steps.
As such, its platform is compliant, flexible, and secure, allowing companies to maintain control over their data while leveraging cutting-edge AI technology.
To learn more about its technology and approach to responsible AI, visit evaluagent.
