Trust and assurance: the key to scaling AI deployments
Research Report |Artificial Intelligence (AI),AI management,Autonomous operations,Privacy & trust
Trust and assurance: the key to scaling AI deployments
Our new report, in collaboration with the IBM Institute for Business Value (IBV), explores the status of communications service providers’ governance, assurance and trust strategies.
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Trust and assurance: the key to scaling AI deployments
As communications service providers (CSPs) seek to move from early AI usage to deploying AI at scale across their organizations, trust and assurance are moving to the forefront of their strategies. Those deployments are impacted by the introduction of increased autonomy in networks, potentially magnifying any AI technology malfunctions and hallucinations.
As a result, operators are taking steps to move from responsible AI, to governance, assurance and finally trust. While responsible AI enshrines the principles, AI governance puts in place the rules and guardrails. But it’s trustworthy AI that CSPs need to deliver to ensure that they fully meet the requirements of their AI governance.
As part of the report, in collaboration with the IBM Institute for Business Value (IBV), we carried out a survey of 130 leaders in operators globally. It set out to test whether CSPs are ready to let AI run their operations and whether they are deploying autonomy faster than they can govern it. The results are shown throughout the report.
We also conducted in-depth briefings with AI leaders within operators to get their reflections on the findings of the survey and to explore the status of their AI governance and assurance initiatives, as well as the evidence they are producing to demonstrate that their AI is trustworthy.
- The status of operators’ AI programs and their transition from using AI to enhance human processes to autonomous systems that replace those processes
- CSPs’ efforts to make AI results more accurate and predictable
- The risks to operators’ businesses if AI goes wrong
- The status of CSPs’ AI governance programs and how they are evolving as they start to deploy autonomous systems
- What CSPs need to do within their AI operations to see what it is doing and to generate evidence of its inputs and outputs
- The gap that sits between AI governance and operations and the emergence of an AI assurance function and capability.
20 years as a chief analyst and research director covering the telecoms sector with a focus on operator business models, transformation and strategies for developing new revenue streams.
