A survey of 500 business executives from organizations with 500 or more employees finds 88% are to some degree now employing artificial intelligence (AI) to improve the services they provide.
Conducted by Atlassian, the survey finds 80% are crediting AI with improving their ability to make more data-driven decisions, with 79% also noting that AI has improved customer service delivery. Nearly two thirds (64%) said improving customer experience was a key factor in their decision to embrace AI, with 78% acknowledging that AI has also positively made their workforce more efficient. More than three quarters also noted that predictive capabilities enabled by AI have helped them more effectively predict and prevent service issues.
Additionally, the survey reveals that 89% of organizations plan to expand or further invest in AI technologies in service management over the next 12 months. Major focuses are training and upskilling initiatives (49%), enhanced customer experience with AI (48%), data insights and reporting (43%) and process automation (43%). More than a quarter (27%), however, have yet to implement any type of AI training.
Overall, a full 71% of respondents said their organization is still in the early phases of adoption, compared to 29% that said they are now optimizing usage. There is, however, already a gap emerging between organizations that have more aggressively adopted AI compared to those that are still in the early phases of adoption.
There is already a 2x return on investment (ROI) being enjoyed by organizations that invest heavily versus those that view AI as a simple tool, a gap that will increase to 4X by 2026, says Jamil Valliani, head of AI product for Atlassian. “Leaders are exploring use cases and taking more risks,” he says.
The most widely adopted use cases for AI currently are analytics and dashboards (55%), followed by personalized self-service solutions for customers and employees (50%) and the streamlining of information access and management (44%).
The top key performance indicator (KPI) being tracked is customer satisfaction (42%), followed closely by operational cost savings (41%), accuracy and performance of AI models (41%), time saved (36%) and response times (35%).
The challenges organizations are encountering, however, are myriad, with data privacy/security (36%), lack of skills (32%), budget constraints (31%), data quality (28%), integration (27%) and uncertainty of AI capabilities (27%) at the top of the list. Nearly half (49%) are also conducting needs assessments to identify specific areas where AI can help, while 46% are ensuring data readiness to support AI models effectively.
Organizations need to be especially aware that the output being provided by an AI model is probabilistic. That means that any deterministic workflow involving an AI model will need to be verified by humans, said Valliani.
Ultimately, however, every organization is going to wind up benefiting from AI whether anyone realizes it or not. Capabilities such as AI agents that are trained to automate specific tasks are now being embedded into almost every application. The issue now is determining how best to harness those capabilities within workflows that generally need to not only be performed the same way consistently, but also require a high degree of accuracy. After all, the challenge with AI is that it doesn’t discriminate when it comes to sharing insights, regardless of how right or wrong they might be.
