FEATURE : AUTOMATION
Robotic Process Automation , and Business Process Automation , automate existing workflows . These innovations are further democratised by low-code and no-code applications that empower anyone to build intelligent workflows , while AI models can become subject matter experts when trained with domain-specific data .
Push backs from IT
“ Challenges typically vary between different organisations . A common one is the sheer pace of technological change , particularly with respect to AI , which can put pressure on teams , leading to disagreements ,” says Salesforce ’ s Nicault .
This is borne out by research . A recent Salesforce survey of 600 IT professionals revealed a new mandate from their leaders : incorporate Generative AI into the technology stack rapidly . However , IT is pushing back , raising concerns over resources , data security , and data quality .
Nearly three in five IT professionals said business stakeholders hold unreasonable expectations on the speed and agility of new technology implementation . In fact , 88 % of IT professionals claimed they were unable to support the deluge of AI-related requests they receive at their organisation .
Most organisations do not have any idea on the full extent of the processes in their organisation , mainly due to lack of transparency across business units .
“ As automation needs full intelligence on all business units , and potential scenarios coming out of results , lots of companies only see snippets of the process . Therefore , identifying what you really want , and how that results into automated processes , is a challenge for most organisations ,” says ServiceNow ’ s Constantinidis .
Other issues include data validation , data classification and data privacy , and these need to be priorities before full Hyperautomation is possible , since AI system will take the data as true values , without verification .
“ You would need AI skills to teach and feed the data , and you would need a data specialist to clean up your data lake . One important thing is governance and risk in classification of the data . So someone would need the skills to understand what data is allowed in AI and what is not . And you would also need the business insight into every business unit ,” continues ServiceNow ’ s Constantinidis .
“ Scenarios will need to be thought through to fill in the hyperautomated processes . How you run your business , using the new technologies and potential options for execution , will need to be re-evaluated , as that might be different to previous technologies ,” adds Constantinidis .
“ Automation cannot happen in a vacuum ; it must reflect and be informed by the business users who know the processes and systems and will ultimately be the users and beneficiaries of the automation ,” points out UiPath ’ s Gibbs .
Once an automated process is deployed , the work does not stop . Implementing a feedback and learning loop with human validation ensures a continuous cycle of improvement wherein new insights are used to refine the automation process , enhance model accuracy , and drive even bigger business impact .
“ There can be challenges when adopting Hyperintelligent Automation tools and solutions alongside an enterprise ’ s existing workflows . While a willingness to adopt a newer technology stack is important , so is weighing the pros and cons of investing in these technologies ,” says ManageEngine ’ s Ramamoorthy .
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