Pune: As companies increasingly embed artificial intelligence into enterprise software that manages contracts, pricing, procurement and revenue operations, Eshaan Jain, who leads Salesforce and Vlocity CPQ product for T-Mobile through Mphasis, has underlined the need for stronger governance frameworks to reduce operational and regulatory risks.The issue came into focus during a virtual interaction with industry leaders and tech professionals recently in the city, where discussions centred on the growing use of AI-driven systems in quote-to-cash (Q2C) and contract lifecycle management (CLM) platforms, particularly within the Salesforce ecosystem that supports large commercial operations worldwide.Jain noted that as organisations adopt autonomous AI agents capable of generating quotes, analysing contracts and automating approvals, ensuring the accuracy of data, audit trails and access controls has become critical. Errors in contract interpretation, unauthorised pricing changes or weak approval workflows could expose companies to financial and compliance risks.Jain also highlighted that the integration of large language models into commercial software is reshaping enterprise operations, but also creating new challenges related to governance, security and accountability.Speaking during the virtual session, Jain, an enterprise software specialist, said organisations need to treat AI systems in revenue operations with the same rigour traditionally applied to financial and IT controls.“AI systems are only as reliable as the data, contracts and business rules that support them. Enterprises need strong governance frameworks, structured commercial data and clear accountability mechanisms before introducing autonomous agents into pricing, contracts or procurement workflows,” Jain said.He added that the future of enterprise AI would depend not only on model capabilities but also on transparent approval systems and audit mechanisms.“Automation should strengthen existing controls rather than bypass them. Businesses must ensure that every AI-assisted decision, whether related to pricing, contracts or approvals, remains traceable, explainable and aligned with regulatory requirements,” he said.Industry discussions during the session also highlighted how machine learning is being used to improve contract intelligence. AI tools are increasingly being deployed to identify key clauses, analyse liabilities and streamline negotiations across large contract portfolios, helping organisations reduce processing time and improve operational efficiency.Jain said that enterprises are moving beyond simple automation to create systems that can interpret contractual obligations and support decision-making at scale. However, this requires high-quality datasets, standardised processes and stronger cybersecurity safeguards.Jain shared examples from his experience across sectors involving Salesforce security, enterprise controls and AI-enabled contract management. He referred to projects involving security analytics, segregation of duties and IT controls, as well as work on supply chain procurement systems handling large transaction volumes.According to him, AI adoption in commercial software is also driving interest in areas such as carbon-aware computing, efficient use of computing resources and resilient digital supply chains.The discussion further examined the role of governance in AI deployment, including accountability frameworks for autonomous systems and methods to reduce risks arising from AI-driven decisions.“Companies often focus on deploying AI quickly, but sustainable adoption depends on data quality, security controls and governance structures. Organisations that invest in these foundations are more likely to build systems that can be trusted at scale,” Jain said.Participants noted that demand for engineers with expertise in AI governance, cybersecurity, enterprise platforms and contract intelligence is expected to rise as businesses expand the use of autonomous systems in revenue and supply chain operations.Jain concluded by stating that the convergence of AI, commercial software and regulatory compliance is creating a specialised domain that could become increasingly important as enterprises automate core business processes across finance, procurement and customer operations.
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