Recently I had the pleasure of opening a panel discussion on "Governance and Risk Management for AI and ML Models" that featured three very experienced, senior executives from leading financial institutions: The discussion centered on the challenges wrought by the rapidly growing use of artificial intelligence (AI) across their enterprises. They shared some very interesting anecdotes and offered insights that speak to a core theme in any enterprise AI journey: Balancing the pressure to maximize value by rapidly deploying AI models with the necessity to limit the organization's risk exposure. For those who may not have the time to view the entire discussion – available here – I'd like to summarize a few highlights that I think are especially important. Before I start – If you're thinking that the panelists' observations are only relevant for financial services companies, hold on a bit. They made it clear from the outset of the discussion that much if not most of their risk management practices are driven by internal controls and business practices rather than by government or industry regulations.

Making matters worse, improvements in infrastructure and technology have made telecoms largely comparable in terms of coverage, connection speeds, and service pricing, meaning companies must transform their businesses if they hope to compete. For many global telecoms, shoring up market share under today's pressures while also future-proofing operations means having to invest in AI. The telecom industry is expected to invest $36.7 billion annually in AI software, hardware, and services by 2025, according to Tractica. Through its ability to parse large data sets in a contextual manner, provide requested information or analysis, and trigger actions, AI can help telecoms cut costs and streamline by digitizing their operations. In practice, this means leveraging the increasingly vast gold mine of data generated by customers that passes through wireless networks -- the amount of data that moves through AT&T's wireless network has increased 470,000% since 2007, for example.

Making matters worse, improvements in infrastructure and technology have made telecoms largely comparable in terms of coverage, connection speeds, and service pricing, meaning companies must transform their businesses if they hope to compete. For many global telecoms, shoring up market share under today's pressures while also future-proofing operations means having to invest in AI. The telecom industry is expected to invest $36.7 billion annually in AI software, hardware, and services by 2025, according to Tractica. Through its ability to parse large data sets in a contextual manner, provide requested information or analysis, and trigger actions, AI can help telecoms cut costs and streamline by digitizing their operations. In practice, this means leveraging the increasingly vast gold mine of data generated by customers that passes through wireless networks -- the amount of data that moves through AT&T's wireless network has increased 470,000% since 2007, for example.

By Tanvir Gill Artificial intelligence or AI is taking the financial world by storm. AI is the field of computer sciences that helps machines/computers to replicate intelligent tasks, normally done by humans, like image processing, game playing, decision making etc. Every aspect of finance is getting disrupted by the arrival of AI. Traditionally, finance is dominated by experts who drive lending, investing and trading decisions. In all these areas, startups and established companies are trying to replace the experts with machines using AI.