The Use of AI in Quote-to-Cash

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Apttus, the quote-to-cash software vendor, recently partnered with Harvard Business Review on a white paper titled “Artificial Intelligence for Maximizing Revenue.” Apttus uses AI for B2B contract and revenue operations. Their white paper notes that “innovation is dramatically opening new avenues for enterprises” and leading executives are adopting AI.

“Apttus creates cloud-based software solutions for quote-to-cash,” Apttus’ Elliott Yama told me. He said that the company started off in contract management. Now they’re focused on automating mission-critical processes for businesses.

Their website states: “End-to-end artificial-intelligence-driven Quote-to-Cash eliminates manual tasks, keeps all stakeholders informed, collapses cycle times, lowers selling costs, and, above all, provides you with a comprehensive revenue view of the customer that is actionable in real time.”

Yama explained, “The best business outcomes require not just automating but also modifying people’s behavior, influencing behavior. So we have a set of behavior applications. Think of business promotions, incentives, rebates.”

Yama said that Apttus’s technology can provide intelligence throughout the quote-to-cash process in order to deliver the maximum business outcomes. According to Yama, AI is commercially beneficial because “it’s superhuman in its ability to take in data and understand, reason, and of course learn from that data.”

Yama also said that current research indicates that “a human supported by a smart model outperforms the smart model alone and it outperforms the human alone.”

Given the rapid rate of technological advancement, I asked Dr. Ramesh Srinivasan whether he can foresee a scenario whereby large corporate incumbents can afford advanced tools and bootstrapped startups lack the ability to meaningfully compete. Dr. Srinivasan is an Associate Professor at UCLA who often writes about the ethical implications of technology. He answered, “More often than not in the digital world, if you are able to have the funds and the capital to intelligently invest in your business, you tend to do better than those that don’t.”

He said that this dynamic manifests itself most strikingly in the search engine optimization market and would also be evident in an internet landscape without net neutrality. “Visibility is what counts. Without visibility you don’t even exist, you know via algorithmic outputs and systems,” he said, after noting that many users don’t scroll past the third result on Google.

He continued, “I’m not necessarily as concerned at AI systems that are simply trying to compute pattern recognition around more quantitative kinds of data outputs, like sales cycles and so on.

I asked Ken Bodnar, a consultant, developer and researcher in emergent technologies such as blockchain, whether business leaders should have any security concerns about third-party platforms and tools that have access to their business information. He said businesspeople should read the fine print and look for data usage limiter clauses. Bodnar explained, “Companies should bowdlerize their data instead of feeding it raw. Also they should use platforms whereby you can do your own feature engineering to further protect your data. 

Feature engineering can be as simple as categorizing your data, or doing something known as a map/reduce feature before sending it off to third parties. The huge risk is that if the fine print allows the third party to use the data, that the knowledge contained in that raw data could be mined and refined and sold to your competitors without you realizing it. The second big risk is a breach of the raw data of the third party, like the Equifax breach.”

On the issue of data security, Yama said, “Cloud-based security standards, such as SOC1 and SOC2, are well established and support vast application of commerce, contracting, telecommunication, and information exchange. In fact, some argue that cloud-based security is equal to, if not more robust than that of its on-premise cousin. 

And, emerging technologies, such as block-chain, offer even greater promise for data security via distributed ledgers. One area worth highlighting, however, is the sovereignty of individuals’ data, especially as it relates to AI, where standards and controls over personal data are nascent and data generators / capturers are evolving at hyper speed.”

Within the industry at large, there is also a growing controversy about what constitutes AI and what constitutes an overhyped form of data analytics. Increased amounts of data and increased computer processing power have expanded the reach of traditional data analysis techniques, such as classification and regression. Algorithms can crunch the numbers in ways that humans can’t, but it’s unclear when this ability can rightly be considered an early form of artificial intelligence.

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