AI in Business: How Automation Could Improve ProcurementEconomy
Artificial intelligence (AI) and machine learning (ML) are making big headlines in the business world; from applications in management to marketing and even recruiting, new technologies are being tested to improve the organization at different levels. Procurement is one strategic business function where AI and ML are predicted to streamline tedious processes, improve the decision-making, and help to manage risks.
More than half of the world's organizations are "are set to make a major investment in AI over the next two years", according to a recent worldwide study of supply chain, procurement, and finance business leaders. The survey conducted by Forrester and the global spend management cloud provider Ivalua, showed that 44% of respondents believe that AI and ML can have the most impact in alerting the enterprise and suppliers to supply chain disruption and help recognize and flag supplier compliance issues according to 39% of the participants. In addition, 37% said that the technologies could quickly identify fraud.
The recognition of AI as a key player in future procurement processes can also be observed in the 2017 Deloitte Global Chief Procurement Officer Survey, where 88% of the participants believed that automation and robotics would influence procurement within five years.
Areas of Influence
- Sourcing Support
AI and ML algorithms can detect the spend patterns and behaviors; they even can run market analysis and provide suggestions for the procurement teams that would otherwise take a substantial amount of time of human labor.
- Supply Management
Through spend analysis, AI and ML can determine expenditure details with above human-level speed and accuracy.
- Risk Management
AI-powered data integration and predictive analysis can provide enterprises with unprecedented insights on real-time risk factors that can help the procurement team manage risk in advance. For example, Risksmethods is an AI-driven service "that uses big data monitoring and machine-learning AI to zero in on the real risk to your supply chain".
- Virtual Personal Assistant (VPA) and Cognitive Procurement Advisor (CPA)
Procurement technology innovators are looking into using Natural Language Processing (NLP) and Natural Language Generation (NLG) to develop VPAs and CPAs, an innovation that will bring AI into the strategic reasoning of an enterprise. Through the increased automation of procurement processes, VPAs and CPAs will be able to execute complex tasks. "A procurement VPA can improve the end-user experience of traditional procurement tools and increase spend under management by guiding people to the correct purchasing tool", according to Magnus Bergfors, research director at Gartner, who further explains that a CPA "can provide summaries, recommendations, and advice in everything from supplier assessments and performance management, to risk management and compliance".
SAP Ariba announced the launch of its AI-powered bot "that will allow users of its cloud-based applications to manage key tasks with greater simplicity, efficiency, and speed". The software and information tech company explained that "through leveraging machine learning, the bot will be able to train and learn about a user’s preferences and a company’s policies and procedures and guide actions in line with them to reduce errors and speed processing".
- Contract Management
AI applications not only help companies review, organize, and find contracts faster than humans, but they can also benchmark data to "identify similar contract terms by industry or region and suggest optimal prices to target", according to Marcell Vollmer, Chief Digital Officer for SAP Ariba. AI-driven technologies also alert the enterprise or the supplier when a performance is violating the contract.
Barriers Still Exist
While the future looks promising for AI in procurement, the application of predictive and cognitive analytics is almost "non-existent" currently, according to Deloitte. This 2018 survey showed that "only one-third of procurement leaders are using technologies such as predictive analytics and collaboration networks" and only 33% percent of procurement leaders "believe that their digital procurement strategy will enable them to deliver on their organizational objectives and value".
Data quality, integration, and availability are the most significant factors that make-it or break-it for the adoption of AI in procurement processes, in fact, none of the benefits of AI can be realized without a strong data foundation.
Data has always been a necessary function for any company, but for an organization to make the most out of AI in procurement data should be considered as a strategic asset and any related problems would need to be addressed before investing in AI.
Digital capabilities, or lack of thereof, can also curb any AI project in procurement because leaders "who lack the right analytical and data science skills will struggle to understand the opportunities and limitations of AI in procurement", according to Gartner's Bergfors. "Organizations that do not have dedicated procurement analysts will need to create this role", while smaller companies can opt to train "citizen data scientists inside the procurement and sourcing teams".
A company is ready to successfully test AI and ML capabilities in its procurement processes only when the right platform and skills are available and ready otherwise, organizations will struggle to acquire value from the technologies.