Traditionally, procurement has been focused on cost savings and has been measured against year-on-year savings impact. While this remains an important objective, the current growth rate in procurement savings is not sustainable. Moreover, rapidly changing consumption habits and complex supply chains are creating new expectations on procurement to bring in much-needed agility and risk mitigation techniques to these processes.1
Today’s typical procurement function hence sets strategy priorities beyond pure cost reduction, and also looks at new products/market development and better managing risk. Procurement leaders focus on specific levers such as consolidating spend, reducing total life cycle / ownership costs, and increasing competition. These are further augmented by initiatives targeting specification improvement, demand management, as well as, increasing the level of supplier collaboration.
Written by Dr. Adam Flesch
Managing Director, Prosperitree Consulting
July 2020
There is a huge role digital technologies and advanced analytics can play in driving the performance of the procurement function and generating new levers of value creation. Soon, automation technologies, supported by robust machine learning algorithms, will redefine activities across the entire source-to-settle spectrum. These technologies will enable organizations to perform transactional activities at much greater speed, lower cost, and higher accuracy.
Once the function has mastered this, the next step would be to generate predictive analytics and to support category managers and their internal partners in deepening their understanding of buyers’ behaviours, while further leveraging their sourcing strategies with precise, real-time monitoring capabilities and forward-looking insights on markets, suppliers and the internal demand.2
Whether it is automating low-value tasks or providing rich insights, AI can have a transformational impact on procurement and supply chain operations. AI offers huge potential to enable smarter procurement, which can create efficiencies and enable better decision making, offering a real competitive advantage to those that adopt.
Today’s Procurement reality
The way business between buyers and suppliers works is not terribly different from how it was 20 years ago. Certainly, communication is faster and easier, and we are not sending purchase orders through the post or queuing at the fax machine to send specification documents to suppliers. Operational activities have been automated to a large extent. However, the core procurement processes, such as purchase-to-pay (P2P) or sourcing management, have not fundamentally changed. The P2P process is often partially automated – but is still executed as it has been for generations, and even sourcing is not too different. An electronic tendering process still almost always follows the tendering ‘rules’ laid down years ago in the days of pen and paper.3
52% of the procurement organization’s time is spent on transactional activities. Though much of this time is dedicated to reconciliation and compliance activities, key metrics on maverick spend, cycle time, and accuracy rates still fall short of expectations. To make matters worse, most of these activities are reactive and are unable to address issues until the damage is done.
As procurement has traditionally been so slow to adapt to rapidly changing technologies, the potential for improvement is tremendous.
Deloitte’s Global Chief Procurement Officer 2018 survey found that only 2%of CPOs say they have ‘fully deployed AI or cognitive technology for use in procurement’. However, 66% of CPOs agree that ‘a key leadership trait of procurement leaders is leading digital and analytical transformation’.4
Procurement leaders globally recognise the power of better data-driven insights: 50% of respondents indicated in Deloitte’s survey that they are proactively leveraging intelligent and advanced analytics for cost optimisation, and 48% named process efficiency improvement. There is also a strong focus on using analytics for management reporting and procurement operations improvement, reinforcing the procurement efficiency objectives.
84% of procurement organisations believe digital transformation will fundamentally change the way their services are delivered in the next three to five years, according to a report from consultancy Oliver Wyman on digital procurement.5
CPOs’ appetite for AI in procurement and supply chain is increasing. They are already using AI in some procurement and supply chain processes, and this trend is set to continue, with 55% of business leaders revealing that they plan to adopt AI in a procurement context over the next 24 months.6
Basic machine learning technology is already used by some procurement applications in areas such as spend analytics and contract analytics. This is mostly limited to automating the processes of collecting, cleaning, classifying and analysing expenditure data in an organization — to identify savings or paths to greater efficiency.
In reality, the use of AI in procurement and supply chain management (P&SCM) is still in the early stages. But again, even chatbots and guided buying are not transformational innovations in any real sense; they may have efficiency benefits, but do not fundamentally change the way procurement works. To do that, AI will need to start affecting the more strategic issues that underpin P&SCM – such as supply continuity, risk management, collaborative value creation and sourcing innovation.3
The Intelligent Procurement of the Future
The Procurement of the Future will generate substantial business value from the parallel application of:
Automation – Building intelligent and adaptive business capabilities to automate activities and improve results through AI-powered Robotic Process Automation
- Automate all manual, tactical, repeatable processes, limiting human intervention to decisions that have not been preconfigured. Resources can therefore shift to more strategic and value-creating activities.
- Extract and analyze huge volumes of structured and unstructured data to improve self-learning and cognitive automation, empowering sourcing professionals to evaluate, negotiate with, and appraise suppliers much more effectively.
- Reduce the time, effort, and costs consumed by source-to-contract activities, using cognitive analytics to remove bias and dramatically speed up review cycles and decision making.
Predictive analytics – Predict outcomes by combining multivariate data sets, modelling techniques, and complex algorithms
- Improve decision making based on information-driven planning and analysis, including “clean sheeting” or “should-cost” calculations.
- Simulate demand based on more criteria and using more sources of information.
- Enable agile supply chain operations based on transactional and IoT sensory data that monitors internal and external conditions for continuous monitoring and timely actions.
- Develop standardized dashboards based on complex algorithms for closer evaluation of supplier performance management and capabilities.
Automation is freeing up the procurement role to focus on more strategic tasks. Due to the business processes AI automates, highly repetitive and rule-based tasks that take days, weeks, or months to complete will be actionable in minutes or even milliseconds. This is why one of the biggest potentials of AI seen by procurement functions is the automation of invoice processing. Also, automated approval of proposed purchases is considered to be a key application area. Employees who would normally focus on processing invoices will be able to focus on creating more strategic plans to improve business process efficiency.6 According to McKinsey, 60% of source-to-pay processes have the potential to be fully or largely automated, yielding up to 3.5% of overall spend savings.7
Robust data and predictive analytics enable smarter decision making. Because of the data explosion of the last few years, the procurement function can benefit from the amount of data being collected. However, this function must make sense of this data to deliver insights and strategic decision making.
ATKearney suggests that Advanced analytics, including should-cost analysis, can reduce cost of component products or services by up to 40%.8
Other benefits like predictive analysis of price trends to get the most competitive price in the market will also increase supplier competition to meet the needs of a new, smarter organization. In fact, the deployment of data-driven analytics will also nurture an ecosystem where procurement is the driving force behind supplier collaboration to further enhance innovative capabilities.
As the procurement function continues to transform, internal customers will require better user experiences. This is why 54% of business leaders – according to Forrester’s survey – want better user interfaces to make it easier for employees to purchase the tools, products, or services they need in an efficient and safe manner by using AI-powered chatbots and digital assistants. Additionally, cross-checking the price of proposed purchases against the marketplace drastically reduces the employee’s time doing menial tasks.6
Real-time capabilities and Big Data will drive analysis to dramatically improve performance management. Delivered as a service, accurate scorecards and dashboards will identify patterns, best practices, and strengths and weaknesses across holistic measures. Visibility, transparency, and management around suppliers and external workforce will minimize exposure to legal risk.
IoT and Big Data capabilities, with advanced analytics engines, will drive holistic and futuristic insights to enable procurement to become cognitive, identify breaking points early, and unmask opportunities to innovate.1
Six areas where AI will transform Procurement
Puchase-to-pay
The basic purchase to pay (P2P) processes for managing and recording the procurement transaction are already automated to some extent in many organisations. Artificial intelligence will take this to a new level. For instance, it will help decision-making by staff who place orders through a catalogue-type tool – ‘guided buying’ will become standard. In order management, autonomous systems will self-trigger requisition and PO processing based on data triggers such as stock information, thereby limiting human intervention for approvals.3
AI will also support in areas such as automated invoice processing, helping systems learn how to handle non-standard invoices or spot potentially fraudulent examples. Invoices will be automatically matched to purchase orders using machine learning algorithms with 99% accuracy rates and faster cycle times. It can also help with the identification and warning of early or late payment, missed discount opportunity, excess ordering or inventory on hand.
Procurement policies will be embedded in the tools. For example, Travel spending will be captured where it happens, even when it happens outside of existing management systems; policy is seamlessly applied to direct bookings to better support traveling employees and utilize negotiated rates.
Sourcing
AI could support sourcing as it is done today, as well as driving new ways of choosing suppliers and awarding contracts. Under conventional ‘request for proposal’ (RFP) or ‘invitation to tender’ processes, AI-driven tools will suggest which suppliers should be invited to participate in the exercise. Using evidence from past events, AI will help design the process and documents and support the evaluation, which will be heavily automated. AI will check that suppliers have the accreditations or certifications they need to do the work.3
Eventually, AI could enable new processes and solutions that move beyond the RFP altogether. AI-powered auction and negotiation bots will become more advanced and we expect to see bots on both sides of the buyer-seller relationship. AI-powered tools will help rapidly collect, present and even analyse commodity, market, and supply intelligence to inform market strategies. They will enable the procurement function to react much faster regarding buying decisions, reducing time from weeks to days, as well as applying statistical analysis to recommend the number of suppliers to invite, or the day of the week for best pricing. With AI, procurement teams can proactively predict procurement actions using historic spend trends and asset information, like refresh and predictive maintenance rates.
Also, it will accelerate the SOW creation process by leveraging machine learning to analyse a user’s previous actions and apply them to the new service request.
Contract Management
AI is likely to play a role at every stage of the contract management process. It is already being used to search the existing contracts in organisations for terms and conditions that may carry a risk for the buyer. Natural language processing has enabled procurement to mine contracts for valuable data through a method called text parsing. Contract management software can utilize parsing algorithms to efficiently scan and interpret even large amounts of contracts for critical information. Taking this even further, optical character recognition (OCR) is an AI-enabled approach that interprets and identifies text automatically from any images, including photos of previously un-digitized scanned contracts.9
Self-learning algorithms will extract and interpret critical information from documents to enforce terms, and improve compliance and negotiation capabilities. Automated contract reviews require human review of only non-standard clauses or terms.
AI will also support the drawing up of new contracts, suggesting appropriate clauses and conditions. Leveraging the structured and unstructured contract data, AI will support to perform should-cost analysis to factor in margin considerations and to strengthen negotiation capabilities. Then AI will identify the information needed from the supplier to manage the contract and performance, and ensure this is captured, recorded and reported.3
In order for organizations to start creating significant value from AI, they must deploy the technology on top of the right platform, data, and processes. Best-in-class approach here would require a centralized and digital contract platform that provides full visibility across the organization, and proactively keeps the team informed with automated alerts and notifications. Digitization of contracts will increase quality, collaboration, transparency, and speed during the negotiating process with suppliers. Terms, key dates, expectations and performance metrics will be clearly stated, communicated, accessible and verifiable.
Risk Management
One of the most interesting aspects of AI in procurement is its ability to process risk factors. Unlike humans, AI has the capacity to analyse billions of data points in seconds to present solutions with accurately assessed risks. Understanding what is relevant for each organisation, putting the right information in front of the right people as quickly as possible, and ensuring that data is turned into actionable intelligence are all aspects of risk management where AI will play a central role.
AI is fantastic at finding patterns and correlations, knowing the ‘normal’ pattern of something means that you can also spot anomalies. For example, it can help to spot the risk of bid-rigging – a form of cartel – in response to a procurement tender. In the UK, the Competition and Markets Authority has worked with a data analysis firm to produce a ‘screening for cartels’ tool, which is free to download as open-source software. It looks at the number and pattern of bidders, pricing patterns, the origins of tender documents and signs that ‘low endeavour’ has been used in creating the bid. Based on that review, it generates a suspicion score for the tender proposals.
Anomaly detection can also be used to identify unexpected changes in purchase prices for a commodity or from a specific supplier. With in-memory computing, running analytics on the transactions platform can help identify fraud and errors in real time, expose policy and compliance issues, immediately notify the team and give instant suggestions, saving significant time and losses to the organization.
In the future, this capability could also include10:
- Analysing weather patterns to predict the best delivery methods and route
- Identifying new sources when existing ones are disrupted or costs abruptly rise due to unforeseeable political or catastrophic events
- Verifying supplier reliability as stability is threatened
- Assessing the rate of raw material depletion to predict the future viability of manufacturing in a specific area
- Monitoring local and global politics to report potential cost increases
In the area of supplier management, enhancing supplier risk management capabilities with risk-sensing technologies and supplier dashboards will enable predictive and proactive risk identification and real-time supplier performance management. Simulating go-to-market conditions based on access to supplier and other external information, taking more than just the historical and transactional data into consideration, can help organizations identify key risk or opportunity areas very early – in the product design and development phase – and formulate their strategy accordingly.
AI can also help with minimizing delivery risk by identifying different stakeholder groups and behaviours to create better engagement throughout the procurement process.
Spend analysis / Category management
Aggregating spend data is the primary challenge for most procurement operations. With millions of items scheduled out in short-term and long-term orders, pulling all the data together to classify and organize long-tail spend is nearly impossible without automation. AI is faster and more accurate than the most experienced teams, freeing humans to do more important and fulfilling work.
Across a large organization, accurate spend classification results in more efficient ordering and returns significant spend reduction. For example, AI is capable of analysing hundreds of thousands of items purchased by a typical company to identify and compare different unit rates charged by suppliers for the same products. Also, detecting similar orders or deals can help identify consolidation opportunities for future sourcing. Once opportunities are identified, optimal agreements can be made with key suppliers to maximize savings and increase the percentage of contracted spend.10
Using machine learning for vendor matching will connect supplier data contained in invoices and purchase orders to a vendor hierarchy. For example, connecting different local subsidiaries of a freight and logistics company to one international supplier.
Through predictive analytics, an organization can operate a modern spend analysis platform with the ability to project spend trends over time. It can start to more accurately forecast demand and predict future costs, which allow it to zero in on the true opportunities of today and tomorrow, not the opportunities of last quarter or last year. Also, it can re-run the projections and analysis on a quarterly or monthly basis, identify when the forecasted trends change, and when it might need to take a harder look at a category, supplier, or geography before a significant price change or disruption happens. This puts it light years ahead of where it was before when it only acquired annual insight into a category or supplier, by which time it might be too late to do anything about an emerged situation.
Through the application of trend analysis and comparisons to similar categories, the organization can predict future volume, spend, and identify the best opportunities for cost savings and avoidance before spend in these products gets out of control. If the organization keeps seeing spend across suppliers for a new type of service, it can be a leading indicate of a change coming down the pipe as the organization transitions to new product lines that need to be supported by new services. And if it sees new products, that could indicate changes in production line technology or back office equipment that will soon be standardized and allow Procurement to get involved early, even before the organizational departments realize that they need Procurement’s support.7
Success requires identifying those categories where demand is significant, static or increasing, and the price differential (which can be expected or locked in) is enough per unit to make a strategic sourcing event worthwhile. For that, AI can help to analyse component and raw material requirements across product lines to identify those components and raw materials that are used across products and that are increasing in both demand and price. By identifying those components, the organization can proactively identify good sourcing opportunities that would otherwise be missed. Even if the savings is low today, preventing future increases is a profitable cost avoidance.
If costs are increasing sharply, sourcing today will avoid cost increases tomorrow. But sometimes you cannot forecast based on (market) price alone, especially for custom manufactured products. That is where deep market intelligence on raw material, energy, and labour costs is required. With this information, an organization can build not only should-cost models for today, but with deep cost forecasting at a category level, it can build should-cost models for tomorrow and identify those categories and products it should be sourcing versus what it should have been sourcing last year.7
Another important impact area is demand management, which focuses on reducing the total number of units needed. AI can help identify categories where spend is increasing due to unjustified or avoidable increase in demand. Rigorously keeping demand to the required minimum helps the business to save on input costs, keeps inventory costs down and minimizes write-off losses due to modified business requirements. But it is not just about demand reduction, it is also about identifying demand growth early to gain volume leverage during negotiations or (e-)auctions. If the business can project that a new product is going to go from 100K units this year to 1M units next year, going to the market with a tender for 1M units will get the company a lot more interest, and potential savings opportunities, than going to the market with a tender for 100K or 110K.
Innovation
The concept of procurement as a key driver for innovation is relatively new, but many firms are realising that supply markets are potentially huge sources of ideas and competitive advantage, often more important or productive than internal innovation or product research. Finding suppliers that can provide real value is not always easy, particularly as many are likely to be smaller and younger firms. So, AI-powered search capability will help organisations define and then identify the suppliers that are most likely to bring these advantages.3
Procurement specialists will bring expertise on trends and future capabilities that can be sourced into early stages of product development and innovation discussions, and will embed with teams and be true partners. As a result, greater emphasis will be placed on quantifying value beyond cost savings.
The road to Smart Procurement
Over 45% of procurement leaders believe lack of integration and poor-quality data are key barriers to the effective application of digital technology in procurement. Most of the time, only 20% of procurement data is utilized to improve operations. The remaining 80% is considered to be “dark data” – unused data that may prove to be valuable, such as unstructured transactional data, contract metadata, general ledger information, data stored across disconnected ERPs or other databases, or external, unstructured data about products and suppliers available over the internet.9
Another key challenge is that the amount of data available is growing across different source systems and there is an increasing challenge to connect heterogeneous sources of data. Today’s Procurement organization may need to bring together spend data from across a number of enterprise planning systems (ERPs), purchase-to-pay solutions or other finance-related software. Each source system may contain only some relevant data points, and there is a need to connect disparate spend data into one hierarchy.
Building the capabilities of data collection prepares the organization for the AI challenges ahead, even if you are not planning on implementing any sort of machine-assisted methods in your operations anytime soon. Collecting and storing data in advance is also good practice as it prepares your data sets and processes ahead of the AI implementation.
Starting with fixing the P2P processes hence is a smart plan as this uneven landscape is hampered by poorly governed data acquisition, accumulation, and refinement. Robotic process automation (RPA) will be best applied in this context.3
Once the adequate data management practices and data sets are secured, building out the source-to-contract (S2C) systems in important category areas is the next step. Artificial Intelligence will help analyse the rich information environment that procurement teams have built up over years of strategic category management and where value can be extracted most effectively.
Finally, building advanced, effective supplier risk management systems that truly help evaluate suppliers, markets and risk (and not just supplier scorecards) can elevate Procurement’s capability to optimize sourcing within a risk-reward framework.
Skills are also a vital piece of the puzzle and are often overlooked. Application leaders who lack the right analytical and data science skills will struggle to understand the opportunities and limitations of AI in procurement.
As Magnus Bergfors, Research Director at Gartner, explains: “Organizations that don’t have dedicated procurement analysts will need to create this role, but it may be hard to justify a full-time position in smaller organizations. In that case, it is important to set aside time and budget to train citizen data scientists inside the procurement and sourcing teams.”11
Another critical role of a procurement professional is to facilitate and be part of the collaboration between human and the machine. This means communicating with and educating stakeholders, end-users and suppliers on the benefits of AI; demonstrating how it will benefit them and training them in its usage. As procurement team members’ routine tasks are automated, their personal work will become more strategic. AI enables them to focus on strategic planning, collaboration, and relationship-building – tasks that machines are not yet capable of doing as well as they are.9
Once the right platform and skills are in place, an organization is ready to experiment with pilot programs the use cases for machine learning and automation.
Digital technologies, including the use of AI, have a critical role to play in helping the Procurement area meet its objectives and deliver substantial business impact. Applying digital technologies to the procurement function will enable strategic sourcing to become more predictive, transactional procurement to become more automated, supplier management to become more proactive, and procurement operations to become more intelligent. The ingredients of a value-centred, progressive new Procurement function.
- https://my.ariba.com/rs/407-PHQ-501/images/SAP-Ariba-Whitepaper-Procurement-2025.pdf?mkt_tok=eyJpIjoiT1RBNFpUZGhZakE1TTJJMCIsInQiOiJMTHF1dVhQdDRxZWl0Y3VFQURpb0hxOEVpbk9acG5xQ25CTFJwXC9hWEtOeVYrRWM5amtGSVpRZndPNXZFTkh3cGFaRFljdHBrUUNcL1ZrNWxDMGJXNzA3ZFNBczJEY2ZnYXgwWVJpMHB5VmhPZ24rMFBYdXhjeDZ1Skp2SWFjczRNIn0%3D
- https://www.procurementleaders.com/blog/guest/artificial-intelligence-time-for-procurement-to-reap-the-rewards-of-automation-682269#.XvRnC2gzaGh
- https://www.raconteur.net/wp-content/uploads/2018/09/AI_Revolution_in_Procurement_HICX-1.pdf
- https://www2.deloitte.com/content/dam/Deloitte/at/Documents/strategy-operations/deloitte-global-cpo-survey-2018.pdf
- https://www.oliverwyman.com/content/dam/oliver-wyman/europe/france/fr/Publications/Digital_Procurement.pdf
- https://info.ivalua.com/forrester-enabling-smarter-procurement-2018-typ?submissionGuid=92c144c4-410b-4d41-a3b7-e196215be6cc
- https://www.mckinsey.com/business-functions/operations/our-insights/a-road-map-for-digitizing-source-to-pay
- https://www.de.kearney.com/aerospace-defense/article?/a/should-cost-review-to-improve-affordability
- https://sievo.com/resources/ai-in-procurement
- https://www.forbes.com/sites/forbestechcouncil/2019/09/23/ai-in-procurement-where-were-headed/#7e39f33b5fb1
- https://www.gartner.com/smarterwithgartner/prepare-for-the-impact-of-ai-on-procurement/
About the Author
Dr. Adam Flesch is the Managing Director of Prosperitree Consulting, and a former McKinsey Jr. Partner. He has been in the management consulting space for more than 15 years serving Clients across a wide range of industries on strategy, risk management, and operation effectiveness topics. He is a strong advocate for the wider use of AI in business, from decision-making to front-line automation.
About Prosperitree
Prosperitree Consulting is a boutique strategy consultancy focusing on AI-centered business solutions and traditional management consulting. It helps businesses build future-proof strategies and establish smarter day-to-day decision-making routines while turning their data assets into actionable insights and enhancing their respective capabilities.
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