Advertising cookies may be set through our site by our advertising partners and help us to deliver personalized marketing content. If you do not allow these cookies, you will experience less relevant advertising.
Artificial intelligence (AI) is redefining the human experience. As businesses digitally transform, they increasingly look to AI to perform cognitive functions that have traditionally required human intelligence such as pattern recognition, learning and problem solving. With AI, businesses are able to streamline processes, increase efficiencies and make faster, data-driven decisions.
Amazon has built its own business on applying AI to improve customer outcomes, optimise selection and revolutionise logistics. AI-enabled innovations like Alexa voice service and Amazon Web Services (AWS) cloud computing technologies are already changing everyday lives and accelerating growth for businesses.
A business’s procurement function is often one of the last to digitally transform. Procurement is rich with data, and that means AI and machine learning (ML) can be especially impactful in helping businesses save money, manage supplier risk and meet customer demand with speed and agility. But some companies, especially those that are smaller or tech averse, believe AI-powered procurement is out of reach. Amazon Business wants to change that. As AWS does for cloud computing, Amazon Business democratises AI-powered procurement for any business or public sector organisation, applying Amazon’s machine-learning technology to a purchasing solution.
As you develop your own technology roadmap, consider the following five ways AI and ML are helping businesses save time and money and learn how Amazon Business is applying AI to help reshape the procurement process.
Analysts are tracking organisations’ use of AI and ML in procurement and are noting the transformative effects. In the past, to evaluate procurement data, companies would need to invest in experts such as business intelligence engineers, data scientists and IT professionals to create complex analytic models from the data. Today, ML technologies can analyse large amounts of data quickly and provide strategic insights that management can use to define and direct strategy.
For example, Amazon Business Spend Visibility is a machine-learning analytics tool available to qualifying Business Prime members. Powered by Amazon QuickSight, Spend Visibility analyses purchasing data to learn and track an organisation’s buying patterns. The tool provides data visualisations managers can use to make budgeting decisions, locate compliance issues and optimise savings. Procurement managers don’t need to be an expert to take complex data and use it to inform strategy.
In their 2020 survey of procurement professionals, WBR Insights found that 76 percent of organisations believe their ability to develop strategic insights based on AI-powered analytics is either “advanced” or “above average.”1
The survey, sponsored by Amazon Business, also found that in addition to developing insights, AI and ML give procurement professionals more time to execute on the opportunities they identify. Collecting, analysing and drawing insights from data becomes less labour intensive. Sixty-two percent of organizations were able to use AI or ML to reallocate time they would have spent on manual processes related to strategic-level planning.1
Procurement professionals are turning over routine and repetitive processes to AI and using ML to optimise spending on both strategically sourced and non-strategic, tail spend supplies.
For strategically-sourced items, organisations employ AI to automate competitive bidding. AI can execute repetitive bids, which helps to increase sourcing speed and secure better pricing.1
For commonly purchased, non-strategic or tail spend supplies, ML can automatically identify preferred products or similar items in Amazon Business’s online store, helping purchasing managers find cost-effective alternatives.
As AI and ML reduce the time required to identify, purchase and reorder supplies, procurement professionals can spend more time on strategic sourcing or other high-value activities.
Many organisations restrict what, where and from whom employees are allowed to buy. Ensuring compliance to company spending policies can be time consuming and is another area where intelligent technologies are simplifying procurement.
AI and ML can assist with supplier evaluation and selection by analysing and flagging potential disruptions in the supply chain and automatically recognizing compliance issues among potential suppliers, minimizing disruptions to operations and saving time.
AI can also assist with guardrails and restrictions around what employees are allowed to buy. Guided Buying allows managers to turn procurement policies into easy-to-follow visual signposts built directly into the shopping experience. The tool steers employees toward the products or suppliers chosen by management and away from restricted suppliers or product categories. Notifications appear on relevant product detail pages and in search results that can include custom messages or show alternative product options when available.
Guided Buying makes it easier for employees to stay compliant and lets management spend less time evaluating purchases and enforcing rules.
AI and ML have long been used in personal shopping to provide a curated buying experience through personalised recommendations, product discoverability and merchandising. Those same capabilities are now transforming business purchasing.
Machine learning gathers data from an individual’s onsite behavior and order history—and on Amazon Business, applies industry-specific parameters and guidelines set by management—to present curated search results and relevant recommendations. And because the system is continually learning, the buyer’s experience improves over time, driving process efficiencies and employee satisfaction.
Unmanaged tail spend can represent as much as 20 percent of total procurement spending and it continues to be especially challenging for large organisations. For tail spend purchases, employees are more likely to go rogue, buying outside of approved channels and creating a blind spot for management.2
As businesses begin to experience the benefits of personalisation at scale, there's a reduction in rogue spending and an increase in control over tail spend. Eighty-two percent of business buyers say they want the same experience when they buy for work as when they’re buying for themselves.3 Traditional procurement systems are less likely to offer a user-friendly experience. When management shifts tail spend purchasing to approved channels that provide an AI-powered, personalised user experience, employees are more likely to purchase from the approved channel where management can control and track spending.
A recent sponsored report from IDC references Amazon Business customers for whom a better user experience is reducing rogue spend and leading to greater visibility and control for management. Multinational food and beverage company Mondelēz reduced lead time from 25 days to 4 days for tail spend items due to better service, easier processes and easier discovery of better-priced products.
According to WBR Insights, eighty-two percent of organizations plan to adopt a cognitive procurement model within the next twelve months or already have one in place.1 As AI and ML play a more significant role in procurement, we can expect to see greater productivity, agility and faster growth.
Though only forty-five percent of organisations have seen a positive ROI from their investments into AI and ML, procurement professionals are optimistic. Sixty-six percent of respondents to the survey by WBR Insights believe ROI and value creation will be the area of their strategy most impacted by cognitive procurement capabilities.1
AI-powered procurement presents an opportunity for procurement leaders to elevate their position and drive strategic value. For organisations of any size, investment in intelligent analytics and automation technology can be as simple as getting starting with Amazon Business.
To learn more, download “Cognitive Procurement and the Implementation of AI and ML.”
References
1. “Cognitive Procurement and the Implementation of AI and ML.” WBR Insights. 2020.
2. “Overcoming Procurement Challenges in the Age of AI and Digital Transformation.” IDC. 2020.
3. “State of the Connected Customer,” Second Edition. Salesforce.
Select Your Cookie Preferences
We use cookies and similar tools to enhance your experience, to provide our services and to understand how customers use our services so we can make improvements and display ads. Approved third parties also use these tools in connection with our display of ads.
Cookies are defined as browser cookies, pixels, local storage, and other similar technologies that read or write information on a user's device. They are collectively referred to on this site as "cookies”. All cookies must be categorised as falling within one of the types below.
Essential cookies are necessary to provide our site and services and cannot be deactivated. They are usually set in response to your actions on the site, such as closing notifications or filling in forms.
Performance cookies tell us how customers use our site and services and enable us to make improvements.
Functional cookies enable us to provide helpful features and services. They may be set by us or by third-party providers whose services we have added to our pages. If you do not allow these cookies, then some or all of these services may not function properly.
Advertising cookies may be set through our site by our advertising partners and help us to deliver personalised marketing content. If you do not allow these cookies, you will experience less relevant advertising.
This cookie is used to set language preference to provide seamless transition between amazon sites.
This cookie is used to set user preference to show or hide notification component on a webpage.
This is a cookie flag that is being set after a user opts a preference on cookie consent notification. Values in this flag are used by multiple platforms to control cookies for the user.
The opt-in service lets us set up a system to approve or deny the downloading of Adobe cookies only. It does not provide support for either gathering user consent preferences, nor it is a repository for preferences.
This is an AWS Classic Load Balancer Cookie; used to map the session to the instance.
This is an AWS Classic Load Balancer Cookie; used to map the session to the instance.
This cookies is used to trace the actual referrer or source of the visit on Amazon Business site.
This cookie is used to store the reference ids associated with source url.
Unique visitor ID used by Marketing Cloud Solutions. The Alphanumeric string after AMCV_ is updated for each user.
Unique visitor ID used by Marketing Cloud Solutions. The Alphanumeric string after AMCVS_ is updated for each user.
This cookie is set and read by the JavaScript code to determine if cookies are enabled.
Contains information about the current page and entry page to the website.
This cookie lets us know how far a user has scrolled on a webpage and expires after ending the browser session.
It's from analytics plugin to fulfill the requirement of scroll depth, similar to Adobe Launch plugin.
This cookie is being set by Adobe Target JS library to check if user's browser is compatible to store Target cookies in order to show personalized experiences.
This cookie is set by Adobe SiteCatalyst analytics software and is used for measuring the performance of page content using A/B split testing.
This cookies is set by Youtube and used to track the number of views of embedded videos.
Advertising cookies may be set through our site by our advertising partners and help us to deliver personalized marketing content. If you do not allow these cookies, you will experience less relevant advertising.
This cookie associates the surfer ID with relevant audience segments and conversions. Information about the last search click helps determine if a conversion event was the result of a click or a view-through
A third-party cookie created after a user clicks a client's ad, and used to map the current and subsequent clicks with other events on the client's website
This cookie is being set by amazon-adsystem.com for tracking user actions on other websites to provide targeted content to the users.
This is a browser ID cookie set by LinkedIn share Buttons and ad tags.
This cookie is used to display personalized and relevant ads to the users and measure the efficiency of the ad campaign.
The cookie is set by demdex.net. This cookie assigns a unique ID to each visiting user that allows third-party advertisers target that users with relevant ads.
This cookie is set by Youtube and used to track the information of the embedded YouTube videos on a website.
Media Optimizer sets the ev_sync_variable name cookie to record the date when synchronization will be performed. This is an ad exchange specific cookie that synchronizes the Media Optimizer user ID with the partner's ad exchange platform. It is created for new users and sends a synchronization request when it expires. The ev_sync_variable name cookie contains the synchronization date in yyyymmdd format and is set in the eversttech.net domain.
Provided by amazon-adsystem.com for tracking user actions on other websites to provide targeted content.
This cookie is used by Google to display personalized advertisements on Google sites, based on recent searches and previous interactions.
This cookie is set by linkedIn. The purpose of the cookie is to enable LinkedIn functionalities on the page.
Audience Manager sets this cookie to assign a unique ID to a site visitor. The demdex cookie helps Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.
This cookie set by Adobe Audience manager. It stores a data provider name or ID and a UNIX UTC timestamp formatted as pipe-delimted strings. The purpose of the cookie is to record the last time it made a data synchronization call.
This cookie is used to store the language preferences of a user to serve up content in that stored language the next time user visit the website.
This cookie is set by LinkedIn and used for routing.
This cookie is provided by LinkedIn. This cookie is used for tracking embedded service.
ef_id is populated and appended to the landing page URL when the end user clicks the ad and is redirected to an Advertising Cloud server, and it is then passed to the advertiser in the destination URL for the ad or keyword.
Linkedin used this to track visitors on multiple websites, in order to present relevant advertisement based on the visitor's preferences.
LinkedIn uses this cookie to make a probabilistic match of a user's identity outside the Designated Countries
Used by Google DoubleClick, it stores information about how the user uses the website and any other advertisement before visiting the website. This is used to present users with ads that are relevant to them according to the user profile.
This cookie is set by 6sense to track user interactions on our website for the purpose of delivering personalized marketing content. Tracked actions include visit frequency, average time spent on the site, and pages viewed.