Deploy AI Agents in Procurement: A Roadmap to Success
13 March 2026 - ID G00846918 - 12 min read
By Meghna Joshi, Don Scheibenreif
Machine buyers — machine customers that buy on behalf of organizations — are an agentic AI capability set to transform internal supply chains and procurement, reducing procurement cycles from months to seconds. This note gives CIOs and other executive leaders a roadmap for deploying machine buyers with practical illustrations from NEC.
Insights at a Glance
Agentic AI and multiagent systems are set to transform enterprise work, but their implementation path and ownership remains unclear. CIOs have an opportunity to lead, yet early AI projects can quickly shape their reputation — for better or worse.
One promising, low-risk use case is the machine buyer: an AI agent that purchases goods or services for a company. Interest is rising among chief procurement officers (CPOs) and chief supply chain officers (CSCOs), and recent advances mean machine buyers are moving from concept to reality.
CIOs should leverage machine buyers as a pilot to showcase AI leadership, test organizational readiness, and lay the groundwork for broader transformation.
Gartner’s analysis offers a roadmap for piloting and scaling machine buyers, supported by insights and an example from NEC, a Japanese IT and electronics firm that has already deployed these agents in procurement and reduced their negotiation times to under one minute (from manual times of between three and 48 hours).
Impact Brief
Sixty-seven percent of CIOs expect their organizations to increase investments in agents. Yet, the newness of the technology means most organizations aren’t sure of where to invest and how to implement agents to get value from investments. This is an opportunity for CIOs to demonstrate AI leadership, test organizational readiness for agentic AI and plan for broader transformation.
CIOs who lead agentic AI deployment in a way that prepares the organization for agents will open up a range of potential leadership roles for themselves as their organizations transform. CIOs who fail to do so will find themselves dealing with the fallout of other leaders deploying agents — regulatory, financial and reputational risks for the enterprise and their own reputation.
Actions
- Pilot: Treat pilots as transformation tests, not technology POCs. Prioritize low-risk use cases and task teams with uncovering workflow impediments, not just deploying machine buyers.
- Build: Align your build, blend and buy approach to the business criticality of use cases and expected return on investment, steering clear of market hype.
- Tune: Focus on building context, not correction. Create formal mechanisms to uncover and codify tactic, contextual knowledge that humans use for decision making.
- Deploy: Gain trust by focusing on the benefits that partners will get from machine buyers, not just usability training or feature walkthroughs, and provide easy access to human negotiators.
- Scale: Involve employees in redefining their roles and redirecting their time to value-added work beyond agents’ abilities.
How to Execute
Pilot: How Do We Design Pilots to Show the Value and Feasibility of Machine Buyers?
CIOs need to treat agentic AI pilots as business transformation exercises, not technology POCs. Prioritize workflows where agents can measurably improve outcome attainment and empower pilot teams to surface organizational flaws that impede agents at scale.
Pilots are typically a proof of concept but in the case of machine buyers, they serve a much more critical purpose — testing organizational readiness for agents. Initial pilots can expose human and technology readiness gaps that need to be plugged for the success of agentic AI use cases more broadly.
Case In Point: NEC’s Machine Buyer Evaluation Criteria
When evaluating potential pilot areas for machine buyers, NEC leaders wanted to balance the potential business value, technical feasibility and practical impact of deployment on employee sentiment. Leaders from their R&D, procurement and technology teams created a “sweet spot” for agent deployment based on:
- The complexity of the workflow, which had to be dynamic to justify agent use over standard automation
- Context data availability that would allow agents to make decisions independently without constant human oversight
- The ability to define clear KPIs that could measure the benefits of agent deployment
- Impact on employees, ensuring that initially selected workflows weren’t integral to employees’ sense of value and self-worth.
For instance, they found that inventory monitoring was too standardized and best left to traditional automation. Contract negotiations were too dynamic and high-risk for full agent autonomy. Negotiation of individual delivery times fell in the “sweet spot” — dynamic, time-consuming but still low-risk enough to delegate to agents.
To maximize the value of the pilot, CIOs should establish two layers of cross-functional leadership: a steering council of CxOs and senior leaders, and a cross-functional team that brings together IT, procurement, business operations, and data governance experts. This team should be explicitly empowered not only to execute the pilot, but to actively probe for process weaknesses, data quality gaps, and limitations in the machine buyer’s logic. CIOs should ensure the team makes it a priority to document flaws, exceptions, and unexpected outcomes as they arise, instead of just focusing on implementing the scope of the pilot.
By equipping the team with the authority and tools to surface and address shortcomings, CIOs ensure that the pilot delivers actionable insights, strengthens organizational readiness, and lays a solid foundation for broader adoption of agentic AI.
Build: Which Machine Buyer Capabilities Can We Buy Off the Shelf, and Where Will We Need to Invest in Custom Development?
Align your build, blend and buy approach to expected return on investment. CIOs must evaluate which aspects of agentic AI are strategic differentiators worth building internally, versus standardized capabilities that can be sourced from vendors or partners.
Agentic AI solutions, including machine buyers, should be evaluated with a clear focus on expected ROI and alignment to business priorities. While the promise of autonomous agents is substantial, CIOs must ensure investments are justified by tangible business outcomes and not driven by vendor hype.
For machine buyers specifically, the market is not yet mature enough to support a full “buy” approach. Most off-the-shelf solutions today are best suited for automating routine substeps in procurement, such as supplier selection, inventory replenishment, or basic contract management, which can typically be integrated with existing procurement systems with relative ease.
However, CIOs should be cautious about sourcing advanced capabilities, like autonomous negotiation and complex decision making, off the shelf at this stage. Not only is the market for enterprise-ready solutions nascent, but buying could inadvertently expose sensitive insights into vendor relationships and financials, creating governance and security concerns. It could also lock CIOs into relationships with vendors who, in coming months, begin to lag behind competitor capabilities. For these mission-critical processes, CIOs should plan to build or blend to ensure both capability and control.
To build a strong understanding of the vendor landscape, refer to our latest market insights:
Tune: How Do We Ensure Machine Buyers Make the Right Decisions?
Focus on building context, not correction. Create formal mechanisms to uncover and codify tactic, contextual knowledge that humans use for decision making.
Ensuring machine buyers make the right decisions requires a systematic approach to capturing and codifying the contextual knowledge that human experts use every day. This includes tacit negotiation strategies, supplier personas, risk thresholds, purchasing policies and escalation paths that underpin effective procurement.
CIOs must invest in knowledge management frameworks that enable business experts to formalize and capture their decision logic. They must dedicate IT and data teams to work with business leaders and subject matter experts to design data models that reflect contextual knowledge, and ensure business leaders task their teams with articulating and documenting the tacit knowledge they use to make decisions. Establish clear governance protocols for reviewing agent decisions, and regularly update the decision logic to reflect changing market conditions, compliance requirements, and business strategies. This codified knowledge becomes the foundation for decision making, enabling agents to operate with greater autonomy while maintaining alignment with organizational objectives.
Deploy: How Do We Ensure Transparency and Trust With Suppliers as We Deploy Machine Buyers?
A critical acceptance barrier for machine buyers is understanding why they are being introduced, not just how they work. Gain trust by focusing on the benefits that suppliers will get from machine buyers, rather than just providing usability training or feature walkthroughs.
Deploying machine buyers in procurement requires careful management of skepticism or confusion in supplier relationships about why agents are being introduced, what data they will consume and what value they deliver. CIOs must frame agents in terms of mutual gains and shared outcomes, such as faster transaction cycles, increased accuracy and that lead to business benefits like cost optimization, to help partners see agentic AI as a tool for joint success, not just an internal efficiency play.
To support this mindset shift, organizations should still provide transparent, explainable agent interfaces and clear escalation paths to human negotiators. However, these efforts must be anchored in a broader change management strategy that addresses partner concerns, demonstrates early wins, and solicits feedback on the partner experience. By focusing on the “why” and making supplier value central to the deployment narrative, CIOs can overcome resistance and foster stronger, more collaborative relationships as machine buyers become an integral part of procurement.
— Satoshi Morinaga, AI leader, NEC“The machine buyers aren’t just beneficial for the company that is deploying them. Machine buyers are always available and respond instantly, unlike human negotiators who can take hours or days. Our supplier partners benefit from this — they don’t have to wait for a human, which speeds up their own processes. Especially in global contexts with different timezones, that’s a significant incentive for them.”
Scale: How Do We Adapt Roles, Workflows, and Tools for Machine Buyers at Scale?
As machine buyers scale, workflow and role redesign become central to realizing their value. Involve employees in shaping agent orchestration tools, redefining their roles and redirecting their time to value-added work beyond agents’ abilities.
Scaling machine buyers from pilot projects to enterprisewide adoption demands a fundamental rethink of workflows and employee roles (see What Kind of Work Still Matters in the Age of AI?). Leaders across business areas, technology and HR must actively engage teams in redesigning processes to ensure that agents and humans work together effectively:
- Explicitly invite employees to experiment with new workflows and redefine their responsibilities.
- Create avenues for employees to share and document their experiments.
- Schedule periodic reviews among teams and senior leaders to share lessons and use inputs to reshape workflows and role descriptions.
As this transformation unfolds, the CIO’s role shifts from directly leading pilot initiatives to enabling business and HR leaders to drive change. Over time, the CIO may also take on more enterprise transformation and innovation responsibilities. For instance, at NEC, the CIO role has evolved into a chief AI transformation officer who accelerates AI adoption within NEC and for client use. He follows a “client zero” approach to pilot AI products in internal functions, documenting necessary people and process changes for scalable use. These pilot lessons also inform the plan for an AI-ready technology stack (see Figure 4).
Success Measures
Agentic deployment
- Automation rate: Percentage of transactions or purchase orders fully handled by the machine buyer without human intervention.
- Process ownership ratio: Ratio of procurement processes owned by staff versus those managed by the machine buyer.
- Error correction time: Reduction in time procurement staff spend correcting errors or handling exceptions.
Efficiency gains
- Reduction in procurement costs: Track overall savings achieved through automated negotiations, dynamic pricing, and reduced manual errors.
- Decrease in unplanned spend: Measure reduction in off-contract or unauthorized purchases.
- Cycle time reduction: Monitor the time taken from purchase requisition to order fulfillment. For example, NEC reduced their negotiation time from three to 48 hours to under one minute.
- Order accuracy: Track error rates in orders placed by the machine buyer compared to manual processes.
- Return or dispute rates: Measure the frequency of returns or disputes due to incorrect orders.
Supplier performance
- Supplier compliance: Track adherence to preferred supplier lists and contract terms.
- Supplier diversity: Measure the expansion and diversification of the supplier base enabled by automated sourcing.
- Supply assurance: Measure reduction in delivery delays, stockouts or inventory wastage.
Workforce productivity
- Procurement staff reallocation: Number or percentage of procurement staff hours redirected from transactional tasks to strategic activities.
- Transaction volume per employee: Number of purchase orders or transactions processed per procurement staff member before and after automation.
- Employee satisfaction: Improvement in procurement team satisfaction scores, reflecting reduced repetitive workload and increased focus on value-added work.
Contributors
Bill Ryan, Brian Menke, Melanie Alexander, Meghan O’Doherty, Sneha Ayyar, Kabeh Vaziri
2025 Gartner AI Survey — CIO and Technology Leader View. This survey was conducted to understand CIOs’ and technology leaders’ views on enterprises’ status in their AI journey, investment and technology priorities for 2025. It also captured their sentiments toward their enterprises’ response to and effectiveness in dealing with disruptions. The survey was conducted online in May 2025 among CIOs (n = 224) and other technology leaders (n = 282). The total sample was 506 respondents, with representation from North America (n = 211), Europe (n = 141), Oceania (n = 54), Asia (n = 41), the Middle East (n = 30) and Latin America (n = 29) across all industry sectors. Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.
2025 Gartner Autonomous Business Study Survey. This survey was conducted to understand the current state of autonomous business at enterprises. Responses were gathered online from August through September 2025 among 350 respondents in mid-level positions and above across various industries and regions, including Asia/Pacific (n = 90), Europe (n = 90) and North America (n = 170). Qualifying organizations reported enterprisewide annual revenue of at least $1 billion or equivalent. To qualify, respondents had to be involved in and have a good understanding of the AI and automation efforts at their enterprise. Qualifying organizations had been piloting or had already deployed at least one of the following: AI agents, intelligent automation or autonomous technologies. Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.
Disclaimer: The organization (or organizations) profiled in this research is (or are) provided for illustrative purposes only, and does (or do) not constitute an exhaustive list of examples in this field nor an endorsement by Gartner of the organization or its offerings.