Procurement is evolving fast. New technology is reshaping how teams operate, make decisions, and deliver value across organizations. While the pace of change can feel relentless, understanding the shifts – and how to respond – is key to staying ahead.
Setting the Scene: From Automation to Orchestration
In 2025, procurement technology is no longer about marginal gains. We are seeing a move from tools that support delivery to systems that actively drive change and redefine how procurement works:
- Agentic AI is taking on tasks that would traditionally require a human. It learns, adapts, and makes decisions based on outcomes.
- Orchestration technology is streamlining complex workflows, connecting tools and platforms, and improving the user experience.
- Best-in-class applications are outperforming legacy systems in key functional and category areas.
- High-quality data has become a critical enabler. Structured, consolidated data is essential to get real value from AI.
These changes are not just conceptual. They are already impacting how procurement teams are structured, how they operate, and the level of influence they have.
There are DIfferent
Types of AI:
Computer Vision
Face Recognition, Object Detection (e.g., driverless)
Natural Language Processing (NLP)
Chatbots, sentiment analysis
Autonomous AI
Robots, self-driving cars, AI agents
Generative AI
Chat GPT, Co-Pilot – text and image creation
Machine Learning
Predictive analytics, facial recognition
Expert Systems
Primitive chatbots/assistants, diagnostics
Planning
Sequential decision making and adjustments
…And Different Ways of Deploying AI
Co-Pilot
Bespoke AI works alongside a human operator. Assists and augments their capabilities in a specific task, process or activity like search, analysis, guiding or content creation. Removes the need for some structures.
Autopilot
Architect builds systems or processes in which AI leads. Carries out tasks/processes and may make decisions without constant human input. Many applications across connect processes/systems.
Self-Built (Pioneers)
AI capability in-house, team builds custom solutions.
Buy To Build (Followers)
Bought in capability to custom-build/knit together solutions.
Embedded (Settlers)
Companies rely upon supply market products for dev cycle.
Practical Steps for Procurement Leaders
You do not need to overhaul everything at once. There are manageable steps that procurement teams can take now:
1. Familiarize and hypothesize
Start by building a foundational understanding of digital tools, trends and terminology. Education sessions, internal briefings and external speakers can help raise awareness across your team. From there, encourage teams to hypothesize how these tools could support current objectives – draft simple roadmaps that link specific technologies to potential business outcomes.
2. Understand current pain points
Before adopting any solution, map out where the challenges lie. Look closely at areas of repeated inefficiency, high manual effort, poor customer experience or slow response times. These hotspots often reveal where AI, automation, or orchestration could make the biggest difference. Prioritizing tech investment around pain points ensures adoption and relevance.
3. Set up a digital competency center
Every transformation effort needs ownership. Create a small, cross-functional team responsible for driving the digital procurement agenda. This could take the form of a Digital Garage, a Center of Excellence or simply a named lead. Their role should include trend monitoring, vendor engagement, and coordinating pilots and proofs of concept.
4. Best-in-class solutions
Many functional applications now outperform the core modules of large procurement platforms. These best-in-class tools offer deeper capability in areas like sourcing, contract lifecycle management, risk and spend analytics. Mapping these tools to known problem areas helps identify opportunities to accelerate improvement without full system overhauls.
5. Supplier roadmaps
Speak to your current suppliers and platform partners about their roadmap for AI and orchestration. Some will already have embedded capabilities or beta features that align with your future needs. Others may be falling behind. Understanding their direction helps you judge whether to invest further, integrate other tools or consider new partnerships.
6. Orchestration
Orchestration technology links disparate apps, data sources and workflows into a more seamless experience. This enables faster processes, improved data visibility and smoother adoption of best-in-class tools. It also supports more agile service models – an increasingly critical factor as organizations adapt to hybrid delivery models and category-specific needs.
7. Gen AI and Agentic AI
Generative AI can support tasks like document creation, summarization and content generation. Agentic AI goes further, actively performing roles like triaging requests, negotiating with suppliers or interpreting data. Both types offer significant potential – especially when paired with orchestration to handle complexity. Understanding when to support humans and when to replace tasks is essential.
8. Category-level solutions
Some of the most exciting transformation is happening at the category level. AI-native tools built for logistics, facilities management, marketing or professional services are helping procurement teams solve stakeholder-specific problems. These tools are often overlooked in favor of functional platforms, but they can unlock major value in focused areas.
9. Clean and map key data and processes
Poor data is one of the biggest blockers to effective AI adoption. Clean, structured and consistently managed data is essential – not just for automation, but for analysis, decision support and reporting. Similarly, processes must be documented and rationalized. A messy process will remain inefficient, even if AI is added to the mix.
10. Start by solving problems with business impact
AI initiatives must lead to better outcomes for users, stakeholders and the business as a whole. Focus on tools that improve speed, accuracy, experience or insight. Avoid pet projects that demonstrate capability but fail to move the needle. Adoption grows when the benefit is clear, measurable and meaningful.
What Are the Risks?
While the potential benefits of AI in procurement are significant, adopting these technologies is not without its challenges. One of the biggest hurdles is building a strong business case. The costs associated with implementing AI at scale can be substantial, and the financial return is not always clear-cut, particularly when replacing already efficient or low-cost delivery models, such as outsourcing.
There is also a growing concern around digital capability. Recent research suggests that fewer than 10 percent of Chief Procurement Officers currently have the necessary digital skills within their teams. As demand for these skills rises, so too will competition for talent, creating potential bottlenecks in transformation efforts.
AI Risks For Procurement Teams
01 – Business Case
AI can be expensive to deploy at scale, and the financial business case is not always clear-cut if moving from low-cost delivery (e.g., outsourcing).
02 – Functional vs Category
A lot of the noise will be at a function level, but a lot of value will emerge at a category level, with niche solutions solving specific stakeholder challenges.
03 – Clean/Managed Data and Processes
While Gen AI can work from unstructured data, optimizing value from AI will only be achieved where there is data and process governance.
04 – Availability/affordability of digital skills
Recent research revealed that <10% of CPOs had the right level of digital skills. These will be at a premium and concentrate in certain sectors.
05 – Ethics And Policing
Machine/self learning AI models need to be policed to ensure that data submitted is correct, learning process as intended, and outputs accurate.
06 – Market Development Speed – Build VS Buy
Early Adopters/pioneers tend to build. Market development likely to outpace followers who need to blend off-the-shelf with bespoke (e.g., agents).
07 – Possible VS Practical Action
It’s easy to get blinded by what’s possible or 3-5 years away rather than what’s achievable now and solves business problems in an adoptable way.
08 – Risk of Doing Nothing
Clearly, the benefits of AI are sizeable when deployed correctly. Ignoring this will create a cost, quality, and talent issue vs competitors.
AI systems themselves require active oversight. For organizations to realize meaningful value, models must be monitored to ensure that inputs are accurate, learning processes are functioning as intended, and outputs are both relevant and reliable. Without this, AI can amplify errors rather than solve problems.
Finally, inaction presents its own risk. As competitors adopt and embed emerging technologies, they may gain advantages in cost efficiency, quality of delivery, and talent retention. Failing to act could leave organizations on the back foot, struggling to keep pace in a market that is moving quickly.
An opportunity? Threat? Or imperative?
This report explores the digital opportunities poised to revolutionize procurement. Based on interviews and industry surveys, it offers insights into the digital skills required for future success through the lens of a CPO. Dive into the full report to learn more about navigating this digital evolution/revolution.
