AI in strategic sourcing is relatively new.
Whereas e-sourcing tools, on the other hand, have revolutionised procurement over the past two decades. However, these platforms have always had significant gaps.
Some of the biggest challenges lie in the preparation work needed before launching an RFX event.
Procurement teams spend countless hours on supplier onboarding, writing specifications, preparing documentation, and then setting up the tender in the platform.
Similarly, post-bid analysis often requires manual data exports and complex spreadsheet work. This creates bottlenecks that slow down the entire sourcing process. Despite advancements in UI/UX and features of e-sourcing tools, it’s often still necessary to do post-bid analysis in Excel.
Artificial intelligence, particularly LLM capabilities and generative AI, is now addressing both of these longstanding pain points. It’s transforming both the preparation and analysis phases of strategic sourcing. This shift promises to make procurement teams more efficient and effective.
Let’s recap what AI can do in tactical sourcing
AI has already proven its worth in tactical sourcing scenarios.
Several tools on the market can autonomously handle non-complex, non-strategic spend categories. These systems work without constant human oversight.
For supplier discovery:
- AI scans vast databases to identify potential vendors.
- It matches suppliers to specific requirements automatically.
- The technology considers factors like location, capacity, lead times, and certifications.
Autonomous pricing requests have become increasingly sophisticated:
- AI can generate and send RFQs to multiple suppliers simultaneously.
- It understands product specifications and translates them into clear procurement requirements.
The technology excels at comparing detailed offers for one-time project spend:
- AI can distinguish between different Incoterms.
- It evaluates lead times against project schedules.
- The system identifies what services are included in each quotation versus what was requested.
- It recognises gaps or differences between multiple different quotations, especially if they’re in the supplier’s format.
AI then makes recommendations based on this analysis:
- It provides clear reasoning for its suggestions. This might include cost savings, risk factors, or delivery advantages.
- The human-in-the-loop approach ensures procurement professionals verify decisions before awarding business, if the business so wishes.
This setup also creates opportunities for negotiation. Procurement teams can use AI insights to negotiate better terms. Alternatively, some organisations are experimenting with AI-powered negotiation bots.
But this progress with tactical sourcing is just the tip of the iceberg.
We all know that, if done right, strategic sourcing presents far greater opportunities for AI enhancement.
Let’s look at how this is being transformed.
Using AI for supplier onboarding
Getting suppliers to register on e-sourcing platforms has always been frustrating.
Procurement teams spend hours chasing suppliers to complete registration. They field endless questions about technical problems and documentation requirements.
This administrative burden delays sourcing events and frustrates both parties. Suppliers often abandon registration processes because they’re so clunky. They find the requirements unclear or the platform difficult to navigate.
Intelligent agents can transform this experience.
- These systems can guide suppliers through registration automatically.
- They answer questions in real-time using natural language processing. The AI understands supplier queries and provides relevant, accurate responses.
Document verification becomes seamless with AI review.
- The technology can check insurance certificates, financial statements, and compliance documents.
- It identifies missing information and requests corrections automatically.
This reduces the back-and-forth typically required for complete registration. All of this is taken out of the Category Manager’s email inbox, and can be done by an AI agent.
AI can also pre-populate supplier information from public databases.
- Agents can extract company details, certifications, and contact information automatically.
- This reduces data entry requirements for suppliers significantly, thus removing a well-known friction point.
The intelligent agent learns from each interaction. It identifies common registration problems and proactively addresses them. This continuous improvement reduces future onboarding friction.
For procurement teams, this means faster supplier engagement.
Events can launch sooner because suppliers register more quickly. The quality of supplier data improves because AI validates information systematically.
Leveraging LLMs to write an RFP or Scope of Work
Writing comprehensive RFPs and Scopes of Work traditionally consumed entire working days. Procurement professionals would start from blank documents. They’d reference multiple previous examples and industry standards. The process required significant expertise and attention to detail.
Generative AI has revolutionised this process completely. Large Language Models can produce draft RFPs in a few minutes. The key lies in proper training, the right prompting, and context provision.
AI systems need access to previous successful RFPs from your organisation. They learn your preferred language, structure, and requirements. Historical examples teach the AI about your specific industry needs and compliance requirements.
Context is equally important. The AI needs to understand the current procurement requirement. This includes technical specifications, commercial terms, and strategic objectives. The more context you provide, the better the output quality.
The iterative improvement process is particularly powerful. You can chat with the AI about specific sections. You might ask it to make the technical requirements more detailed. Or request simpler language for supplier understanding. Each conversation refines the document further. The AI incorporates your feedback immediately.
This collaborative approach produces better results than traditional drafting methods.
Some e-sourcing platforms now include this capability natively. Users can generate RFPs directly within their procurement tools. Other solutions focus on post-bid analysis but include document generation features.
The time savings are substantial. What once took eight hours now requires one hour of guided AI interaction.
This frees procurement professionals for higher-value strategic activities.
Document quality often improves too. AI ensures consistent language and structure. It doesn’t forget important clauses or standard terms. The risk of human oversight errors decreases significantly.
Post-bid Analysis
Traditional post-RFP analysis required extensive manual work. Procurement teams would export bid data into Excel spreadsheets. They’d then spend hours creating pivot tables and comparison charts. Complex scenarios required multiple spreadsheet versions.
This manual approach introduced errors and consumed valuable time. It also limited the depth of analysis possible. Teams could only explore scenarios they thought to model manually.
Leading e-sourcing platforms now integrate AI-powered analysis tools. These systems can perform sensitivity analysis automatically. They model different award scenarios without manual spreadsheet work.
If-this-then-that analysis becomes straightforward. The AI can model outcomes based on different supplier combinations. It might compare sourcing everything with the lowest bidder versus splitting across multiple suppliers.
Cherry-picking scenarios are particularly valuable. AI can identify the optimal combination of suppliers across different categories. It considers not just price but also risk factors and strategic objectives.
The technology excels at non-price factor analysis. AI can weight different criteria according to your strategic priorities. It might emphasise innovation capability for technology purchases. Or prioritise local suppliers for sustainability goals.
Different weighting scenarios help test decision robustness. The AI can show how results change with different priority weightings. This helps procurement teams understand the sensitivity of their decisions.
Strategic sourcing often involves complex trade-offs. AI can model these relationships systematically. It might balance cost savings against supply risk. Or evaluate innovation potential versus implementation complexity.
The speed of analysis increases dramatically. What once took days of spreadsheet work now happens in minutes.
This enables procurement teams to explore more scenarios and make better-informed decisions, in less time than before.
AI also identifies patterns humans might miss. It can spot correlations between supplier characteristics and performance outcomes, which may influence future sourcing strategies.
What impact will AI in strategic sourcing have?
The transformation of strategic sourcing through AI will deliver eight significant benefits:
- Improved cycle times represent the most immediate impact. Faster supplier onboarding, automated RFP creation, and instant analysis reduce sourcing event duration. What once took months can now happen in weeks.
- Reduced workload for repetitive tasks frees up procurement professionals for more strategic work. AI handles document preparation, data entry, and basic analysis. Teams can focus on supplier relationships and strategic decision-making.
- Human error reduction improves sourcing quality significantly. AI doesn’t make calculation mistakes or erroneous Excel formulas. Document consistency improves across all sourcing events.
- Enhanced supplier experience drives better participation rates. Streamlined onboarding and clearer communication reduce supplier friction. This leads to more competitive bids and better outcomes.
- Removal of manual work-around obstacles eliminates resistance to e-sourcing adoption. Teams no longer avoid these tools due to administrative burden. Usage rates increase across the organisation.
- Increased spend through e-sourcing tools improves overall procurement data quality. More transactions flow through managed processes. This enhances spend visibility and strategic insights.
- Objective measurement of non-price factors becomes more reliable and consistent. AI applies evaluation criteria uniformly across all bids. This reduces bias and improves decision quality.
- Automated compliance management particularly benefits regulated industries. AI can check regulatory requirements automatically. It flags non-compliant bids and ensures documentation completeness.
What does the future hold for AI in strategic sourcing?
These improvements compound over time.
- Better data leads to better insights.
- Improved supplier relationships deliver better outcomes.
- Reduced administrative burden allows focus on strategic value creation.
The procurement function evolves because it can efficiently run more strategic sourcing events effectively. AI handles routine tasks while humans focus on relationship management and strategic decision-making. This transformation continues to position procurement as a true business partner.
Strategic sourcing becomes more accessible to smaller organisations too. Increasingly, the most innovative e-sourcing tools are those that are focused on the mid-market rather than the large enterprises. Without the 6-figure price tag, these tools become accessible for smaller procurement teams.
The future of AI in strategic sourcing lies in a smart human and AI collaboration.
Technology handles data processing and routine analysis. Humans provide strategic direction and relationship management. Together, they achieve better outcomes than either could alone.
