Introduction

Every career decision is made under conditions of incomplete information. Organizations cannot predict future economic conditions or the long-term consequences of the decisions they make today. Likewise, procurement professionals cannot know how demand for their experience, skills, or ways of working will evolve over time. Both organizations and individuals therefore rely on the best information available at any given moment.

Much of that information describes developments that have already become visible. Salary surveys, employment statistics, and job vacancy data primarily reflect decisions made by organizations months earlier.

A more forward-looking perspective comes from observing developments that emerge earlier in the economic cycle: changes in business activity, investment priorities, regulatory developments, technological advances, and organizational decisions regarding the design of work. These indicators make it possible to assess how procurement roles, responsibilities, and the market value of professional experience may evolve before those changes become fully visible in labour market statistics.

Big Fish defines a market signal as an observable phenomenon supported by reliable data or consistent market observations that increases the probability of a lasting change in the value of procurement roles, professional capabilities, or the way procurement functions are organized. An isolated observation remains simply information. A pattern confirmed through multiple independent observations becomes a basis for interpretation.

The first edition of Big Fish Procurement Career Signals examines three independent developments:

• the growing adoption of Artificial Intelligence in procurement,
• changes in the scope of procurement activities delivered through SSC and GBS organizations,
• the increasing strategic importance of supplier data driven by new European Union regulations.

Each signal is published separately because each operates through a different mechanism and affects a different part of the procurement landscape.

This publication presents the first signal: the changing market value of procurement experience associated with the use of Artificial Intelligence in sourcing, procurement analytics, contract management, procurement operations, and Procurement Excellence.

How Was This Signal Developed?

Big Fish Procurement Career Signals is not based on a single report or a handful of selected publications. Instead, it is built on continuous market monitoring covering labour market data, macroeconomic research, procurement studies, corporate announcements, regulatory developments, and information on investments, emerging technologies, and evolving operating models.

For this edition, the monitoring process covered several thousand reports, publications, press releases, and market records. The collected materials were subsequently evaluated against four criteria:

• relevance and recency,
• data quality,
• independence of the source,
• significance for procurement and the ability to identify a credible mechanism linking the observed phenomenon to procurement roles and career development.

Only observations supported by multiple independent data sources—or consistently reflected in recurring market decisions and organizational changes—were included in this publication.

The sources listed at the end of this report represent the direct evidence supporting the analyses presented here. They should not be interpreted as a complete record of all materials reviewed during the monitoring process.

SIGNAL 01

AI in Procurement: Why the Value of Experience Depends on Process and Decision Impact?

Artificial Intelligence is being adopted faster than organizations are redesigning procurement processes and roles. In Poland, 64% of employees already use AI, while only 38% of companies invested in AI during the past year.[1]

As a result, much of AI adoption is being driven by individual initiative rather than enterprise-wide transformation. Employees increasingly rely on widely available AI tools to analyse information, prepare documents, and reduce the time required to complete repetitive tasks.

Procurement reflects the same imbalance.

Gartner surveyed 101 Chief Procurement Officers regarding their organizations' readiness to redesign procurement processes and roles around AI. Only 36% reported being highly confident in their organization's ability to do so.[2]

At the same time, procurement leaders observe noticeable improvements in individual productivity without seeing equivalent improvements in team or functional performance. Gartner describes this phenomenon as the AI Productivity Paradox.

AI enables individuals to work faster without necessarily improving organizational performance.

The benefits remain largely confined to individual roles until procurement processes themselves are redesigned. Sustainable performance improvements require changes to process design, work allocation, governance, collaboration across functions, and performance measurement.

Simply introducing AI into an existing workflow primarily increases speed and throughput. It does not automatically improve procurement outcomes.

The labour market is already beginning to reward experience that combines Artificial Intelligence with supply chain expertise.

Gartner analysed more than 35 million job advertisements, including nearly 600,000 supply chain vacancies.[3] Between the first quarter of 2023 and the first quarter of 2026, demand for AI-related capabilities within supply chain grew rapidly. Based on Gartner's cumulative figures, Big Fish estimates the average annual growth rate at approximately 70%.*

These figures cover the broader supply chain function rather than procurement alone and therefore do not indicate what proportion of procurement positions currently require AI-related competencies.

The composition of those vacancies is equally significant.

According to Gartner, 58% targeted mid-to-senior professionals, while executive-level positions were also overrepresented, although Gartner did not publish their exact share.

This indicates that organizations are not simply looking for employees who know how to use AI tools. They are looking for professionals capable of embedding AI into complex supply chain processes, evaluating business outcomes, and taking responsibility for how AI-generated insights influence business decisions.

*Big Fish calculation based on Gartner's cumulative growth figures. The underlying dataset covers the entire supply chain function rather than procurement alone. 

How Is AI Changing Procurement?

Artificial Intelligence creates the greatest value in activities involving large volumes of data, documentation, and repetitive analysis.

It can support supplier market intelligence, spend analysis, cost modelling, RFx preparation, bid evaluation, contract analysis, risk assessment, and contract performance monitoring.

Category Management

AI expands both the breadth of information available to category managers and the speed at which it can be analysed.

It can support market intelligence, supplier mapping, cost-driver analysis, scenario development, and strategic option evaluation.

Technology, however, does not determine the direction of the analysis. Business objectives do.

Depending on the strategic importance of a category, procurement may focus on Total Cost of Ownership (TCO), Purchase Price Variance (PPV), cash flow, quality, lead times, supply continuity, risk, regulatory compliance, innovation, or sustainability objectives.

More data creates value only when it helps answer the strategic questions that matter for a particular category and improves the KPIs used to measure its performance.

Procurement Analytics

AI shortens the path from raw data to actionable insight.

Activities such as spend classification, data consolidation, anomaly detection, and preparation of initial analyses can increasingly be completed with significantly less manual effort.

As automation expands, the analyst's role changes.

Competitive advantage increasingly comes from deciding which analyses matter, validating the assumptions behind AI-generated outputs, understanding the causes of observed deviations, and translating analytical findings into procurement decisions at the category, contract, or supplier level.

The value of procurement analytics is shifting from producing reports to influencing business decisions.

Contract Management

AI enables organizations to analyse far larger contract portfolios, compare contractual provisions against internal standards, identify deviations, and continuously monitor contractual obligations.

As these capabilities mature, demand will grow for professionals capable of defining contractual standards, establishing acceptable risk thresholds, developing preferred contractual language, and designing escalation rules.

Contract management is therefore becoming increasingly integrated with category management, supplier relationship management, compliance, and benefit realization rather than remaining a standalone administrative activity.

Procurement Excellence

Procurement Excellence is likely to experience the most profound transformation.

Rather than focusing primarily on technology implementation, Procurement Excellence is becoming responsible for redesigning how procurement operates.

This includes selecting the right AI use cases, defining data requirements, establishing operating standards, integrating AI into existing systems, determining which outputs can be automated, defining governance for human oversight, and measuring business outcomes.

Experience gained in Procurement Excellence increasingly reflects the ability to redesign the procurement function itself—not simply to implement new technology.

Procurement Operations

Within procurement operations, AI will increasingly automate routine transactional work, including request classification, workflow routing, document verification, response preparation, and exception detection.

Operational procurement professionals will spend less time processing standard transactions and more time resolving exceptions, collaborating with suppliers and internal stakeholders, identifying root causes, and coordinating activities across multiple business functions.

As a result, the value of operational procurement roles will increasingly depend on managing complexity rather than processing volume.

AI Does Not Replace Procurement's Role in Decision-Making

The impact of AI also depends on procurement's position within the business decision-making process.

When procurement participates in defining business needs, AI can support scenario modelling, cost analysis, market assessment, and sourcing recommendations before purchasing decisions are made.

When procurement becomes involved only during supplier selection, the same technologies primarily improve the execution of an already established process.

Technology expands procurement's capabilities, but it does not compensate for limited involvement in strategic decision-making.

The earlier procurement contributes to business decisions, the greater the value AI can create.

What Kind of Experience Will Have the Greatest Market Value?

Using widely available AI tools will soon become a standard part of office work.

Experience limited to generating text, preparing summaries, or producing simple analyses will increasingly lose its ability to differentiate candidates in the labour market.

Far greater value will be attached to implementing a specific AI application within a procurement process.

Such initiatives may involve reducing the time required for supplier market analysis, increasing spend visibility, improving classification accuracy, automating manual activities, accelerating contract review, or identifying supplier risks at an earlier stage.

The value of these projects does not come from the technology itself. It comes from the measurable relationship between the technology deployed, the process redesigned, and the business outcome achieved.

The rarest—and therefore most valuable—experience will involve transforming the way an entire procurement team or function operates.

Delivering this kind of transformation requires combining procurement expertise with an understanding of data, business processes, technology, governance, organizational responsibilities, and user behaviour.

It also requires coordinating changes across IT, Finance, Legal, and business functions.

Organizations will increasingly need professionals capable of leading this transformation from selecting the right AI use case through implementation to measuring business outcomes.

When describing AI-related experience, procurement professionals should use the same structure they would apply to any other procurement project:

• the initial situation,
• the business objective,
• the process involved,
• the data used,
• the actions taken,
• the performance measurement approach,
• and the business results achieved.

Simply naming the AI tool provides very little information about the level of professional competence.

Far more meaningful is the scale of the business problem addressed, the level of decision-making responsibility, and the measurable impact on category or procurement KPIs.

The same perspective should be applied when evaluating future career opportunities.

There is a significant difference between joining a role that merely provides access to an existing AI solution and one that involves selecting AI use cases, improving data quality, redesigning procurement processes, and taking responsibility for measurable business outcomes.

Only the latter builds experience that remains valuable regardless of the specific technology, software platform, or employer.

Ultimately, the greatest shift is not from manual work to automation.

It is from producing analysis to designing how analysis shapes business decisions.

Knowledge of AI tools will become increasingly commonplace.

Experience that combines Artificial Intelligence with procurement expertise, meaningful KPIs, and demonstrable process transformation will remain comparatively rare—and therefore substantially more valuable.

Signal                          Assessment

Signal Strength	4 / 5
Confidence Level	Medium–High
Time Horizon	0–24 months
Procurement Areas Affected	Category Management, Procurement Analytics, Contract Management, Procurement Excellence, Procurement Operations
Nature of the Change	Technological, Capability-related, Organizational

The strength of this signal is supported by the convergence of three independent observations:

• the rapid adoption of AI by employees,
• direct evidence from Chief Procurement Officers regarding the redesign of procurement functions,
• and the growing number of supply chain positions requiring AI-related capabilities.

The confidence level is assessed as medium to high.

Current research consistently points to the direction of change. However, the full implications for procurement job structures, compensation, and the number of procurement roles remain only partially documented.

In addition, available labour market data covers the broader supply chain function rather than procurement exclusively. Consequently, its implications for procurement should be interpreted with appropriate caution.

Methodology

Big Fish Procurement Career Signals is developed through a two-stage analytical process.

The first stage consists of continuous monitoring of macroeconomic developments, labour market research, industry publications, regulatory changes, corporate announcements, and observable changes in the way procurement functions are organized.

The second stage focuses on validating selected observations and evaluating the mechanisms through which they may influence procurement organizations and the market value of professional experience.

Our analytical approach combines the logic of Michael Spence's signalling theory with the principles of cautious inference advocated by Nassim Nicholas Taleb.

Observable business decisions and market developments often reveal the direction of structural change before it becomes visible in employment statistics. At the same time, meaningful interpretation requires recognising information noise, measurement limitations, and the risks associated with simply extrapolating existing trends.

Analytical Framework

Every potential market signal passes through five stages of evaluation.

1. Data Validation

Each source is assessed for publication date, market coverage, sector, occupational group, research methodology, and data quality.

We distinguish between information describing the current market situation, evidence of ongoing change, and forward-looking projections.

Wherever possible, analyses are based on primary rather than secondary sources, and indicators developed using different methodologies are not compared directly.

2. Observation Assessment

An isolated data point describes a situation at a specific place and time.

An observation becomes a market signal only when it is confirmed by multiple independent sources, remains consistent over time, reflects recurring business decisions, and is supported by a credible mechanism explaining its impact on procurement.

This approach reduces the risk of presenting temporary fluctuations as structural market trends.

3. Mechanism Identification

We evaluate how the observed phenomenon may influence business organizations.

The assessment considers its potential impact on costs, productivity, operational risk, business continuity, process design, allocation of responsibilities, and decision quality.

Without a clearly identifiable business mechanism, market information alone cannot support conclusions regarding procurement functions or procurement careers.

4. Procurement Impact Assessment

The impact on procurement is assessed through the actual scope of work rather than job titles.

The evaluation considers decision-making authority, responsibility for categories, suppliers, or business processes, access to data, influence on cost and risk, and collaboration with other business functions.

Job titles provide only supporting context.

The true value of a procurement role is determined by the responsibilities it carries and the business consequences of its decisions.

5. Career Impact Assessment

Finally, we assess whether a particular type of experience:

• is becoming increasingly sought after by employers,
• is evolving into a market standard,
• retains value primarily within selected industries, categories, or operating models,
• remains relevant but is becoming less effective in differentiating candidates,
• or is likely to lose importance because of automation, centralization, or regulatory developments.

Our conclusions regarding career value are derived primarily from the analysis of procurement organizations and business processes.

Labour market data serves as additional validation whenever sufficiently robust evidence is available.

Signal Assessment Framework

Each Procurement Career Signal receives three independent assessments.

Signal Strength

Signal Strength measures the potential impact of the observed phenomenon on procurement and on the market value of professional experience.

The scale ranges from 1 to 5:

• 1 indicates an effect limited to a narrow group of organizations or procurement roles.
• 3 indicates a clearly observable impact within selected areas of procurement.
• 5 represents a structural market shift affecting a substantial part of the procurement function and labour market.

Confidence Level

Confidence Level reflects the quality, consistency, recency, and breadth of the available evidence supporting the signal.

Time Horizon

Time Horizon indicates the period during which the observed development is expected to begin influencing procurement processes, job responsibilities, and recruitment criteria.

A signal may have high potential impact while maintaining only a moderate level of confidence.

For example, a regulatory change may significantly reshape procurement within a specific category even though its long-term effects on employment remain difficult to quantify.

About Big Fish

For more than 20 years, Big Fish has specialized exclusively in the procurement profession.

We deliver executive search and specialist recruitment projects, assess procurement capabilities, and support organizations in developing high-performing procurement functions.

By combining insights gained from working with procurement leaders and organizations with continuous market analysis, we identify developments that may influence procurement roles, capability requirements, and the long-term value of professional experience.

Big Fish Procurement Career Signals was created to provide procurement professionals and business leaders with a structured framework for understanding these emerging market developments before they become established market realities.

Sources

The references below constitute the direct evidence supporting the analyses presented in this publication.

They have been selected from a substantially broader body of monitored materials on the basis of methodological quality, recency, and relevance to the mechanisms discussed in this report.

[1] Randstad, Workmonitor 2026 — Polish data on employee adoption of Artificial Intelligence and corporate investment in AI technologies.

[2] Gartner, Gartner Survey Shows Just 36% of Chief Procurement Officers Are Very Confident in Ability to Redesign Function for AI, 19 May 2026 — survey of 101 Chief Procurement Officers regarding the redesign of procurement roles and processes around Artificial Intelligence.

[3] Gartner, Gartner Says There Is an Outsized Need for AI Talent in Supply Chain, 15 June 2026 — analysis of more than 35 million job advertisements, including nearly 600,000 supply chain vacancies. This source is used as broader labour market context rather than procurement-specific evidence.

The complete monitoring database also includes materials used to identify, compare, and reject potential observations during the analytical process.

These materials are intentionally not published as part of the bibliography because they did not constitute direct evidence for the conclusions presented in this report.