Introduction

Every career decision is made under conditions of incomplete information. Organizations do not know what the future economic environment will look like or what consequences today's decisions will have. Professionals do not know how demand for their experience, skills, and ways of working will evolve. Both employers and employees make decisions based on the information available at a given point in time.

Some of this information describes developments that have already become established. Data on salaries, employment levels, or the number of job openings reflect decisions companies made many months earlier.

The direction of change can, however, be assessed using information that emerges at an earlier stage: shifts in economic activity, investment patterns, new regulations, technological advances, and organizational decisions regarding the way work is structured. These indicators make it possible to evaluate how procurement roles, responsibilities, and the market value of specific professional experience may evolve.

Big Fish defines a market signal as an observable phenomenon supported by data or consistent market observations that increases the probability of a lasting change in the value of roles, competencies, or the way the procurement function is organized. A single observation remains just information. It is the convergence of several independent observations that provides the basis for meaningful interpretation.

AI in Procurement: The Value of Experience Depends on Its Impact on the Process and the Decision

Artificial intelligence is spreading across the workplace faster than organizations are redesigning their processes and job structures. In Poland, 64% of employees already use AI solutions, while only 38% of companies invested in AI during the past year. As a result, many AI applications are driven by employees themselves, who use publicly available tools to analyze information, prepare documents, and reduce the time spent on repetitive tasks.

Procurement shows a similar imbalance between the scale of AI adoption and the transformation of the procurement function itself. Gartner surveyed 101 Chief Procurement Officers regarding their organizations' readiness to redesign procurement processes and roles. Only 36% expressed a high level of confidence in their organization's preparedness. At the same time, procurement leaders report higher individual productivity without a comparable improvement in team or function-wide performance.

Gartner describes this phenomenon as the AI productivity paradox. AI enables individual employees to complete more work in less time, but the benefits remain confined to the individual role. The procurement function improves only when processes themselves are redesigned through changes in work allocation, elimination or simplification of process steps, revised collaboration models, and updated performance metrics. Simply adding AI to existing workflows primarily increases the speed and volume of activities.

The labor market is already beginning to reflect growing demand for professionals who combine AI expertise with supply chain knowledge. Gartner analyzed more than 35 million job postings, including nearly 600,000 related to supply chain. Covering the period from Q1 2023 to Q1 2026, the analysis shows rapid growth in supply chain positions requiring AI-related competencies. Based on Gartner's cumulative growth figures, the average annual growth rate is approximately 70%. However, the data covers the entire supply chain domain and does not indicate what proportion these roles represent within the overall labor market.

The composition of these vacancies provides additional insight. Demand is concentrated primarily on more experienced professionals. Mid-to-senior positions represented 58% of AI-related supply chain vacancies, while director-level positions were also overrepresented, although Gartner did not disclose their exact share. This suggests increasing demand for professionals capable of embedding AI into complex supply chain processes, evaluating its outcomes, and taking responsibility for how AI-generated insights are used.

How Is AI Changing Procurement?

AI offers the greatest potential in areas involving large volumes of data, documents, and repetitive analysis. It can support supplier market intelligence, spend analysis, cost modeling, RFx preparation, bid comparison, contract analysis, risk assessment, and monitoring supplier performance against agreed conditions.

In Category Management, AI expands the range of information that can be incorporated into category strategies while significantly reducing analysis time. It can support market analysis, supplier mapping, cost driver identification, scenario development, and option evaluation. However, the direction of analysis must always be determined by the category's objectives and performance metrics. Depending on the strategic importance of the category, these may include TCO, cost development, PPV, cash flow, quality, lead time, supply continuity, risk, regulatory compliance, innovation, or sustainability objectives. Data only creates value when it answers questions derived from the category strategy and supports the relevant KPIs.

In Procurement Analytics, AI shortens the path from data acquisition to hypothesis generation. Activities such as spend classification, data integration, anomaly detection, and preparation of initial analytical scenarios can be completed much faster. Consequently, the analyst's role shifts toward understanding data sources, evaluating model assumptions, interpreting deviations, and recommending actions at the category, contract, or supplier level.

In Contract Management, AI enables the analysis of much larger contract portfolios, comparison against predefined standards, identification of deviations, and monitoring contractual obligations. As this area develops, greater value will be placed on professionals capable of defining contract standards, acceptable risk levels, alternative clauses, and escalation rules. Contract data can then be integrated with category management, supplier relationship management, compliance, and benefits tracking.

The most significant transformation is likely to occur within Procurement Excellence. Here, organizations determine where AI has a justified business case, define data requirements, standardize processes, and integrate AI solutions with existing systems. They must also determine which outputs can be automated, which require human approval, how exceptions should be managed, and which KPIs will measure implementation success. Experience gained in Procurement Excellence therefore extends beyond technology projects to the design of the procurement operating model itself.

In Procurement Operations, AI will increasingly automate routine activities such as request classification, workflow routing, document validation, response preparation, and discrepancy detection. Human work will increasingly focus on exceptions requiring judgment, supplier or stakeholder interaction, root cause analysis, and cross-functional coordination. The value of operational roles will increasingly depend on managing exceptions effectively and continuously improving processes.

The impact of AI also depends on procurement's position within the decision-making process. When procurement is involved in defining business requirements, AI can support option analysis, cost modeling, market assessment, and strategic recommendations. Where procurement becomes involved only during supplier selection, the same technologies primarily improve execution efficiency rather than influencing decisions. AI expands what procurement can achieve within a role but cannot replace procurement's involvement in earlier strategic decisions.

What Experience Will Have the Greatest Market Value?

Using publicly available AI tools will soon become a standard office skill. Experience limited to generating texts, summaries, or simple analyses will become progressively less valuable as a differentiating factor.

Greater value will come from implementing AI within a specific procurement process. Examples include reducing supplier market analysis time, increasing spend visibility, improving data classification quality, reducing manual work, accelerating contract reviews, or identifying risks earlier. The importance of such projects depends on the connection between the technology used, the process improvement achieved, and the KPI used to measure success.

The rarest and most valuable experience will involve changing the way an entire procurement team or function operates. This requires combining procurement expertise with knowledge of data, processes, systems, governance, and user behavior. It also involves aligning new ways of working with IT, Finance, Legal, and business stakeholders. Organizations will increasingly seek professionals capable of leading this transformation from selecting AI use cases to measuring business outcomes.

When describing AI-related experience, professionals should present it in the same structured way as any procurement project: the initial situation, objectives, process scope, data used, actions taken, governance mechanisms, and measurable results. The name of the AI tool itself says little about the individual's competence. Far more important are the scale of the business problem, the level of responsibility, and the project's measurable impact on category or procurement KPIs.

The same criteria should be applied when evaluating future career opportunities. There is an important distinction between roles that merely provide access to AI tools and roles responsible for selecting AI use cases, improving data quality, redesigning processes, and delivering measurable business outcomes. The latter develops experience that remains valuable regardless of changes in technology, systems, or employers.

The most significant shift will be the transition from creating analyses to designing how those analyses are used. Knowledge of AI tools will become increasingly widespread. What will remain scarce—and therefore valuable—is experience combining AI with procurement expertise, relevant business KPIs, and demonstrated improvements in procurement processes or decision-making.

Signal Assessment

Criterion	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 change	Technological, competency-based, and organizational

The strength of this signal is supported by the convergence of three independent observations: the rapid adoption of AI among employees, direct evidence from CPOs regarding procurement transformation, and the growing number of supply chain positions requiring AI-related competencies.

The confidence level remains medium to high because existing research consistently points in the same direction. However, the long-term impact on job structures, compensation levels, and the overall number of procurement roles is still insufficiently documented. Furthermore, job posting data covers the broader supply chain domain, so its direct applicability to procurement should be interpreted with appropriate caution.

Methodology

Big Fish Procurement Career Signals combines economic data, industry intelligence, regulatory developments, labour market research, corporate decisions, and observations on how procurement functions are organised.

The analysis draws on the logic of Michael Spence’s signalling theory and the principles of cautious inference reflected in the work of Nassim Nicholas Taleb. Observable decisions and developments may indicate the direction of change before it becomes clearly visible in employment data. Their interpretation, however, requires consideration of information noise, measurement limitations, and the risk of simply extrapolating past trends.

Each potential signal goes through five stages of analysis.

1. Data verification

We verify the source, publication date, period covered, market, sector, job group, and data collection method. We distinguish between information describing the current state and data relating to change or forecasts. Wherever possible, we refer back to primary sources and avoid directly comparing indicators developed using different methodologies.

2. Assessment of the observation

A single figure describes a situation at a specific place and point in time. We consider an observation to be a signal when it is confirmed by other sources or persists over time, is consistent with decisions made by companies, and can be linked to a clear mechanism through which it may affect procurement.

Our approach reduces the risk of presenting a temporary change as a lasting trend.

3. Identification of the mechanism

We determine how the observed development may affect companies. Among other factors, we analyse its implications for cost, productivity, risk, business continuity, process organisation, allocation of responsibility, and decision quality. Without identifying the underlying mechanism, market information does not provide a sufficient basis for drawing conclusions about the procurement function.

4. Assessment of the impact on procurement

We assess impact through the actual scope of work: the type of decisions made, responsibility for a category, supplier, or process, access to data, influence on cost and risk, and cooperation with other organisational functions. Job title is of secondary importance. The value of a role is determined by its scope of responsibility and the consequences of the decisions made.

5. Assessment of career relevance

We examine whether a given type of experience:

• is increasingly required by employers;
• is becoming a basic standard;
• remains relevant mainly in selected sectors, categories, or operating models;
• is still needed but increasingly differentiates candidates less strongly;
• may lose scope as a result of automation, centralisation, or regulatory change.

Career conclusions are based on the preceding analysis of the company and the procurement function. Labour market data serves as additional confirmation where available.

Signal Assessment

Each signal receives three separate assessments.

Signal strength indicates the scale of its potential impact on procurement and on the value of professional experience.

Signal strength is rated on a scale from 1 to 5. A rating of 1 indicates an impact limited to a narrow group of roles or organisations. A rating of 3 indicates a clear impact in selected areas of procurement. A rating of 5 indicates a broad structural change affecting a significant part of the procurement function and the labour market.

Confidence level reflects the quality, recency, number, and consistency of the available sources.

Impact horizon indicates the period within which the change may begin to affect processes, role scope, and recruitment criteria.

A high signal strength may coexist with a medium confidence level. A regulation may significantly change work in a particular category even though its impact on employment remains difficult to measure.

About Big Fish

For more than 20 years, Big Fish has specialised exclusively in the procurement market. We deliver executive search and specialist recruitment projects, assess competencies, and support organisations in developing their procurement functions. We combine knowledge gained from working with organisations and professionals with systematic market analysis.

Big Fish Procurement Career Signals was created to structure and interpret developments that may influence role scope, demand for competencies, and the value of professional experience in procurement.

Sources

SIGNAL 01 — AI in Procurement

• Randstad, Workmonitor 2026 — data for Poland on employees’ use of AI and corporate investment in this technology.
• Gartner, Gartner Survey Shows Just 36% of Chief Procurement Officers Are Very Confident in Ability to Redesign Function for AI, 19 May 2026 — a survey of 101 CPOs concerning the redesign of procurement roles and processes around AI.
• Gartner, Gartner Says There Is an Outsized Need for AI Talent in Supply Chain, 15 June 2026 — an analysis of more than 35 million job postings, including nearly 600,000 supply chain roles. The source was used as broader labour market context; the data does not relate exclusively to procurement.