Bid Automation in Public Procurement — How AI Cuts Bid Preparation Time by 60%
The public procurement market in Poland is more than PLN 230 billion a year — and yet most firms that submit bids still work in a manual mode. Hand-browsing the Public Procurement Bulletin, laborious analysis of tender documentation, copying content between document templates — that is the daily reality of bid teams in 2026. Meanwhile firms that have deployed AI-supported automation prepare bids 60% faster and win tenders more often than the competition.
This article shows how bid automation works step by step — from monitoring notices in BZP and TED, through scoring the chance of winning, to generating complete bid documents. We present real figures, a comparison of tools on the market, and a case study of an IT firm that, with AI, cut bid-team working time by more than 100 hours a month.
Why is bidding in public procurement so time-consuming?
Preparing a bid in a public contract is not a matter of “writing an email with a price.” It is a complex, multi-stage process that involves specialists from different parts of the company — salespeople, lawyers, engineers, and accountants. Every stage needs precision, knowledge of the Public Procurement Law, and flawless documentation.
A typical bidding process — step by step
The standard cycle of preparing a bid in a public contract looks like this:
- Tracking notices — daily browsing of BZP, TED, BIP, and the Competitiveness Database for contracts that match the company profile.
- SIWZ/SWZ analysis — a detailed reading of the Specification of Contract Terms, identification of requirements, award criteria, participation conditions, and deadlines.
- Bid / no-bid decision — assessing whether it is worth submitting a bid, given win probability, margin, resources, and risk.
- Document preparation — filling in bid forms, preparing statements, references, cost estimates, schedules, and technical descriptions.
- Verification and submission — completeness check, electronic signatures, submission via the e-Zamówienia platform or miniPortal.
What does this actually cost?
Market data is unambiguous — preparing one bid in a public contract takes 40 to 80 hours of work. For large contracts (above EU thresholds) that time can exceed 120 hours. At an average specialist hourly cost of PLN 150–250, that means PLN 6,000–20,000 spent on every submitted bid.
Worse, the average win rate in public procurement is just 15–25%. That means a firm submitting 10 bids a month statistically wins 1.5–2.5 of them — and the cost of preparing the remaining 7–8 bids is a net loss.
Putting the numbers together: a firm investing PLN 500,000 a year in bidding recovers value from barely a quarter of that amount. The rest is a sunk cost — unless the time and cost of preparing each bid can be cut radically.
Hidden costs of a manual process
Beyond direct labour costs there are hidden costs that rarely appear in management reports, and that over a year can exceed the visible ones:
- Missed opportunities — with manual monitoring the firm often learns about a tender too late, when the submission deadline is too short for a serious bid. Industry research indicates that firms without automatic monitoring lose access to 15–20% of potentially interesting contracts — simply because they did not notice them in time.
- Formal errors — a missing document, a wrong date, a bad file format, or an outdated statement results in rejection. According to Public Procurement Office data, as many as 12% of bids are rejected for formal reasons alone — reasons that automation could eliminate entirely.
- Team burnout — monotonous, repetitive work on bid documentation drives turnover in tender departments. Recruiting and onboarding a new public-procurement specialist costs 3–6 months of productivity and PLN 20,000–40,000 in direct spend.
- No learning — without systematic analysis of wins and losses the firm does not improve its bidding strategy year on year. Every lost bid should feed a predictive model — but in a manual process those data disappear in inboxes and binders.
- Opportunity cost — hours spent on bidding are hours taken from project delivery, product development, or building client relationships. For a firm with a 15% margin on public contracts, every specialist hour redirected from delivery to bidding costs the firm PLN 200–400 in lost revenue.
What can be automated in the bidding process? (Opportunity map)
Not every element of the bidding process is a candidate for automation — but the large majority is. The key is to understand which tasks are repetitive, rule-based, or require processing large volumes of data. That is where AI gives the greatest advantage.
Notice monitoring (BZP, TED, BIP, BK) — alerts instead of manual checking
The first and most obvious area to automate. Instead of browsing four different portals every day (the Public Procurement Bulletin, Tenders Electronic Daily, Public Information Bulletins of individual contracting authorities, and the Competitiveness Database), the system automatically scans all sources and sends notifications about notices that match a defined company profile.
Modern monitoring systems use not just simple keyword filters but also semantic classification — they understand the context of a notice and can match it to the firm’s competences even when the contracting authority used non-standard terminology.
Chance scoring (AI analyses history, criteria, competition)
The bid / no-bid decision is the moment when AI can deliver the greatest strategic value. The system analyses:
- The firm’s historical results in similar contracts
- Award criteria and their weights (price vs. quality vs. experience)
- Known competitors of the contracting authority (based on its contract history)
- Participation conditions vs. the firm’s actual resources
- Delivery deadlines vs. team availability
On that basis it generates a percentage score — an estimated probability of winning the given tender. The firm can set a threshold (for example “we submit bids at a score above 40%”) and automatically drop contracts with a low probability of success.
Extracting requirements from the SIWZ (NLP on PDF documents)
Specifications of Contract Terms are often 50–200-page PDF documents. Reading them by hand and extracting the key information takes an experienced specialist 4–8 hours. NLP (Natural Language Processing) algorithms can, within a few minutes:
- Extract all technical and functional requirements
- Identify award criteria with weights
- Find participation conditions for the procedure
- List required documents and statements
- Detect potential risks and “traps” in the SIWZ text
Generating bid documents (templates + AI)
The last stage — creating the bid itself — can also be partly automated. The system uses a template library and the firm’s earlier bids to generate draft documents:
- Bid form with company data filled in automatically
- Statements on meeting conditions (JEDZ / ESPD, statements under Art. 125 of the Public Procurement Law)
- Experience lists based on a database of completed projects
- Technical descriptions based on earlier bids for similar contracts
- Cost estimates based on historical price data and current rates
Instead of creating documents from scratch, the bid specialist verifies, corrects, and polishes versions generated by AI. That is a paradigm shift: from “writing” to “editing.”
AI scoring — how to assess the chance of winning a tender before you submit a bid?
Tender scoring is the function that pays back the automation investment fastest. The mechanism is simple: instead of relying on the intuition and experience of one employee, the firm uses a predictive model trained on thousands of historical procedures.
How does scoring work?
The AI model analyses every new notice against a dozen or so variables and assigns it a score on a 0–100% scale. Key factors in the algorithm:
Profile fit:
- Does the subject of the contract sit within the firm’s core competences?
- Has the firm delivered similar projects in the past?
- Does it meet participation conditions (turnover, references, staff)?
Competitive analysis:
- How many contractors submitted bids to this contracting authority in the past?
- Who are the main competitors in this contract category?
- What prices did competitors offer in similar procedures?
Economic factors:
- What is the estimated contract value vs. delivery cost?
- What are the award criteria — does price or quality dominate?
- Is the delivery deadline realistic given available resources?
Risk factors:
- Does the contracting authority have a history of problems (appeals, cancellations)?
- Does the SIWZ contain unusual clauses or requirements?
- Does the project carry technical or staffing risk?
Decision thresholds
In practice firms use a three-level decision system:
- Score 60–100% — automatic “bid” decision — the offer is prepared as a priority
- Score 30–59% — manager decision required — additional factors are analysed
- Score 0–29% — automatic “no-bid” decision — the contract is skipped
That system lets the firm concentrate resources on tenders with the highest probability of success, instead of spreading itself across dozens of “trial” procedures.
Learning from results
The key advantage of AI scoring over expert judgement: the model learns from every result. Every win and every loss goes back into the model, which corrects its weights. After 6–12 months of operation scoring becomes substantially more accurate than at the start — and substantially more accurate than the intuition of even an experienced specialist.
What is more, the model learns not just on the firm’s own data but also on market data. A platform aggregating the results of thousands of procedures sees patterns unavailable to a single contractor — for example that a given contracting authority systematically picks the cheapest bid despite declared quality criteria, or that in a given region a certain firm dominates a contract category.
The cumulative effect is powerful: a firm using AI scoring for 2 years has a fundamentally different quality of bid / no-bid decisions than a firm just starting with automation. That is a competence advantage that grows over time and is hard for competitors to copy.
Real-time BZP and TED monitoring — the end of manual checking
Daily monitoring of public-procurement portals is one of the most frustrating tasks in bid departments. It is tedious, repetitive, and — worst of all — critically important. Missing one notice is a lost opportunity that cannot be undone.
The problem with manual monitoring
A firm operating in several industries (for example IT + consulting + training) must track notices on at least four platforms:
- BZP (Public Procurement Bulletin) — national contracts below EU thresholds
- TED (Tenders Electronic Daily) — contracts above EU thresholds across the EU
- BIP (Public Information Bulletins) — requests for quotation below PLN 130,000
- BK (Competitiveness Database) — contracts financed from EU funds
Each portal has a different interface, different filters, and a different publication frequency. A tender specialist spends 2–4 hours a day on manual browsing — often in “scroll and scan” mode, which leads to visual fatigue and missed notices.
Automatic monitoring — how it works
An automatic monitoring system runs in a continuous cycle (24/7) and performs the following operations:
- Data retrieval — automatic scraping or API integration with all sources (BZP, TED, BIP, BK)
- Classification — an NLP algorithm assigns each notice to industry categories
- Matching — comparison with the company profile (industry, region, value, contract type)
- Preliminary scoring — a fast chance assessment based on basic parameters
- Notification — email / SMS / Slack alert with a summary and a link to the full analysis
Comparison: manual vs. automatic monitoring
| Parameter | Manual monitoring | Automatic monitoring |
|---|---|---|
| Daily time | 2–4 hours | 5–15 minutes (reviewing alerts) |
| Source coverage | 2–3 portals (BZP + TED) | 4+ portals (BZP + TED + BIP + BK) |
| Frequency | 1–2 times a day | Continuous (every 15–60 minutes) |
| Missed notices | 10–20% (fatigue, leave) | < 1% (technical outages) |
| Monthly cost | PLN 8,000–15,000 (specialist FTE) | PLN 500–2,000 (subscription) |
| Scalability | Linear (more industries = more time) | Constant (adding an industry is a filter change) |
| Weekend work | No | Yes (the system has no days off) |
The difference is drastic — monitoring automation saves 90–95% of time while increasing coverage and eliminating human error.
How to configure effective automatic monitoring?
The key to effective monitoring is a precise company profile. Filters that are too broad generate information noise (dozens of irrelevant alerts a day); filters that are too narrow miss interesting contracts at the edge of the firm’s competences.
A recommended configuration includes:
- Industries and CPV codes — core CPV codes matching the firm’s main competences + related codes (for example an IT firm monitors not just “72000000 – IT services” but also “48000000 – Software packages” and “71000000 – Architectural and engineering services” when it delivers BIM projects)
- Geographic region — the area in which the firm can realistically deliver the contract (for remote services — all of Poland; for on-site services — a selected region)
- Contract value — minimum and maximum value aligned with the firm’s capacity (contracts that are too small will not cover bidding costs; contracts that are too large exceed delivery capacity)
- Type of contracting authority — central administration, local government, State Treasury companies, universities, hospitals (each type has a different specificity and a different purchasing culture)
- Exclusions — contracting authorities with which the firm had negative experience, or contract categories with a historically low win rate
How much time does automation save? Real figures
Let us move to concrete numbers. The table below shows time savings at individual stages of the bidding process after deploying AI automation:
| Process stage | Manual time | Time with AI | Saving |
|---|---|---|---|
| Notice monitoring (daily) | 2–4 h | 10–15 min | 92% |
| SIWZ/SWZ analysis | 4–8 h | 30–60 min | 85% |
| Bid / no-bid decision | 2–4 h | 5–15 min | 90% |
| Document preparation | 20–40 h | 8–16 h | 60% |
| Verification and correction | 4–8 h | 2–4 h | 50% |
| Bid submission | 1–2 h | 30–60 min | 50% |
| Total per bid | 40–80 h | 12–22 h | 63% |
What do these savings mean in practice?
For a firm submitting 10 bids a month:
- Before automation: 400–800 hours of work / month → 2.5–5 FTEs devoted to bidding
- After automation: 120–220 hours of work / month → 0.75–1.4 FTEs on bidding
That is not just a cost saving — it is human capacity freed for strategic work: building relationships with contracting authorities, improving the substantive offer, negotiating terms in a single-source procedure.
Automation ROI — when does it pay back?
At a typical cost of deploying a bid-automation system (PLN 2,000–5,000 / month for the platform + PLN 20,000–50,000 for configuration and integration), the investment pays back within 2–4 months — assuming the firm submits at least 5 bids a month.
An additional effect: a higher win rate. Firms using AI scoring report a win-rate increase of 5–15 percentage points — which, given contract values in public procurement, means hundreds of thousands of zloty in extra revenue per year.
Hidden benefits of automation
Beyond measurable time and cost savings, bid automation brings benefits that are harder to quantify but equally important for competitiveness:
- Quality standardisation — every bid prepared with AI meets the same quality standard, regardless of who verifies it. That removes the “good shift / bad shift” problem in the bid department.
- Institutional memory — knowledge of processes, templates, and best practices is encoded in the system, not in employees’ heads. The departure of a key specialist no longer paralyses the department.
- Speed of response — the firm can submit a bid even for a contract announced with a short deadline (7–14 days), because AI does most of the preparatory work in hours, not days.
- Management data — leadership gets a dashboard of metrics: how many bids are in the pipeline, what the scoring is, what public-procurement revenue is forecast for the next quarter.
Tools on the market: comparison of tender platforms 2026
The market for tools to automate bidding in public procurement has matured significantly in the last two years. Below is a comparison of the main platforms available in Poland in 2026:
| Function | BudOS | Przetargi360 | OpenNexus | e-Zamówienia |
|---|---|---|---|---|
| BZP monitoring | ✅ Real-time | ✅ Every 1 h | ✅ Every 2 h | ✅ Native |
| TED monitoring | ✅ Real-time | ✅ Every 4 h | ✅ Every 4 h | ❌ |
| BIP monitoring | ✅ 5,000+ sources | ⚠️ Selected | ❌ | ❌ |
| BK monitoring | ✅ Full | ✅ Full | ✅ Full | ❌ |
| AI scoring | ✅ ML + history | ⚠️ Rules | ❌ | ❌ |
| SIWZ extraction (NLP) | ✅ GPT-4 + own model | ⚠️ Basic | ❌ | ❌ |
| Document generation | ✅ Templates + AI | ⚠️ Templates | ⚠️ Templates | ❌ |
| Historical database | 1.4 million procedures | 800 thousand | 500 thousand | No archive |
| Active notices | 5,600+ live | 3,000+ | 2,000+ | All BZP |
| Competitive analysis | ✅ Contractor profiles | ⚠️ Basic | ❌ | ❌ |
| API / integrations | ✅ REST API | ⚠️ Webhook | ❌ | ⚠️ XML |
| Price (from) | PLN 1,500 / month | PLN 900 / month | PLN 500 / month | Free |
The key to choosing a tool
The choice of platform depends on the scale of activity, budget, and the firm’s needs. There is no single ideal solution for everyone — the key is fitting the tool to the stage of development of the bid department:
- e-Zamówienia — the mandatory platform for submitting bids, but it offers no automation or analytics functions. It is the “regulatory minimum,” not a competitive tool. Every firm must use it, but should not treat it as the single source of information about tenders.
- OpenNexus / Przetargi360 — solid solutions for firms submitting 3–8 bids a month that need mainly monitoring and basic alerts. They work as a first step in automation — relatively low entry cost, fast deployment, but limited AI and predictive-analysis capabilities.
- BudOS — an enterprise-class platform for firms that treat public procurement as a main sales channel and need a full stack: from AI scoring, through NLP extraction, to document generation and competitive analysis. Highest cost, but also the highest return on investment at large bidding scale.
The key difference for BudOS is a database of 1.4 million historical procedures combined with 5,600 active notices updated in real time — that is the foundation on which predictive scoring models are trained. The larger the historical base, the more accurate the forecasts and the better the bid matching. No other platform on the Polish market holds a comparable volume of training data.
The new Public Procurement Law 2025/2026 and automation — what is changing?
Public Procurement Law in Poland is in continuous change — and the 2025–2026 amendments have a direct effect on what can be automated in the bidding process.
Full electronic procurement — in force since 2024
From 1 January 2024 all public contracts (regardless of value) must be run electronically. That eliminates paper bids and opens the way to full automation of submission — from the electronic signature to automatic generation of files in required formats.
A uniform document standard (eESPD 2.0)
The new version of the European Single Procurement Document (ESPD / JEDZ) in structured XML/JSON format makes automatic filling and verification substantially easier. An AI system can automatically map company data onto JEDZ fields without human intervention.
Mandatory e-invoices and e-Zamówienia
Integration of purchasing platforms with e-invoice systems (KSeF) creates a new data ecosystem in which AI systems can automatically verify a contractor’s history, check references, and confirm financial capacity — shortening the time needed to prepare qualification documentation.
Changes in award criteria
The 2025 Public Procurement Law amendment puts more weight on non-price criteria (quality, innovation, social and environmental aspects). For automation that is an opportunity — AI systems can better optimise bids for multi-criteria evaluation, selecting arguments and evidence against the specific criterion weights of a given contracting authority.
In practice that means a bid prepared with AI not just meets formal requirements, but is strategically optimised to maximise points. The system analyses historical evaluations by the given contracting authority and identifies which formulations, evidence, and solutions scored highest in the past — then suggests an analogous approach in the new bid.
Contracts below thresholds — new transparency duties
From 2026 contracting authorities must publish a larger number of requests for quotation in BIP (the threshold lowered from PLN 130,000 to PLN 80,000). That means more notices to monitor — and an even greater advantage for firms using automatic monitoring over those that rely on manual browsing.
Estimates indicate that the new threshold will increase the number of publicly available notices by 25–35% — which for firms without automation means either more work on monitoring, or accepting that a substantial part of the market remains invisible to them. Firms with automatic BIP monitoring will not feel that change — their systems will simply process more data at no extra cost.
Dynamic purchasing systems and framework agreements
The new rules also promote dynamic purchasing systems (DSZ), in which the contracting authority runs an open procedure that contractors can join on an ongoing basis. That model is well suited to automation — an AI system can monitor active DSZ, automatically register the firm as a participant, and generate partial bids for individual call-offs within the system.
Case study: an IT firm submitting 20 bids a month
We present an anonymised case of a firm in the IT sector (150 employees, turnover PLN 45 million) that in 2025 deployed the BudOS platform to automate its public-procurement bidding process.
The “before” situation — controlled chaos
Before deployment the firm maintained a three-person team dedicated to bidding:
- Monitoring specialist — daily browsing of BZP and TED, preliminary selection of notices
- Bid specialist — preparing documentation, filling in forms, assembling annexes
- Department head — bid / no-bid decisions, final verification, deadline oversight
Key “before” metrics:
| Metric | Value |
|---|---|
| Number of bids submitted | 20 / month |
| Total team working time | 160 h / month |
| Average time per bid | 8 h |
| Win rate | 18% |
| Wins / month | 3.6 |
| Bid-department cost | PLN 45,000 / month |
| Missed notices (estimated) | 15–20% |
The firm won on average 3–4 tenders a month out of 20 submitted bids. The team worked under constant deadline pressure, and turnover in bidding roles was 40% a year — specialists left because of monotony and stress.
BudOS deployment — a 6-week process
Deployment of the BudOS platform ran in three phases:
Weeks 1–2: Profile configuration and integration
- Defining the firm’s competence profile (industries, technologies, regions, contract size)
- Importing the history of submitted bids (won and lost) to train the scoring model
- Integration with the CRM and the references database
Weeks 3–4: Templates and document library
- Creating a library of bid templates (forms, statements, lists)
- Configuring automatic filling of company data
- Preparing a base of technical descriptions for reuse
Weeks 5–6: Tests and optimisation
- Parallel preparation of 5 bids the “old” and “new” way — quality comparison
- Scoring calibration based on team feedback
- User training (2 days of workshops)
The “after” situation — 6 months from deployment
After six months of work with the BudOS platform the firm reorganised the bid department:
- 1 bid specialist (with AI competence) — verifying alerts, overseeing scoring, final editing of bids
- AI support — monitoring, scoring, requirements extraction, generating working documents
Key “after” metrics:
| Metric | “Before” value | “After” value | Change |
|---|---|---|---|
| Number of bids submitted | 20 / month | 22 / month | +10% |
| Total working time | 160 h / month | 60 h / month | −63% |
| Average time per bid | 8 h | 2.7 h | −66% |
| Win rate | 18% | 31% | +13 p.p. |
| Wins / month | 3.6 | 6.8 | +89% |
| Bid-department cost | PLN 45,000 / month | PLN 18,000 / month | −60% |
| Missed notices | 15–20% | < 2% | −90% |
Key takeaways from the case study
- Win rate rose from 18% to 31% — mainly thanks to better tender selection (AI scoring dropped bids with a low probability of winning) and higher-quality documentation generated with artificial intelligence.
- The firm submits more bids with less effort — 22 bids instead of 20, but at 60% less working time. That is the effect of automating repetitive tasks and eliminating manual monitoring.
- Reducing the team from 3 to 1 person did not mean layoffs — two specialists moved to the project-delivery department, where their knowledge of contracting-authority requirements proved invaluable in delivering contracts.
- Deployment ROI: 4.2 months — a saving of PLN 27,000 / month on personnel costs + extra revenue from 3 additional won tenders a month covered the cost of the platform and deployment in under 5 months.
- Psychological effect — the remaining specialist reports higher job satisfaction. Instead of spending time on manual portal browsing and form-filling, they concentrate on bid strategy and building relationships with contracting authorities. Turnover in the department fell to zero.
FAQ — frequently asked questions
Is bid automation compatible with the Public Procurement Law?
Yes — fully. The Public Procurement Law regulates the process on the contracting authority’s side (how to run a procedure), not on the contractor’s side (how to prepare a bid). A contractor may use any tools to prepare a bid — provided the submitted bid meets all formal and substantive requirements set in the SWZ. Automation helps meet those requirements better and faster; it does not bypass them. It is worth stressing that the contracting authority itself uses electronic platforms (e-Zamówienia, miniPortal) — expecting a contractor to drop technology when preparing a bid would be an anachronism.
Can AI submit a bid on its own, without human oversight?
At the current stage of the technology we do not recommend full autonomy, and it is not market practice. AI handles monitoring, scoring, requirements extraction, and draft document generation well. However, final verification and the decision to submit a bid should always stay with a human. Legal liability for the content of the bid rests with the contractor (a natural or legal person), not with an algorithm. The recommended model is “AI prepares — a human verifies and approves.” In the future, as regulation of AI in public administration develops, that model may evolve — but in 2026 human oversight is the industry standard and a compliance requirement.
How long does it take to implement a bid-automation system?
Typical implementation time is 4–8 weeks, depending on the scale of activity and the complexity of the company profile. Basic monitoring and alerts can go live within a few days. Full scoring configuration (with bid-history import and model training) takes 3–4 weeks. Document templates and integrations with internal systems — another 2–4 weeks.
Does automation pay off for a small firm submitting 3–5 bids a month?
Yes — though ROI appears more slowly than at large scale. For firms submitting 3–5 bids a month the main value is not time savings (though that matters too), but better scoring and tender selection. Instead of submitting 5 bids “blind” at a 15% win rate, the firm can submit 3 carefully selected bids at a 35% win rate — winning as many or more with less effort.
What data is needed to launch AI scoring?
The minimum is a history of 20–50 submitted bids (won and lost) from the last 2–3 years — together with information on the contracting authority, value, industry, and award criteria. The larger the history, the more accurate the model. Firms without enough history of their own can use general models trained on market data (for example the 1.4 million procedures in BudOS) — and then fine-tune the model as their own results accumulate. Qualitative data is also worth including: reasons for wins (what the contracting authority praised in the protocol), reasons for losses (price ranking, gaps in experience), and information on competitors who won instead.
Summary — bid automation is not the future, it is the present
Public procurement in Poland is a huge market — but access to it is blocked by bureaucracy, documentation complexity, and a chronic lack of time. Firms that still rely on manual BZP monitoring, intuitive bid / no-bid decisions, and hand-prepared documents — lose not just time and money, but above all chances to win contracts.
Automation with AI changes the rules of the game on the public-procurement market:
- 24/7 monitoring — no notice is missed, regardless of leave, sick days, and weekends
- Predictive scoring — resources concentrated on tenders with the highest chance of winning, elimination of shots in the dark
- NLP extraction — hours of SIWZ analysis turned into minutes, with automatic identification of risks and requirements
- Document generation — from “writing from scratch” to “editing a draft,” while keeping consistency and completeness
- Continuous learning — every win and loss improves the model, increasing forecast accuracy month by month
The effect? A 60% time saving, a win-rate increase of 10–15 percentage points, and a team freed from monotonous work for strategic tasks. For firms submitting 10+ bids a month that is the difference between profitability and a loss on tender activity.
The public-procurement market in Poland is growing — in 2026 the value of announced procedures will exceed PLN 250 billion. Firms that invest in automation today will have an advantage that is hard to close in a year or two. Those that delay — will submit ever more expensive bids at an ever lower win rate, losing to competitors who bet on technology.
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