Winning Tenders, Losing Profit: The Role of AI in B2B Deals

Denis Ermilov Deputy Director for Strategic Clients at Cinimex CRM and the sales funnel answer the question, “Will we close the deal?” — stage, amount, probability, and timeline. That is enough to manage sales. But it is not enough to manage the outcome. Profit is determined by factors that usually exist outside the funnel: delivery costs, contract terms, the cost of changes, penalty risks, and the cost of scarce resources.

The discussion around the economic outlook for 2026 increasingly revolves around a common theme: low growth rates. For businesses, this means that expanding revenue “through market growth” is becoming more difficult, while competition for existing demand is intensifying. Under these conditions, companies are increasingly turning to tools that can deliver quick sales results: discounts, accelerated timelines, additional commitments, and customized terms. These decisions can help win tenders and pass approval processes, but at the same time they increase the cost and risk burden of a deal. Profitability, meanwhile, often remains something that is not actively managed but only becomes visible later, during execution.

A typical scenario then unfolds. A company wins a tender by offering a discount and promising to “deliver quickly.” Two months later, the customer introduces new security requirements, the legal team increases liability and penalties, and the number of integrations turns out to be twice as high as expected. The project is formally on track, the customer is satisfied with the progress, but the economics have already turned negative. The problem is not with any particular team: this is how modern B2B deals often work, with pricing and contractual decisions being made faster than the facts determining delivery costs, contractual risks, and final profitability can be collected and verified.

Diagnosis: The Sales Funnel Shows Revenue, but Not Profit

CRM and the sales funnel answer the question, “Will we close the deal?” — stage, amount, probability, and timeline. That is enough to manage sales. But it is not enough to manage the outcome. Profit is determined by factors that usually exist outside the funnel: delivery costs, contract terms, the cost of changes, penalty risks, and the cost of scarce resources.

This is a well-known management effect: when a metric becomes a target, an organization starts optimizing the metric rather than the actual outcome. In sales, that metric is often revenue and speed of closing, while deal profitability ceases to be something that is managed on a regular basis.

Why This Has Become the New Reality

The reasons are familiar across IT projects, industrial services, telecom contracts, logistics, and professional services.

First, deal cycles are long and involve many stakeholders. Terms change along the way: new requirements emerge, acceptance criteria change, and legal provisions become stricter.

Second, deals are highly customized. Integrations, SLAs, and security requirements turn delivery costs into a set of assumptions that can quickly become outdated.

Third, deal information is fragmented across functions. Sales teams manage the pipeline, delivery teams track tasks and resource plans, finance maintains separate calculations, and legal teams work with different contract versions. The complete picture emerges only later.

A Paradigm Shift: From CRM to a Deal Management Framework

In response to these limitations, a discipline known internationally as DealOps / Deal Economics is emerging. In essence, it is the operational management of deal economics. The idea is pragmatic: a deal is treated as a management object with continuously updated information on pricing and terms, delivery costs, legal commitments and risks, changes in scope, and planned-versus-actual performance.

The key requirement for such a framework is manageability. It should not be a “beautiful report” produced at the end of the month, but a mechanism that makes it possible at any moment to reconstruct which terms were agreed, what was added, who recorded each change and when, how it affects cost and risk, and ultimately make an informed decision.

The Role of AI: Making Deal Facts Measurable and Manageable

AI does not replace negotiations or expert judgment. Its practical role is to quickly consolidate facts scattered across documents and communications and highlight changes that affect cost and risk. AI is particularly useful where people spend significant time searching for information, comparing versions, and reconstructing context.

Below are eight typical cases where a deal economics management framework combined with AI can deliver practical value.

Case 1. A Discount Was Approved, but Delivery Costs Were Not Recalculated

A discount is approved quickly because closing the deal and securing the result is considered a priority. The deal framework captures the initial cost assumptions and recalculates acceptable pricing thresholds when terms change. AI helps compare the discount with commitments made in emails and documents, ensuring that pricing decisions are accompanied by decisions on scope, phasing, and change terms.

Case 2. The “Bid or No-Bid” Decision Is Made Without an Economic Filter

The RFP is extensive and time is limited. AI quickly identifies the “expensive” elements: security requirements, integrations, penalties, acceptance criteria, warranties, and deadlines. The result is earlier tender qualification and a clear negotiation agenda before the company invests significant resources in detailed preparation.

Case 3. The Commercial Proposal Exists Separately from the Deal Economics

The commercial proposal has been approved, while the cost calculation for contract execution remains “with one person in a spreadsheet.” The deal framework links commitments to the scope of work, roles, and estimated effort. AI helps identify discrepancies between the wording in the commercial proposal or communications and what has been included in the delivery cost calculation.

Case 4. Scope Creep Happens Through Emails and Calls

Changes arrive as a series of small clarifications. AI captures new commitments from meeting notes and emails and compares them with the original version of the agreements. The deal framework converts this into a management action: adjust the price or timeline, or formally document the scope boundaries.

Case 5. Legal Changes Alter the Deal’s Risk Profile

There are multiple contract versions, with changes to penalties, liability, and acceptance terms. AI compares the versions and highlights economically significant changes. This makes it possible to discuss legal wording as an economic parameter: what exactly increases the deal’s risk and cost.

Case 6. The Deal Is Closed, but There Is No One Available to Deliver It

Key skills or resources required to execute the contract are unavailable, already committed, or have become more expensive, while deadlines have already been promised. The deal framework compares resource requirements with availability and captures the cost of resource scarcity. AI helps aggregate information about requirements so that the resource plan is based on the actual scope of work rather than an optimistic estimate.

Case 7. The Margin Decline Becomes Visible at the Portfolio Level, Not in an Individual Deal

A single deal may appear acceptable, while the overall customer portfolio moves toward low margins or even losses due to recurring combinations of terms. The deal framework aggregates the parameters and makes the structure of losses visible by type of work, terms, risks, and payment schedules. AI accelerates classification and helps identify recurring causes of cost overruns.

Case 8. Post-Deal Reviews Become a Standard Practice

Without systematic analysis, organizations repeat the same mistakes. AI helps identify recurring causes of deviations: where scope boundaries were not clearly defined, where contractual concessions were made without compensation, and where integrations were underestimated. The deal framework turns these insights into continuously updated rules and training materials. Ideally, the company also gains insights and recommendations for changing its business model and developing improved services for customers.

Where to Start Without Turning It into a “Year-Long Transformation”

A practical starting point is usually to identify two or three of the most common sources of losses — such as discounts without recalculation, scope creep, or contractual risks — and build a framework for capturing the relevant facts and decisions around them.

The success criterion is not a “beautiful dashboard,” but a reduction in the number of cases where deal economics turn negative only after execution has already begun.

In large international companies, deal economics management frameworks and AI assistants are typically integrated into sales, delivery, and financial control processes. In Russia, comparable approaches and products are emerging — tools that bring deal information and its economics together and help companies keep profitability under control as a deal progresses and is executed.