AI applied to core processes that do save money 

The speech is over, the execution begins. 

During 2024 and 2025, many companies went through the same curve: enthusiasm, pilot projects, impressive demos... and then an awkward question: Where is the real savings? In “hype mode,” AI is used for experimentation. In “execution mode,” AI connects to core processes and is measured against concrete variables: time, cost, quality, risk, and speed of decision. 

The good news is that the value exists. Generative AI is estimated to contribute between USD 2.6 and 4.4 trillion annually trillion in English to the global economy, and that around 75% of potential value focuses on four areas: customer operations, marketing and sales, software engineering and R&D.  
The bad news is that the execution is not automatic, at least 30% Generative AI projects will be abandoned after the pilot before the end of 2025 due to issues such as data, risk controls, costs, and lack of value clarity.  

This article gets right down to the practical aspects. Applied AI“, in which processes does it usually translate into savings, and how to move from the “pretty POC” to an implementation that can withstand audits, operations, and scaling. 

SigloBPO

Redesign the process, not an occasional tool 

A common mistake is implementing AI as a final layer, “have a chatbot summarize those documents”... This can improve specific experiences, but rarely captures sustained savings if the base process remains the same: messy inputs, exceptions without rules, approvals without owners, and scattered data. 

In execution, the approach is different: identify where time and cost are going, and use AI to Reduce repetitive work, elevate quality, and accelerate decisions. within the actual workflow. In other words, it's not about “using AI,” but rather about change how we work. 

Generative AI particularly excels at knowledge and language tasks (writing, classification, search, synthesis), and this directly impacts functions where a large portion of time is consumed in read, write, review, document, and coordinate.  

3 processes where AI often generates savings 

1) Customer Operations 

If your organization has customer service, internal support, help desks, or “customer operations,” that's usually where one of the fastest impacts lies because the work is language-intensive: understanding the case, searching for information, drafting a response, documenting, and closing. 

We are talking about how in a company with 5,000 agents, the application of generative AI increases incident resolution by 14% per hour and it reduces the handling time per case by 9%, in addition to reducing attrition and escalations to supervisors.  

Where does saving come from? 

• Fewer minutes per interaction (AHT), which reduces cost per ticket. 

• More “first contact resolution”, which reduces re-contacts and rework. 

• Better consistency (fewer errors from incomplete answers). 

What distinguishes the pilot execution: “A bot” is not enough. What works is integrating AI with a governed knowledge base, resolution guides, automatic case classification, and quality controls (e.g., detecting missing information before closing a ticket). 

2) Purchases and contracts 

Savings rarely come solely from “making a document faster.” They come from better value capture: negotiating better, reducing risks, preventing leaks (clauses, automatic renewals, penalties), and increasing compliance with internal policies. 

BCG has reported that, in its work with companies, procurement functions using AI can reduce total costs by approximately between 15% and 45% (by category) and remove up to 30% from the team's work, freeing up capacity for strategic tasks.  

Where is AI applied practically? 

• Reading and extracting clauses in contracts and addendums (alerts for risks and deviations). 

• Automatic Comparison Between Negotiated Conditions vs. Executed Conditions. 

• Assisted generation of RFPs, comparative matrices, and negotiation minutes with traceability. 

What distinguishes the pilot execution: Having a “contract summarizing model” is useful; having it integrated into a workflow where those alerts trigger actions (legal review, renegotiation, blocking of out-of-policy purchases) is what turns AI into real savings. 

3) Internal knowledge and back office 

In many teams, the “invisible cost” is coordination time: searching for documents, requesting versions, clarifying criteria, reconstructing context, and re-explaining. Generative AI can change that by allowing knowledge retrieval through natural language questions and continuous dialogue.  

How does that translate to savings? 

• Fewer hours spent “searching to be able to work.”. 

• Fewer errors from using incorrect versions or outdated criteria. 

• Faster decisions because context is available. 

What distinguishes the pilot execution: The knowledge base must be curated (not a “document dump”), with permissions, versions, owners, and a governance layer to prevent the AI from “making things up” or citing erroneous sources. 

How to go from talk to execution 

Start with the cost (not the tool) 

Define a measurable goal: reduce AHT, reduce rework, increase resolution, decrease contract leakage, shorten approval cycles. If you can't measure “before and after,” it will be difficult to demonstrate value. 

2) Choose “core” use cases with an owner and a stable process 

The best first case is one that meets three conditions: 

• sufficient volume (many transactions/cases) 

• Relatively clear rules 

• a responsible area that can operate the change 
  
  

3) Ensure data and controls from day 1 

Gartner hits the nail on the head: many projects fail because of data quality, insufficient controls, costs, and lack of clear value.  
Practical translation: define sources, permissions, traceability, quality assessment, and risk controls (including human review where applicable). 

4) Design “human-in-the-loop” for quality (not for braking) 

It's not about one person redoing everything. It's about them reviewing where it hurts the most: exceptions, high risks, sensitive decisions, quality samples. This way, confidence increases and errors decrease. 

5) Scale with standards (playbooks + continuous measurement) 

When a case works, it is documented: what the AI does, what it doesn't do, how it is measured, what error thresholds are, and how the team is trained. Without that, each implementation becomes custom and expensive. 

But is all this worth it?  

The conversation about AI is no longer “if it's worth it,” but Where and how to run it so that it impacts real costs without breaking quality or control. The difference between discourse and execution is simple: AI stops being a demo and becomes part of the process, with metrics, governance, and continuous improvement. 

If you are exploring AI in your organization, choose A core process, Define a savings metric and test a case with controls and measurement from the start. If you're interested in this topic, share the blog with your team and tell us: in which process would you like to see that impact: customers, purchasing, or back office?