Artificial Intelligence (AI) is already part of the operation of companies that need to gain productivity, reduce service friction, and make decisions based on data. Adoption is no longer restricted to large corporations because AI tools are now integrated with common everyday systems, such as CRM, call centers, and analysis platforms.
In this content, you will understand what AI is in the business context and see 6 practical ways to apply technology to critical business areas, with examples and criteria to prioritize initiatives.
What is Artificial Intelligence in the context of companies
AI is a set of techniques and systems capable of performing tasks that require information interpretation, data learning, and automated decision-making within defined rules and objectives. In practice, companies use AI to classify requests, predict demand, recommend offers, detect anomalous behavior, and automate repetitive steps with greater operational consistency.
In service and relationship operations, AI often appears in conversational solutions (chatbots and virtual assistants), in call routing and prioritization models, in recommendation mechanisms, and in analyses that identify customer behavior patterns over time.
Why Artificial Intelligence has gained relevance for business
AI has become a priority because it helps resolve operational bottlenecks that directly affect revenue, cost, and customer experience. The technology allows expanding service capacity without growing the team at the same rate, accelerating analyses that previously required manual effort, and applying personalization based on behavioral data.
In areas such as sales, marketing, finance, and security, the gain usually comes from three fronts:
- Execution speed in repetitive or bulky activities;
- More consistent decisions when there are historical data and well-defined criteria;
- Service and relationship scale on digital channels with registration and traceability.
6 ways AI can contribute to your business
1) Virtual assistants for customer support and internal support
Virtual assistants can work at the front, advising customers on recurring questions and guiding purchase steps. They can also operate internally, supporting teams with inquiries about procedures, policies, and product information.
Practical examples:
- Assistant that advises the customer on order status, exchange and return;
- Internal assistant for sales teams to consult business rules, availability, and arguments by segment;
- HR support to answer questions about benefits, onboarding, and routine requests.
Where it usually generates results:
- Volume reduction in repetitive calls;
- Standardization of responses and reduction of rework;
- Accelerated response time during peak demand.
2) Chatbots with AI for answering and automating requests
Chatbots are one of the most direct uses of AI in customer relationships because they serve at scale and structure the screening of demands. In addition to answering questions, a chatbot can execute requests integrated with systems, record calls, and direct more complex cases to an attendant.
Practical examples:
- Issuance of duplicate, registration update and consultation of protocols;
- Schedules and rescheduling with automatic confirmation;
- First-level support with intent screening (technical issue, billing, cancellation, purchase).
Where it usually generates results:
- 24/7 service with reduced queues;
- Increased resolutions at first contact when there is a good knowledge base;
- Structured data collection about contact reasons.
3) AI in sales and marketing for segmentation, personalization, and forecasting
In sales and marketing, AI is applied to understand behavior, predict buying propensity, and improve campaign targeting. The gain appears when the company manages to transform dispersed data into operational decisions, such as lead prioritization, recommended offer, and moment of contact.
Practical examples:
- Lead scoring based on browsing history, interactions, and profile;
- Product recommendations and next approach steps;
- Dynamic segmentation for campaigns based on recent behavior.
Where it usually generates results:
- Increased conversion into a funnel due to better prioritization;
- Reduction of CAC when campaigns become more efficient;
- Revenue growth through offer customization and timing.
4) Financial AI for forecasting and loss prevention
In finance, AI is used to design scenarios and identify risks in advance. In companies with a high volume of transactions, it is also common to use it to detect fraud and anomalies.
Practical examples:
- Cash flow forecast by receivables history and seasonality;
- Identification of default patterns and churn risk by behavior;
- Detection of transactions with a profile that is not the expected standard.
Where it usually generates results:
- Better cash and budget planning;
- Reduction of fraud and chargeback losses;
- Agility gain in analyses that previously relied on spreadsheets and manual validations.
5) AI applied to management and operations to integrate areas and automate processes
In management, AI helps connect data between areas and automate operational decisions. In many scenarios, value appears when combining process automation with layers of intelligence for classification, prioritization, and routing.
Practical examples:
- Call routing by urgency, topic, and customer profile;
- Demand forecasting to adjust service scale and inventory;
- Automatic classification of requests received by text, voice, and social networks.
Where it usually generates results:
- Better use of operational capacity;
- Reduction of cycle time in internal processes;
- Visibility of bottlenecks based on consolidated data.
6) AI for security, compliance, and data protection
AI also helps protect against attacks and suspicious behavior by identifying anomalous patterns in access, transactions, and system use. Value grows when the company defines safety rules and uses models to prioritize events and reduce false positives.
Practical examples
- Detection of suspicious accesses by location, time and device;
- Signaling of fraud attempts by behavioral pattern;
- Automated monitoring for alerts and faster investigation.
Where it usually generates results:
- Reduction of incidents and response time;
- Prioritization of risks based on objective signals;
- Strengthening internal policies and traceability.
How to choose where to start with AI
Use case selection tends to work best when following objective criteria. A practical model considers:
- Impact on the result (revenue, cost, experience, risk);
- Implementation complexity (integrations, data, process changes);
- Data availability (quality, history, governance);
- Regulatory adherence (LGPD, internal policies, audit trails).
Common cases to start with usually involve automated service, call screening, and automations with a direct return on productivity.
Metrics to track results
Defining indicators from the start prevents AI from becoming a “technology” project with no connection to the result. Usual metrics ahead:
- Service: TMA, first-contact resolution rate, CSAT, human transfer fee, cost per contact;
- Sales and marketing: conversion by funnel stage, CAC, LTV, response rate, incremental revenue per campaign;
- Financial and risk: default, fraud losses, detection time, forecasting accuracy;
- Operations: cycle time, automated volume, rework rate, SLA compliance.
Best Practices for Implementing AI with Predictability
- Make one mapping of journeys and processes before automating to avoid digitizing bottlenecks;
- Structures knowledge base and standardization of answers in conversational initiatives;
- Plan integrations with CRM and transactional systems to enable end-to-end automation;
- Define scaling rules for sensitive cases and create audit trails;
- Garanta data governance and adherence to the LGPD with collection, retention, and access criteria.
How Plusoft can support the adoption of AI in service
O Plusoft AI is a chatbot solution with personalization by channel, aimed at companies that need to automate service, collect interaction data, and improve decisions based on consolidated information. The solution can operate on digital channels and support journeys such as financial requests, schedules, purchases, sales, and exchanges, reducing friction and accelerating resolutions.
When conversational AI is connected to business processes and systems, service gains scale and the team begins to concentrate energy on cases that require analysis, negotiation, and specific negotiations.
Do you want to evolve your service with AI applied to the operation? Learn more about Plusoft AI and evaluate the best design for your scenario.
