Executive Summary

Vitor Luis Genaro; Felipe Carvalhal

Summary prepared by the ResumeAI tool, an artificial intelligence solution developed by the Pecege Institute focused on synthesis and writing.

The technology consulting market demands increasing operational efficiency from organizations, combined with analytical capability and speed of customer response. In this dynamic scenario, the pre-sales area assumes a fundamental strategic role, being responsible for translating complex client demands into robust technical and commercial proposals. Such proposals not only support investment decisions but also enable the realization of new contracts, directly impacting the company’s competitiveness and financial sustainability. Proposal development is a knowledge-intensive activity, involving requirements analysis, scope definition, effort estimation, solution modeling, and risk assessment, as highlighted by Marr (2019) on the importance of Artificial Intelligence in contemporary business models.

However, Ábaco Consulting, a technology consulting firm, faced significant challenges in its commercial proposal development process. The pre-sales operation presented notable operational inefficiencies, characterized by a lack of standardization, excessive centralization of knowledge in a few senior professionals, and low reuse of information between proposals. These factors limited the operation’s scalability, increased rework, and hindered information standardization, compromising the agility and consistency necessary to maintain competitiveness in the sector. The dependence on individual knowledge, without structured repositories, created bottlenecks and raised operational costs.

To diagnose the root causes of these inefficiencies, Ábaco Consulting conducted an in-depth analysis, using the “5 Whys” framework, a technique widely employed to investigate problems down to their structural origins (OHNO, 1988). The analysis compared the organization’s internal indicators with market benchmarks across four critical dimensions: average sales value (rate/hour), pre-sales structural cost, proposal development time, and proposal validation time. The results revealed that, across various project sizes, the practiced sales value diverged from expectations, and the average development and validation times significantly exceeded market standards, especially in more complex projects.

The detailed investigation, using the “5 Whys” method, revealed that the divergence in sales value was caused by commercial insecurity, stemming from the lack of standardization and structured documentation of proposals. This insecurity led to the application of defensive discounts on larger proposals, directly impacting the organization’s profitability margins and EBITDA. The absence of a structured knowledge base and the reliance on senior professionals for proposal validation contributed to reduced competitiveness and difficulty in scaling the participation of junior profiles, increasing the area’s structural cost.

The high structural cost of the pre-sales area was attributed to the strong dependence on senior consultants for the preparation and validation of proposals. This concentration of knowledge in a few individuals resulted in operational costs close to or exceeding the sales value in larger projects, limiting the area’s scalability and the ability to integrate less experienced professionals. The absence of a structured knowledge repository and the lack of integration between information management tools were crucial factors that prevented the efficient reuse of accumulated experiences, generating rework and prolonging the preparation cycles.

The deadlines for proposal development and validation, which consistently exceeded estimates, were identified as a consequence of a process based on manual content recreation and the absence of a reusable knowledge repository. This scenario resulted in frequent rework, multiple validation rounds, and overload on key professionals, delaying customer response and increasing the risk of losing business opportunities. Knowledge governance was deficient, with the use of isolated spreadsheets and a lack of integration between documentation tools, meeting records, and institutional templates, culminating in low standardization and information inconsistency.

The integrated analysis of the challenges showed that the inefficiencies were not inherent to the technical complexity of the projects, but rather to structural limitations in information organization and knowledge governance. The dependence on individual professionals’ knowledge, the lack of information reuse mechanisms, and the high operational costs compared to market practices compromised the area’s efficiency, the financial predictability of opportunities, and the sustainable growth capacity of the pre-sales operation. This diagnosis pointed to the urgent need for a restructuring that addressed these root causes systemically.

Faced with this scenario, Ábaco Consulting evaluated three main intervention alternatives to restructure the proposal development process. The first option consisted of expanding the pre-sales team with new junior professionals. Although it could increase productive capacity, this alternative would not solve the structural causes of the problem, such as the lack of standardization and the dependence on tacit knowledge, resulting in high hiring costs and a long-term return on investment (ROI). This approach did not address the root of the inefficiency, merely adding more resources to an already flawed process.

The second alternative considered the acquisition of a market solution for proposal automation and intelligent CRM. This option presented a medium implementation cost and good scalability, with a medium-term ROI. However, market solutions often impose standardized models that may have limitations in adhering to Ábaco’s specific consulting model, in addition to offering less control over internal data and processes. Customization and evolutionary flexibility would be restricted, which could compromise adaptation to the business’s particularities and integration with the company’s existing systems.

The third alternative, and the recommended solution, proposed the internal development of an autonomous Artificial Intelligence agent, customized to the specificities of Ábaco. This option stood out for presenting the lowest implementation cost, leveraging existing licenses and infrastructures within the organization. Furthermore, it offered high adherence to the consultative model, excellent scalability and continuous evolution, high control over data and processes, and a short-term return on investment, estimated at up to six months. Studies such as those by Bosche et al. (2025) and Joshi (2025) corroborate the effectiveness of generative AI and intelligent agents in complex commercial processes, highlighting the reduction of human effort and the increase in documentary consistency.

The justification for choosing an autonomous Artificial Intelligence agent lies in its simultaneous alignment with operational, strategic, and economic criteria. Operationally, the solution allows for structuring and standardizing organizational knowledge, reducing dependence on senior professionals and enabling the participation of junior and mid-level profiles in proposal development. Technologically, integration with existing systems such as SharePoint and transcription tools ensures control over data, information security, and flexibility. Economically, leveraging existing licenses and reducing the need for team expansion minimize incremental costs, accelerating return on investment and expanding operational capacity without a proportional increase in costs.

Strategically, the implementation of the AI agent positions Ábaco Consulting at an advanced level of digital maturity, transforming the pre-sales process into a structured, scalable, and data-driven asset. This not only solves current problems but also creates a sustainable foundation for the organization’s continuous evolution of its business model. The initiative aligns with the vision of strengthening competitiveness in a market increasingly driven by speed, data intelligence, and operational efficiency, according to the literature on the impact of AI in business processes (Marr, 2019; Bosche et al., 2025).

The Artificial Intelligence agent implementation plan was structured in five phases, with a total timeline of nine months, aiming for progressive and controlled adoption. The first phase, Mapping and Initial Integration, lasting one month, focuses on integration with automatic meeting transcription tools, such as Fathom or Fireflies, to capture information from initial customer contacts. The expected outcome is the automatic and structured capture of meeting data, essential for feeding the agent’s knowledge base and ensuring that crucial information is duly recorded from the beginning of the sales cycle.

The second phase, Knowledge Base Organization, also lasting one month, focuses on structuring and categorizing previous proposals by segment, scope, and complexity, and on connecting via API with SharePoint. The objective is to create a unified and searchable knowledge base, with controlled and secure access, allowing the AI agent to access and utilize relevant historical information for generating new proposals. This stage is crucial for overcoming the absence of a reusable knowledge repository, one of the main problems identified in the initial diagnosis.

The third phase, Automation of Templates and Flows, lasting two months, involves configuring automated flows for filling institutional, technical, and commercial templates, using generative AI. The expected outcome is the automated creation of standardized and consistent documents, reducing manual effort and rework in proposal preparation. This phase is fundamental to ensuring information standardization and documental consistency, which were significant deficiencies in the previous process, according to the root cause analysis.

The fourth phase, Generation and Validation of Pilot Proposals, also lasting two months, is dedicated to the development of the first complete proposals generated by the AI agent. This stage includes supervised review and feedback collection from the involved areas, ensuring that the generated proposal model is validated and ready for broader adoption. Rigorous validation is essential to ensure the technical and commercial quality of the generated proposals, building confidence in the new tool and allowing for fine-tuning before expansion to the entire operation.

The fifth and final phase, Expansion and Continuous Monitoring, lasting three months, foresees the gradual expansion of the agent’s use and the creation of performance indicator dashboards. These dashboards will monitor metrics such as drafting time, rework, conversion rate, and margin, consolidating the solution as a permanent asset of the pre-sales area. Continuous monitoring is vital to ensure the continuous improvement of the process, the traceability of results, and adherence to strategic objectives, allowing Ábaco Consulting to fully capitalize on the benefits of intelligent automation.

The investment required for the implementation of the Artificial Intelligence agent was estimated at less than 10% of the annual payroll of the pre-sales team. This cost is considered low, especially because the solution leverages existing technology licenses and infrastructures within the organization, minimizing the need for additional acquisitions. The financial return forecast is short-term, less than six months, demonstrating the high economic viability of the proposal. This rapid return on investment is driven by projected gains in productivity and efficiency, which translate into cost savings and increased revenue generation capacity.

The expected impacts with the implementation of the AI agent are significant and cover several operational and strategic dimensions. A reduction of 57% in the average proposal preparation time and a decrease of 63% in validation time are projected. The average structural cost of the pre-sales area is expected to be reduced by 12%, while operational rework is estimated to fall by 70%. The standardization of information, one of the solution’s pillars, should increase by 40%, ensuring greater consistency and quality in commercial proposals. These efficiency gains free up professionals for more strategic and higher value-added activities.

Although the projected sales value with AI shows a variation of -5% compared to the current scenario, this small reduction is compensated by a projected increase of 50% in the annual capacity for producing commercial proposals. This expansion of capacity, without the need for proportional expansion of the personnel structure, creates conditions to increase the organization’s potential revenue and improve the profitability of commercial opportunities. The reduction in rework and greater reuse of structured knowledge also contribute to decreasing the operational effort required for each new proposal, optimizing resource allocation and maximizing return on investment.

The governance for solution monitoring will be conducted by the Solutions area of Ábaco Consulting, responsible for consolidating and analyzing performance indicators. Executive monitoring will be carried out by the Commercial Director, who will track the evolution of results and adherence to the project’s strategic objectives. At the operational level, the pre-sales team will be responsible for the continuous registration of information and updating the database, ensuring the model’s feedback and the continuous improvement of the process. This governance structure ensures that the solution remains aligned with business needs and that its benefits are maximized over time.

The managerial contribution of this initiative is multifaceted. The adoption of the Artificial Intelligence agent allows for the consolidation of corporate knowledge, reduction of operational inefficiencies, expansion of the pre-sales area’s productive capacity, and strengthening of the technical quality of commercial proposals. The technology does not replace human capital, but enhances it, freeing professionals for more strategic, data-driven action focused on generating customer value. The recommendation, therefore, is the progressive adoption of the AI agent, with short cycles of development, testing, and adjustments, consolidating it as a permanent and strategic asset of the pre-sales area.

In summary, the Business Case demonstrated that the challenges in Ábaco Consulting’s pre-sales area were not related to the team’s technical capacity, but rather to how organizational knowledge was structured, reused, and transformed into consistent commercial proposals. The lack of standardization, the centralization of knowledge in a few professionals, and the lack of integration between information tools directly impacted the proposal preparation time, the operational structural cost, and the financial predictability of projects, as detailed in the analysis presented.

The implementation of an autonomous agent based on Artificial Intelligence emerges as a direct and effective response to these structural limitations. By structuring corporate knowledge, automating operational steps, and supporting the generation of technical and commercial proposals, the proposed solution allows for the transformation of the pre-sales process into a more agile, scalable, and data-driven model. The expected benefits go beyond the reduction of time and operational cost, including increased proposal consistency, improved value communication with clients, and expanded organizational capacity to respond in competitive commercial processes.

The standardization of information and the structured reuse of knowledge will also allow for the reduction of rework, strengthening of process governance, and expansion of the participation of different professional profiles in the development of proposals. The success of the initiative will be measured by objective performance indicators, such as the reduction in the time for proposal development and validation, the decrease in the structural cost of operations, the reduction of rework, and the increase in document standardization. Furthermore, a positive impact is expected on the conversion rate of commercial opportunities and the financial predictability of projects, consolidating Ábaco Consulting as a leader in innovation and operational efficiency.

From a strategic point of view, the initiative represents an important advance in the organization’s digital maturity, positioning Ábaco Consulting more competitively in a market increasingly driven by speed, data intelligence, and operational efficiency. Given the projected results, it is concluded that the implementation of the autonomous Artificial Intelligence agent represents a viable initiative, with an estimated economic return in less than six months and significant potential for value generation for the organization. More than an operational improvement, the project proposes an evolution in the pre-sales area’s operating model, transforming the company’s accumulated knowledge into a structured and scalable asset, ensuring its sustainability and continuous growth.

Bibliographic References:

BOSCHE, A., WANG, J., BOWEN, P. ET AL. (2025). Al Is Transforming Productivity, but Sales Remains a New Frontier. Bain & Company. Disponível em: https://www.bain.com/insights/ai-transforming-productivity-sales-remains-new-frontier-technology-report-2025

JOSHI, D. (2025). Data-Driven Transformation of Technical Pre-Sales Engineering through Al and ML. International Journal of Computer Applications, 187(41). Disponível em: https://ijcaonline.org/archives/volume187/number41/joshi-2025-ijca-925719.pdf

MARR, B. (2019). Artificial Intelligence in Practice: How 50 Successful Companies Used Al and Machine Learning to Solve Problems. Wiley. Disponível em: https://www.wiley.com/en-us/Artificial+Intelligence+in+Practice-p-9781119548213

OHNO, TAIICHI. (1988). Five Whys Toyota Production System: Beyond Large-Scale Production. Productivity Press. Disponível em: https://asq.org/quality-resources/five-whys

Executive summary from the Final Course Work of the Specialization in Executive Leadership and Management of the MBA USP/Esalq

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