Maintaining the tax regularization of a company is essential to avoid problems during audits. Tax and accounting obligations vary according to the adopted tax regime and the type of activity carried out by the business. Meeting these requirements ensures compliance with the legislation and reduces operational risks.

To better understand, it is important to know the different tax regimes, such as Actual Profit, Presumed Profit, Simples Nacional, and MEI. After this understanding, it is possible to delve into the main tax and accounting obligations that every company must follow to maintain its activities legally and organized.

What are tax obligations and why are they important?

Tax obligations are the duties that companies must fulfill before the government, such as paying taxes, submitting declarations, and issuing fiscal documents. Failure to comply with these obligations may result in fines, interest, and even the company's registration in the active debt registry, which hampers access to credit and can compromise the business's reputation.

Some of the main tax obligations include

- Corporate Income Tax (IRPJ): federal tax levied on the company's profit;

- Social Contribution on Net Profit (CSLL): federal contribution aimed at financing Social Security;

- Social Integration Program (PIS): federal contribution aimed at funding unemployment insurance and salary bonus payments;

- Contribution to Social Security Financing (COFINS): a federal contribution aimed at financing Social Security;

- Tax on Industrialized Products (IPI): federal tax levied on industrialized products;

- Tax on Circulation of Goods and Services (ICMS): state tax levied on the circulation of goods and the provision of services;

- Service Tax (ISS): municipal tax levied on the provision of services;

- Declarations: declaration of Federal Tax Debts and Credits (DCTF), Digital Accounting Bookkeeping (ECD), Digital Tax Bookkeeping (EFD), among others;

- Issuance of Invoices:issuance of electronic invoices(NF-e) to document the sale of goods and the provision of services.

Technology as an ally in tax management

Technology has proven to be a great ally in companies' tax management. Enterprise management systems (ERPs) automate processes, facilitate the issuance of invoices, generate accurate reports, and ensure compliance with legislation. Furthermore, integration with accounting systems and e-commerce platforms optimizes time and reduces the risk of errors.

Tips for efficient tax management

It is essential for companies to understand their tax obligations, identify all requirements applicable to their sector, and stay updated on possible legislative changes. The organization of fiscal and accounting documents is also essential, ensuring they are always accessible for consultations and audits.

The use of technology can facilitate thetax management, through software that automates processes and improves tax control. Additionally, having the guidance of an accountant or tax consultant contributes to legal compliance and risk reduction. Finally, a well-structured tax planning allows for optimizing the tax burden and avoiding financial surprises.

In recent years, the way B2B startups grow has changed drastically. The increase in customer acquisition cost (CAC), the saturation of paid channels, and the growing market distrust have made one problem clear: the traditional growth model is no longer sufficient. In this context, the concept of Reputation-Led Growth (RLG) emerges, a strategy that positions reputation as the main lever for growth and revenue acceleration.

Reputation-Led Growth is a growth model in which the brand's credibility, authority, and trust drive acquisition, conversion, and retention. Instead of just investing in performance marketing and aggressive SDRs, startups that implement RLG build an ecosystem where customers come because of the trust generated in the market.

While Brand-Led Growth (BLG) focuses on building a strong and memorable brand identity, RLG's growth comes from strategic influence. Companies that dominate this model not only sell a product or service but also become references in their sector, reducing the buyer's perceived risk and shortening sales cycles.

Startups following the Reputation Led Growth model do not rely heavily on paid advertising or purchased traffic. Instead, they gain visibility through strategic PR, thought leadership, and social proof. In the traditional model, the sales funnel begins with paid traffic, lead generation, and active outreach. At RLG, clients arrive with more maturity and fewer objections, as the company's reputation has already been validated in the market, which reduces the contract closing time because they become the safe and obvious choice for their clients. Furthermore, a strong reputation positively impacts retention.

How to accelerate revenue with Reputation-Led Growth?

CMOs of B2B startups need to understand that reputation is not just an intangible asset – it is a revenue accelerator. The implementation of an RLG strategy in practice is based on the following pillars:

1. Turn your executives into strategic spokespersons
The reputation of a startup often begins with its leaders. CEOs and CMOs need to be active in the market, sharing knowledge and leading discussions. LinkedIn, industry events, and specialized media are essential channels for this.

2. Leverage PR and earned media to generate social proof
Consistent presence in strategic vehicles builds trust. The B2B client needs external validation to reduce risks.

3. Build credibility through strategic partnerships
Startups that partner with solid players instantly gain more trust in the market.

4. Build a brand advocate ecosystem
Satisfied customers are the best acquisition channel. At RLG, reputation spreads through digital word of mouth and strategic recommendations. Customer testimonials and impact cases published are more powerful than any performance campaign.

Reputation-Led Growth is not a passing trend. In the financial market, for example, where trust is everything, startups that dominate this game attract customers faster, sell with less friction, and build barriers against the competition. CMOs who understand this stop being just marketing managers and become growth strategists, using reputation as a real engine of scale.

The question now is no longer "how much are we investing in branding?", but rather "how are we ensuring that the market trusts our brand even before the first commercial contact?"

How many times have you been moved by a marketing campaign? People connect with people, something that is increasingly present in corporate strategies to strengthen this emotional connection with their customers. When a brand manages to turn its message into an engaging story, it ceases to be just an option in the market and becomes part of the audience's life, something that, if well executed, can bring excellent results for the brand's reputation and sales.

The modern consumer is more informed, more demanding, and has less patience for brands that only "push" products. We live in an era of personalization, purpose, and transparency, where brands that manage to humanize their communication and offer authentic experiences certainly lead the way. This, combined with the growth of artificial intelligence and automation, has made the human factor even more valued, highlighting the importance of combining technology with sensitivity in order to create memorable experiences that generate competitive advantages.

In this scenario, many companies create such strong emotional connections that their customers become true fans, connecting not only with the products or services offered but also with the human beings behind it all. According to neuromarketing studies, as evidence of this, campaigns with purely emotional content performed about twice as well as those with only rational content. But a good storytelling goes beyond just this emotional connection.

When well constructed and paired with a well-told narrative, this connection awakens desire and need, making the customer identify with the brand's values, feel that it understands their pains and desires, that it speaks to them, and realize that there is a greater purpose behind that communication – something that can be decisive in generating a conversion.

Brands that create strong feelings feel the impact directly on sales. After all, loyal consumers tend to buy more often and defend that company, recommending it to family and friends. Furthermore, this loyalty helps reduce sensitivity to discounts and promotions, as you pay for what you perceive as valuable. Customers who feel understood and valued are also less likely to switch to competitors, thereby improving the retention rate.

Now, how can companies strengthen this emotional connection and achieve all these benefits? Start by getting to know your client deeply. Use data to understand your pains, desires, and behaviors. The more personalized the content is, the greater the chance of creating a real connection.

With this information in hand, craft an authentic storytelling. I tell real stories, with characters, challenges, and emotions. A good narrative engages because it reflects situations from the audience's life, evoking empathy and identification. A tip for building this is to use consumers' emotional triggers; after all, emotions like belonging, nostalgia, overcoming, and empathy, when used with sensitivity, make the message more impactful.

Create memorable multichannel experiences, regardless of the platform invested in. The campaigns must reflect the brand's emotional tone, and the experience needs to be smooth, coherent, and delightful at all touchpoints. All of this must be aligned with a very clear purpose and value, as modern consumers value brands that take a stand. Transparency, inclusion, sustainability, and social responsibility are topics that create engagement when communicated truthfully.

Gather as much information as possible about your clients' behaviors and needs, understand the triggers that work best for this audience, measure the results, and understand the impacts of this with conversion metrics. Brands that manage to balance these two universes (reason and emotion) with strategy and sensitivity will not only sell more but also achieve something even more valuable: emotional loyalty.

Artificial Intelligence (AI) is often seen as a revolutionary technology, capable of providing efficiency, accuracy, and opening new strategic opportunities. However, as companies benefit from the advantages of AI, a critical and sometimes overlooked challenge also arises: algorithmic fairness. Hidden biases in these systems can compromise not only the efficiency of business decisions but also lead to significant legal, ethical, and social consequences.

The presence of algorithmic biases can be explained by the very nature of AI itself, especially in machine learning. Models are trained with historical data, and when this data reflects biases or social distortions, the algorithms naturally end up perpetuating these biases. In addition to biases in the information, the algorithm itself can cause an imbalance in the weighting of factors, or in the data used as proxies, that is, data that replace the original information but are not ideal for that analysis.

An emblematic example of this phenomenon is found in the use of facial recognition, especially in sensitive contexts such as public security. Several Brazilian cities have adopted automated systems in an attempt to increase the effectiveness of police actions, but analyses show that these algorithms often make significant errors, especially when identifying individuals from specific ethnic groups, such as Black people. Research by MIT's Joy Buolamwini found that commercial algorithms have error rates above 30% for Black women, while for white men, the rate drops dramatically to less than 1%.

Brazilian legislation: more rigidity in the future

In Brazil, in addition to the General Data Protection Law (LGPD), the Legal Framework for AI (Bill No. 2338/2023) is also under consideration, which establishes general guidelines for the development and application of AI in the country.

Although not yet approved, this bill already signals rights that companies must respect, such as: the right to prior information (informing when the user is interacting with an AI system), the right to explanation of automated decisions, the right to contest algorithmic decisions, and the right to non-discrimination due to algorithmic biases.

These points will require companies to implement transparency in generative AI systems (for example, clearly indicating when a text or response was machine-generated) and audit mechanisms to explain how the model arrived at a particular output.

Algorithmic governance: the solution to biases

For companies, algorithmic biases go beyond the ethical sphere; they become significant strategic issues. Biased algorithms have the potential to distort key decisions in internal processes such as recruitment, credit granting, and market analysis. For example, a branch performance analysis algorithm that systematically overestimates urban areas at the expense of peripheral regions (due to incomplete data or biases) can lead to misdirected investments. Thus, hidden biases undermine the effectiveness of data-driven strategies, causing executives to make decisions based on partially incorrect information.

These biases can be corrected, but they will depend on an algorithmic governance framework, focusing on the diversity of the data used, transparency of processes, and the inclusion of diverse and multidisciplinary teams in technological development. By investing in diversity within technical teams, for example, companies can more quickly identify potential sources of bias, ensuring that different perspectives are considered and that errors are detected early.

Furthermore, the use of continuous monitoring tools is essential. These systems help detect algorithmic bias drift in real time, enabling quick adjustments and minimizing negative impact.

Transparency is another essential practice in bias mitigation. Algorithms should not function as black boxes, but rather as transparent and explainable systems. When companies choose transparency, they gain the trust of customers, investors, and regulators. Transparency facilitates external audits, encouraging a culture of shared responsibility in AI management.

Other initiatives include adherence to frameworks and certifications for responsible AI governance. This includes creating internal AI ethics committees, establishing corporate policies for its use, and adopting international standards. For example, frameworks such as ISO/IEC 42001 (artificial intelligence management), ISO/IEC 27001 (information security), and ISO/IEC 27701 (privacy) help structure controls in the data processes used by generative AI. Another example is the set of best practices recommended by the NIST (National Institute of Standards and Technology) of the USA, which guides algorithmic risk management, covering bias detection, data quality checks, and continuous model monitoring.

Specialized consulting plays a strategic role in this scenario. With expertise in responsible artificial intelligence, algorithmic governance, and regulatory compliance, these companies help organizations not only avoid risks but also turn equity into a competitive advantage. The work of these consultancies ranges from detailed risk assessments to the development of internal policies, including corporate training on AI ethics, ensuring that teams are prepared to identify and mitigate potential algorithmic biases.

In this way, mitigating algorithmic biases is not just a preventive measure, but a strategic approach. Companies that care about algorithmic fairness demonstrate social responsibility, strengthen their reputation, and protect themselves against legal sanctions and public crises. Impartial algorithms tend to provide more accurate and balanced insights, increasing the effectiveness of business decisions and strengthening organizations' competitive position in the market.

By Sylvio Sobreira Vieira, CEO & Head of Consulting at SVX Consulting