AI for companies has never been talked about more. More than 6 out of 10 companies already use generative AI on a regular basis – almost double the number just ten months ago. It’s easy to see the appeal: like any new technology, AI brings promises of lower costs, increased revenues and a whole new world of possibilities.
But there’s another side to the coin, which rarely makes the headlines. The number of companies that have implemented AI without a clear strategy, the projects that failed before generating value and the initiatives that, from the outset, had a short shelf life. All in all, between 70 and 80% of AI projects fail to generate the expected results.
For the adoption of AI to be truly effective and simplify day-to-day business, solid planning and a well-defined strategy are essential. In this article, find out what AI for business really is, what its advantages and risks are, why projects fail and follow a step-by-step guide to start implementing it successfully in your organization.
What is AI for business?
AI for business refers to the application of artificial intelligence to automate processes, analyze data, optimize decisions and create new business opportunities. It involves everything from chatbots and virtual assistants to improve customer service, to advanced machine learning models that predict market trends or detect fraud. AI can be used to reduce costs, increase efficiency and boost revenues, but its success depends on strategic implementation, with clear objectives and effective integration into existing business processes.
A real example of this transformation is the case of Transportes Beleza, which was facing challenges in generating budgets for international moves. To solve this problem, Softingal developed an innovative application based on RAG (Retrieval-Augmented Generation) and ChatGPT, capable of generating precise quotes based on photographs of the furniture to be transported. This solution reduced errors, sped up the process and improved the customer experience, demonstrating how AI can simplify operations and increase business efficiency.
What are the advantages of AI for companies?
Artificial Intelligence is a real competitive advantage for companies that know how to apply it. These are the concrete benefits.
Faster, more informed decisions
AI analyzes massive volumes of data in real time, identifies patterns, suggests actions in seconds and anticipates trends. The result? Faster decisions, based on hard data and not just intuition or manual reports.
Intelligent automation
AI for business can take over time-consuming and repetitive processes to free up employees for higher-value tasks. For example, companies that receive hundreds of emails a day can use AI to automatically categorize and respond to simple requests, which reduces response time and increases customer service efficiency.
Personalization at scale
One of the great challenges for companies is to offer personalized experiences without losing efficiency. For example, AI allows e-commerce platforms to analyze each customer’s buying behavior and recommend products that really interest them, which increases conversion rates and loyalty.
Enhanced security and fraud detection
Cybersecurity benefits enormously from AI, since algorithms can analyze behavior patterns and identify suspicious activity in real time. From cyber attacks to financial fraud attempts, AI is an extra layer of protection for any company.
Why do AI projects fail in companies?
Artificial intelligence promises to transform businesses, increase efficiency and boost revenues, but the reality doesn’t always live up to expectations. Many AI projects fall by the wayside before bringing any real return, whether due to implementation failures, lack of strategic alignment or technical problems.
If your company wants to avoid wasting time and resources, learn about the most common mistakes that lead to AI projects failing:
- Lack of a clear objective – Implementing AI “just because” doesn’t work. Without a well-defined purpose, projects become costly and ineffective.
- Poor quality data – AI depends on good data to generate good results. If the data is incomplete, skewed or out of date, the AI model will be unreliable.
- Unrealistic expectations – AI is not magic. Many companies expect immediate results without considering the time needed to train, test and integrate the models.
- Lack of integration with existing processes – Efficient AI needs to fit into current workflows, not create more complexity.
- Undefined ROI – If the company doesn’t know how to measure the success of the project, it will be difficult to justify the investment.
- Lack of specialized know-how – Without the right teams to manage, interpret and adjust the models, an AI project can quickly stagnate.
- Underestimation of risks and legal compliance – From data privacy (GDPR) to cybersecurity, neglecting regulations can lead to legal and reputational problems.
How to implement AI for companies?
To ensure that the implementation is successful and brings tangible results, it is essential to follow a structured process.
1. Define clear objectives
Before you start, the first question you need to answer is: what problem do you want to solve? AI can bring numerous advantages, but its implementation must be guided by strategic objectives. Some companies want to improve customer service, others are looking to reduce operating costs, while others want to gain more accurate insights into consumer behavior.
2. Evaluate the available data
AI depends on data to work properly. The more complete and organized your data is, the more effective its application will be. That’s why, before adopting any AI-based technology, it’s essential to understand what data your company already has, how good it is and how it can be used.
If your data is scattered across several systems or is inconsistent, you may need to invest in your organization first. Many companies fail to implement AI because they don’t have reliable databases, and end up with wrong predictions or ineffective models.
In addition, it is important to ensure that data respects privacy standards, especially with regulations such as the GDPR. Misuse of data can lead to heavy fines and undermine customer trust.
3. Choose the right tools and partners
Not all companies need to develop AI solutions from scratch. There are a number of affordable tools that make it possible to incorporate AI without the need for large investments.
At Softingal, we have several decades of experience in identifying and implementing the tools best suited to the specific needs of each business.
4. Start with a pilot project
Implementing AI for businesses doesn’t have to be a radical change. The ideal is to start with a pilot project, where you can test the technology in a specific area.
This pilot project serves to validate hypotheses, measure the real impact of AI and identify necessary adjustments before investing on a larger scale. Choose a specific application, define success metrics and monitor the results.
For example, if you want to improve the customer experience, you could start with a chatbot to answer frequently asked questions. If your goal is to optimize stock management, you could test a predictive model to forecast product demand.
5. Train the team
Technology only works well if people know how to use it correctly. One of the biggest challenges in adopting AI is ensuring that teams are prepared to work with these tools and have confidence in their potential.
Organize workshops, demonstrate success stories and involve the team in the implementation from the start. If people understand the value of AI and know how to use it, adoption will be much easier and more effective.
6. Create a culture of continuous improvement
After implementing a pilot project, AI needs to be constantly adjusted to continue delivering good results.
Establish performance metrics and regularly assess whether the solution is achieving the defined objectives. If you’ve implemented AI to optimize customer service, analyze metrics such as average response time, satisfaction rate and number of issues resolved without human intervention.
If the results are not what you expected, you may need to train the AI models better, adjust the input data or redefine the objectives.
Hold regular meetings to discuss adjustments and improvements and collect feedback from users (employees or customers) to identify points for improvement.
7. Expand the use of AI
If the pilot project has been successful, it’s time to expand AI to other areas of the company. Based on the initial results, you can extend its application to more complex and strategic processes.
For example, if you started with AI for customer service, you can now explore the automation of administrative tasks, sales forecasting or the analysis of market trends. The key is to identify where AI can continue to bring value.
AI for business in practice: the case of Transportes Beleza
Transportes Beleza contacted us to simplify the quoting process for their international removals services. To do this, we created something truly unique: an application that uses Deep Learning and RAG (Retrieval-Augmented Generation) to turn simple photographs of furniture into instant, accurate quotes.
Adopting this technology has completely redefined the budgeting process. It has reduced errors by 50% and saved 70% of the time invested by the client. By simplifying and speeding up quoting, Transportes Beleza has eliminated bureaucracy and improved the entire customer experience.
AI for companies: count on Softingal to take your business further
There’s no doubt that AI for business carries great disruptive potential. Softingal is ready to help your organization explore this new territory and guide you every step of the way – from choosing the right tools to implementing solutions that really add value to your business. Contact us and find out how we can help you reach the next level with artificial intelligence.
