One of the biggest paradigm shifts in the business world in recent decades has been the instrumentalization of AI. More than 71% of companies have implemented or plan to implement AI to automate or optimize tasks.

As good as it looks in theory, not all organizations are achieving the results they expected, and most are still struggling to implement AI effectively. The problem? Companies often lack a well-grounded strategy.

Renaiss is familiar with this issue, and focuses on thoroughly studying the challenges businesses face in implementing AI, in order to offer the right tools for  companies to carry out effective AI integrations. For Renaiss, business implementation of AI systems does not equate to adopting  the newest technological tool: the process requires an in-depth strategic vision that is aligned with business objectives. AI is not a magical solution to a company’s whole range of problems, but rather a set of capabilities and tools that, when properly applied, may help businesses achieve their goals. In order to properly make proper use of the game-changing tool AI constitutes for business, meticulous initial planification -through a well-defined AI implementation strategy, is required.

In this article, we will explore the basic steps organizations should follow in order to create a practical AI implementation strategy. From  target definition to data preparation and integration with existing systems, a solid strategy is a prerequisite for a successful implementation. Let’s break it down into seven steps!

1. Problem and Target Definition

As experts in business AI, Renaiss considers it essential for companies to clearly define the operational problems they seek to resolve, as well as the specific targets they want to achieve before embarking on any AI project.

One of the main reasons why AI projects fail to yield proper results is the lack of alignment between the technology and the actual business problems. Without a clear understanding of both-the problem to be addressed and the technology, it is easy to waste time and resources on inefficient technological solutions. A study by SoftServe revealed that 80% of companies implementing AI fail to achieve their goals due to either poor planning or to a lack of clarity regarding the problems they are trying to solve.

In this regard, the first step is to identify the business processes that would benefit the most from AI. From process optimization, improving customer experience, reducing costs, or creating new products or services, AI applications are countless. Such an analysis must be based on concrete, reliable, and recent data that allows for the evaluation (and if possible, quantification) of both the magnitude of the problem to be solved and the potential role of AI in addressing them.

Once the problem is identified, clear and measurable objectives need to be set through specific KPIs, which will guide the implementation. These objectives must align with the company's overall vision and mission, and should be as practical and specific as possible. A common mistake businesses make is to set abstract goals such as “improving efficiency,” instead of concrete quantifiable targets like “reducing processing times by 20% over the next six months.”

2. Data Preparation

The effectiveness of an AI system is proportional to the quality and relevance of the data processed by it. The preparation of this data is a crucial process to consider in any AI implementation strategy. This process includes collecting, cleaning, and structuring data so as to ensure it is accurate and relevant. Without proper data preparation, even the most sophisticated algorithms can produce inaccurate or unhelpful results, negatively affecting the AI’s efficiency.

A report from Xeridia supports the importance of the data preparation phase: 80% of the time spent on AI projects is dedicated to data preparation and cleaning. Additionally, proper data organization and usage is especially relevant to ensure compliance with the General Data Protection Regulation (GDPR).

One of the first steps in the data preparation process is data cleaning, which involves correcting errors, eliminating duplicates, and ensuring consistency across all systems. Another important process is the normalization and structuring of data, which often comes from various sources, such as databases, spreadsheets, or applications, and may exist in different formats or languages. Organizing and structuring this data into a common, machine-readable format is essential for its proper analysis and processing.

Renaiss’ innovative architecture, enables the automation of both the data cleaning and normalization and structuring processes through “Small Language Models” or “SLMs”. This way, Renaiss technology greatly facilitates and optimizes one of the most complex and costly processes in AI implementation.

Finally, it is imperative for companies to establish a proper data governance strategy that ensures data is accessible, but also secure and compliant with applicable privacy policies and regulations. Defining clear policies on who can access the data, and how or when it can be collected and used among others, helps prevent unlawful practices regarding data usage, while preserving data quality in the long term.

3. AI Model Selection and Development 

With the wide spectrum of AI models currently available on the market, one of the main challenges to address in enterprise AI implementations is selecting the AI model that better aligns with the project’s objectives. Architectures like Renaiss RenForce offer companies key assistance in facilitating these decision-making processes.

Choosing the appropriate AI model will directly impact the success of its implementation within a company’s processes, as well as the results it is able to achieve. It is imperative to consider different types of AI models (such as neural networks, decision trees, or supervised and unsupervised learning models) in order to select those that best fit the organization’s identified needs.

In this sense, a 2021 study by MIT Sloan Management Review revealed that 85% of companies that are able to successfully implement AI choose their models based on the complexity of the problem they are trying to solve, rather than simply selecting the most technologically advanced model.

Success in enterprise AI implementation is not about choosing the most acknowledged or innovative model, but rather about selecting the one that best suits the specific needs and limitations of a company, thus optimizing resources and ensuring due results. Renaiss has the knowledge and experience required to provide top-tier assistance in the making of these decisions, but most importantly, it counts with its own proprietary and pioneering model orchestration system which enables automatic AI model selection on the basis of each specific given prompt or task. 

4. Integration with Existing Systems

The integration of AI solutions with an organization’s existing systems is another phase that will determine the success or failure of an AI project. This integration does not only involve technical connection between different technological platforms and tools, but also requires duly aligned operational and human support . Some key aspects to consider are the following:

• Technical Integration Challenges

A common challenge companies face is aversion to change. Many organizations rely on legacy systems that have been in use for years, and employees may be reluctant to adopt new technologies —especially if these are unfamiliar to them. To address this reluctance, it is essential for the company to foster an organizational culture that acknowledges and promotes innovation and evolution, relating both the company at a technological level and its employees at the professional one. Training and communication are key: employees should not perceive AI as a threat, competition, or a further increase of their efforts , but rather as a tool at their disposal and for their benefit, which will assist them in their operations, improving overall efficiency, easing task development, and optimizing time and resources.

• Repository Integration

From a technical standpoint, integrating AI systems with existing architectures is one of the main challenges in AI implementation. In many cases, one of the major technical hurdles companies and their IT teams face is the existence of a wide variety of data repositories of different natures -some of which are not under direct control of the teams. Business AI integration requires connecting AI systems to the company’s various data repositories. Typically, “APIs” or “Application Programming Interfaces” are used to carry out this connection, allowing different systems (like AI or repositories) to communicate with each other. To streamline AI integration with company data repositories, Renaiss has a selection of APIs available, which are compatible with commonly used business data repositories, which significantly speeds up this complex task.

• Monitoring and Adjustment

Once the integration has been completed, it is crucial to monitor its performance and make adjustments as needed. Continuous monitoring and evaluation allows companies to identify those areas which may require improvement, and to consequently optimize the interaction between AI and the existing systems. Implementing clear metrics that are able to reliably measure the performance of the integration is essential to ensure that AI delivers the desired impact.

• Interdepartmental Collaboration

Collaboration between different teams within a company is also crucial for successful integration of AI systems. The IT, operations, and business teams must work together to ensure that the AI solutions are aligned with the company’s strategic goals. This collaborative approach facilitates technical integration while helping to ensure that AI solutions efficiency is maximized throughout the organization.

5. Ethical Considerations and Regulatory Compliance

At Renaiss, addressing legal and ethical concerns related to AI implementation is considered a priority. Compliance with regulations such as the European Artificial Intelligence Act (AI Act) or the GDPR is of the utmost importance, and must be addressed in relation to both the design and development of an AI system, as well as in its implementation and use. In this regard, ethical considerations such as transparency, proper data usage, and bias mitigation in models must be observed at all times. Decisions made by AI algorithms should be fair, equitable, and responsible.

From a legal standpoint, it is necessary to evaluate the compliance of AI systems with applicable regulations, which involves analyzing both the technology to be used in a specific integration and the intended purpose of the system within an organization. This will allow for proper risk analysis and management -based on the classifications set forth on the AI Act, which establishes the requirements that developers and companies must comply with. Renaiss actively collaborates with companies to ensure the compliance of their implementations with applicable regulations.

Another key aspect, both from a legal and ethical perspective, is data privacy. AI models often process large amounts of personal data, making it necessary to ensure that technological and business practices align with applicable data protection regulations, such as the GDPR.

Currently, the ethical conflicts that may arise from improper AI implementation or use are widely debated -conflicts that can also have legal consequences. One of the most relevant examples is the need for bias mitigation. AI models can reproduce or even amplify existing biases within the used data, leading to unfair or discriminatory decision-making. According to a study by the AI Now Institute at New York University, organizations that prioritize identifying and correcting biases in their models significantly improve the fairness of their outcomes, which in turn enhances trust in their solutions and ensures compliance with various applicable regulations.

Therefore, ensuring regulatory compliance and adherence to ethical considerations not only protects the company from legal risks but also fosters trust among users and clients, ensuring that AI solutions are socially responsible and equitable.

6. Planning for Scalability and Sustainability

The scalability and sustainability of an AI system are characteristics that  Renaiss considers as the cornerstone of a successful long-term implementation, thus both must be anticipated on any proper AI implementation strategy. A strategy that considers the scalability and sustainability of an integration enables the organization to manage any potential increases in operational complexity, which could derive from not only from technological evolution but also from potential business growth.

In terms of technological infrastructure, many companies choose to adopt cloud infrastructures to scale their AI solutions efficiently. According to an IT User study, by 2025, 74% of organizations implementing AI will use cloud platforms due to their flexibility and scalability. Cloud platforms enable companies to handle larger volumes of data and more complex AI models without the need for constant investment in additional hardware.

At the same time, it is crucial to identify the impact that AI implementation will have on a company’s human resources. As AI adoption expands within the organization, more specialized personnel will be needed -such as AI specialists, machine learning engineers, or IT teams. Continuous training together with the hiring of specialized talent are essential to sustain long-term technological growth.

Lastly, strategic partnerships with technology providers, consultants, and research organizations are useful tools for maximizing the benefits of AI at the business level. Establishing key partnerships may allow companies to access the latest innovations and technologies in the industry, driving technological growth without furthering the need for internal developments.

A proper integration of AI systems often leads to technological and commercial growth. Proper planning for the scalability of AI systems ensures coverage before any potential needs that may arise from this growth, ensuring that AI systems remain flexible, efficient, and sustainable in the long term.

7. Monitoring and Continuous Improvement

Once an AI system has been implemented, Renaiss strongly recommends monitoring and evaluating its performance by using tangible indicators based on the goals initially set in the AI implementation strategy. For this, clear KPIs or metrics should be defined in order to analyze AI performance in key areas, such as result accuracy, operational efficiency, cost reduction, or customer satisfaction among others.

In addition to monitoring system performance, it is recommended to establish an ongoing feedback loop. This involves regularly reviewing the results produced by AI systems, identifying areas for improvement, and adjusting the systems or processes as required. Such an iterative approach ensures the long-term effectiveness of AI while simultaneously allowing for system optimization as data, business needs, and technology evolve.

The monitoring and feedback processes enable the establishment of a continuous improvement cycle that facilitates evolution and adaptiveness in the business environment -such as to new regulations, market demands, or technological advancements. This way, it is not only ensured that the AI is effective at the time of its implementation, but also that it is able to evolve with the company, providing sustainable benefits over time.

Conclusion

Successfully implementing enterprise AI requires prior strategic planning, constant evaluation, and continuous improvement. From selecting the most appropriate AI model for a specific project to integrating them with existing systems or adapting the human resources, each step must be aligned with the company's objectives. The ability to continuously evaluate and adjust solutions is built on a solid initial strategy, and ensures that technology addresses immediate problems while providing value that is sustainable in time and across multiple scenarios.

If your company is ready to leverage the benefits of AI, contact Renaiss. Our team of experts will guide you through the process: from initial strategic planning to project implementation and monitoring, we work to ensure your systems are aligned with tangible goals and prepared for the future. Unlock all your company's potential with Renaiss!