A guide for businesses to scale generative AI

The huge expansion of artificial intelligence with the advent of generative AI is forcing AI leaders to review their data platforms. The companies that see this early will be the ones that can position themselves competitively in a data-driven future.

We take a look at the steps and common pitfalls in navigating the challenges that new developments will pose to businesses and the keys to implementing a scalable AI framework.  

Scalability AI: Initial Barriers

Despite its short life, generative AI has already allowed data and artificial intelligence experts to work on many cases, revealing the incalculable value of this technology and the emergence of challenges to scale it.

Proper data management remains one of the main barriers to creating value in GenAI projects. In fact, according to a McKinsey survey, 70% of respondents have experienced difficulties integrating data into AI models, ranging from issues with data quality, defining processes for data governance, and the availability of sufficient training data.

Many companies still do not fully understand how to develop the data capabilities to support generative AI cases at scale and how to use them to improve data practices.  

Pillars to drive large-scale AI

The path to scalable and effective AI rests on 3 main pillars: 

• Community: The development of an AI-centric business within an organization is critical. It is important to foster a culture of collaboration and knowledge sharing. This fosters innovation and accelerates the learning curve for AI applications across the organization.  
• Common ground: Emphasis should be placed on building comprehensive and integrated AI platforms. This approach can provide a broader view of potential use cases and improve the combined utility of AI tools across different business functions.  
• Coordination: it is essential to identify and prioritize high-impact AI initiatives, so strategic alignment ensures that each company’s business objectives are clear and tangible results can be achieved.  

How to achieve scalable AI

As mentioned above, the first and most important barrier companies face when implementing an AI project is getting good and sufficient data.

Introducing poor data into GenAI models poses great risks, ranging from poor results, costly solutions, cyber breaches, and loss of user confidence. Therefore, inaccuracy of results is the biggest risk a company can encounter in the use of this technology.

Traditional methods of ensuring data quality are not sufficient, and new ways of improving and extending source data must be considered.  

Getting data from better and more reliable sources

It is very common to encounter difficulties in managing growing unstructured data sets. Combining it with structured data increases the possibility of errors, as it is more complicated to code it so that data processes can be easily replicated.

The good news is that tools have evolved to manage the relationship between different types and sources of data. But even though data engineers understand the relationship between data sets, they need to assign different methods to interpret that data based on different attributes, such as data format. This is a major challenge as companies integrate formats into systems that are becoming increasingly complex.  

Fortunately for businesses, multimodal models are now sophisticated enough to analyze more complex document types with disparate data formats, such as extracting tabular data from unstructured documents.

However, accuracy issues require constant review, which is time-consuming if done manually. Therefore, automated evaluation methods, mechanisms to manage version control and model consistency must be implemented.  

Create data that are not available

Some more advanced AI use cases are difficult to implement because the necessary data is hard to obtain and process, which is often a problem in industries that have very strict data security standards.

To overcome this challenge, data engineers have the possibility of being able to manually generate a file to test the efficiency of a use case. However, the process can be time-consuming and inefficient.

This is why data and AI leaders invest in AI tools to generate synthetic data as test data or to produce new values based on column descriptions and table context, allowing them to create new data or make revisions to existing data.  

Focusing on data governance and data security

The starting point for generative AI is to lay the foundations for trust in its design, its function, and how the results are used. Therefore, to achieve trusted AI, one must start with data governance and specific considerations around security.

GenAI-specific governance and usage policies help manage the potential risks of the technology’s capabilities embedded in enterprise resource planning, customer relationship management, and other business applications.

To add greater security, look beyond the systems themselves. The network architecture, security policies, data governance, and compliance framework must be evaluated in light of the new risks.

In addition, a good option is to consider third-party risk management and continuous monitoring of how governance and risk practices interact with those of the enterprise. This will help to close gaps and achieve a much more secure environment.  

Designing a strategy to speed up return on investment

The best AI solution is the one that can scale. Therefore, it is important to create patterns that can be applied across business processes. Unlike traditional AI, generative AI does not require a new model for each task, making it easy to deploy the same GenAI model in many areas quickly.

To achieve the greatest return on investment, it is best to focus on the core processes of each business.  

Implementing use cases that create value

The first PoCs should be aligned with each company’s strategy, with a focus on core business processes, organizational readiness, and repeatability.

When these early use cases are in place, data and AI professionals can study those that have positive and notable results, and thus create a working model in which the results of GenAI models can be refined and give way to a mechanism to monitor and customize the internal workings of the model.  

Scalable AI Solutions

One of the great advantages of generative AI is that its scalability occurs and transforms much faster than we are used to.

The key to achieving rapid ROI and powerful transformation through GenAI is to focus on experimentation, scale, and security.

At Plain Concepts we help you design your strategy, secure your environment, choose the best solutions, close technology and data gaps, and establish rigorous oversight that achieves accountable AI. You can achieve rapid productivity gains and build the foundation for new business models based on hyper-personalization or continuous access to relevant data and information.

We have a team of experts who have successfully applied this technology in numerous projects, ensuring the security of our customers. We have been bringing AI to our clients for more than 10 years and now we propose a Framework for the adoption of generative AI:  

• Unlock the potential of end-to-end generative AI.
• Accelerate your AI journey with our experts.
• Understand how your data should be structured and governed.
• Explore generative AI use cases that fit your goals.
• Create a tailored plan with realistic timelines and estimates.
• Build the patterns, processes, and teams you need.
• Deploy AI solutions to support your digital transformation. 

Preparing your company to successfully adopt generative AI is at the core of our framework, where we will cover 4 main pillars: strategy and governance of data and your privacy, security and compliance, reliability and sustainability, and responsible AI. This will help you avoid the risk of projects never making it to production.

Don’t wait any longer and start scaling your GenAI solutions!