Generative AI for business: a growth potential?

Understanding generative AI

To benefit from generative AI, business leaders first need to understand what it is.
Generative AI stands as a sophisticated technology that utilizes neural networks, specifically generative models, to produce data and content that closely resemble human-generated materials. Among the most prevalent types of generative models are Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Thanks to these models, generative AI is seen by the market as a versatile and potent tool within the domain of artificial intelligence.
However, it is imperative to understand that generative AI represents merely one facet of artificial intelligence. While its primary focus is around the creation of content, it delineates itself from other AI functionalities, such as data analysis and classification. These other AI categories are designed to decipher data and provide insights, while generative AI's principal function lies in the creation of content.
One common misconception pertains to generative AI being synonymous with chatbots. To differentiate between these two entities, it is important to know that a chatbot is a specific application that may incorporate certain components grounded in generative AI technology. Which means, chatbots can also be constructed utilizing alternative technologies, such as robotic process automation. Similarly, generative AI encompasses a broader spectrum of use cases and applications.

How generative AI brings value to the business 

Below are examples of Generative AI Use Cases that an organization can benefit from: 

•    Content  Generation: remain competitive by creating original content based on existing data.
      - Text generation
      - Image generation

•    Creative Design: AI algorithms help to create inventive designs.
      - Art and music generation
      - Graphic design

•    Data Augmentation: help your team to understand and process high volume of data.
      - Improving datasets
      - Synthetic data generation

•    Personalizing Experiences: provide a tailored content to a specific audience.
      - Chatbots : for a personalized customer experience
      - Targeted advertisement based on patterns in your customers’ behaviour

Key points to successfully integrate generative AI into your business

Nevertheless, determining the suitable use case for the integration of generative AI within an organization should be underpinned by a multifaceted evaluation, aimed at securing a genuinely effective return on investment. Several critical factors necessitate consideration in this regard:

•    Digital Maturity
The successful adoption of generative AI hinges upon its alignment with the digital maturity of the organization. This harmony ensures that users within the organization will not only embrace the application but actively employ it to enhance their workflows. A phased approach, introducing generative AI incrementally, can facilitate seamless integration while safeguarding against resistance or underutilization.

•    Data Maturity
As previously expounded, the proficiency of generative AI is intrinsically tied to the quality and quantity of data at its disposal. A substantial quantity of high-quality data is indispensable for refining AI models. It is paramount to acknowledge that biased data begets biased AI, which, in turn, can lead to the misinterpretation or misuse of information. Data quality and diversity, alongside rigorous data governance, must underpin any successful generative AI initiative.

•    Priority
The prioritization of use cases for generative AI deployment is a critical decision. Organizations must decide whether to initiate its application in business-critical processes or to first capitalize on its potential for reducing human resource costs. This decision shapes the scope and investment of the adoption project and significantly influences the ultimate value that an organization can derive from it. Clearly defining the priority use cases ensures that generative AI aligns with the overarching strategic goals of the organization.

•    IT Environment
The cloud environment provides a fertile ground for the scalability and traceability of generative AI applications. This is pivotal in boosting the effectiveness and adoption rate once the application is introduced to the organization. Leveraging cloud infrastructure is instrumental in ensuring the smooth operation of generative AI systems and their seamless integration into existing IT frameworks.

•    Cloud Adoption
It is imperative to recognize that the adoption of generative AI within a cloud environment necessitates meticulous consideration during the design phase of the cloud infrastructure. Proper integration is pivotal in ensuring that generative AI applications can seamlessly coexist within the broader cloud ecosystem, offering optimal performance, scalability, and efficiency.

Generative AI can significantly benefit from cloud infrastructure by leveraging the scalability, computational resources, and flexibility provided by cloud platforms. To find out more about the convergence of generative AI and cloud infrastructure, read our article "Azure services for generative AI ".

Challenges and considerations when adopting generative AI within a company strategy.

However, it is noticeable that certain challenges of adopting generative AI should be taken into consideration. 
Generative AI presents significant ethical challenges, primarily related to content generation and potential misuse: 

•    Biased content
These models can inadvertently produce biased, offensive, or harmful content, posing risks to individuals and communities. More often, data silos resulted by the lack of data integrity and the knowledge of existing datasets lead to the biased models that cannot provide adequate and updated information.  

•    Privacy and accessibility
Concerns exist in regards to the privacy issues as models might inadvertently reveal sensitive information contained in the training data. The accessibility of data and the AI should be a common decision between DPOs (Data Protection Officers), IT Managers and data scientists, ideally taken before the actual development of the AI. 

•    Integration and adaptation
There's also the question of integrating well the generative AI into existing IT systems and workflows, which can be technically challenging. Because inadequate integration may result in poor user experiences or inefficiencies. 
Therefore, the expertise, the data integrity and inclusivity, and the system integration providing the adoptability of the AI application are essential for organizations in their process of adopting the generative AI technology. 
The above-mentioned challenges can be prevented or mitigated with a better consultancy regarding the workflows, user stories and the IT resources planning, together with the technology focus not only regarding the data integrity but also the IT and security system integration. 
 

Telindus & Codit: a comprehensive range of services to embrace generative AI as a business

Telindus, in collaboration with Codit, offers a comprehensive range of services designed to support organizations in their digital transformation journey. Proximus services encompass the following key areas:

•    Strategy and Planning
Proximus work closely with the client organization to devise a strategic roadmap that aligns with the goals. This includes defining clear objectives, setting key performance indicators (KPIs) for success, engaging stakeholders in terms of their user stories and workflows, and establishing a well-defined action plan.

•    Assessment
Our team conducts a thorough evaluation to assess the organization’s readiness for adopting advanced technologies, such as Artificial Intelligence (AI), Machine Learning (ML), process automation, data optimization, and application automation. 
The results of the assessment help the client be aware of the available datasets, and help the client have clear overview of the existing data policies, and security policies within the organization, translated into the IT measures. So that the client can develop an adoption plan with less technical risks.  

•    Enablement and Implementation
Proximus assist in creating the necessary prerequisites for seamless implementation. Utilizing agile methodologies, Proximus develop and execute plans to ensure the successful deployment of advanced technology solutions.
Codit and Telindus have rich experience with data, AI and IoT as well as cloud services. Proximus ensure that the generative AI adoption can benefit from the cloud infrastructure, and can be well integrated with other cloud services and the on-premise systems. 
Proximus also ensure that the deployment of the AI feature follows the guidance of the DPOs (Data Protection Officers) and CISOs (Chief Information Security Officers), with documentation required for the compliance traceability. 

•    Training and Talent Enhancement
Codit offers a range of training programs and talent empowerment services to upskill the team and maximize the benefits of the technology investments.

Conclusion

Generative AI stands at the forefront of technological innovation, offering a remarkable glimpse into a future where machines possess the creative process to craft content that rivals human ingenuity. Its applications, from content generation to creative design and data augmentation, are vast and promising. However, as with any powerful tool, it comes with ethical challenges that demand careful consideration. 
As Proximus navigate this exciting landscape, it's essential to remember that responsible adoption, transparency, and ethical principles must guide the journey into the boundless realm of artificial intelligence, where imagination and innovation converge to unlock a world of new possibilities.
For further information or to explore how Proximus services can benefit the client organization, please do not hesitate to get in touch with us. Proximus look forward to provide its assistance and expertise in the digital transformation journey.