4 Key Priorities for Successful GenAI Implementation

Generative Artificial intelligence (GenAI) is no longer a futuristic concept; it’s becoming as ubiquitous as Instagram and Tiktok. Just like Instagram and Tiktok revolutionized visual storytelling, GenAI has revolutionized content creativity. We are already beginning to see its initial impact across various industries. However, to fully unlock its potential, companies need more than just sporadic use and investment in GenAI technologies. They need a well thought out strategic approach that incorporates the following four priorities:

1. Strategic vision and alignment
2. Technology foundation
3. Change management
4. Execution and responsible implementation

Strategic vision and alignment

To successfully implement GenAI, organizations must align AI initiatives with their business objectives. Without a clear strategic vision, AI projects risk becoming experimental without delivering real value.

Strategic alignment with business objectives

The foundation of successful GenAI implementation begins with aligning GenAI initiatives with the overarching business strategy. Companies must identify specific business challenges or opportunities that GenAI can address, ensuring that projects directly contribute to the organization’s strategic goals and KPIs. This alignment helps prioritize AI efforts and ensures that investments deliver tangible business value. According to Gartner, a key factor behind 30% of GenAI projects being abandoned after POC is that business value is not clearly established.

Culture of experimentation and innovation

Successful AI companies are constantly experimenting and keeping up to speed with the rapid advancements in GenAI. It is crucial that they test out new GenAI capabilities and tools and identify the areas where it can provide the greatest impact to their business. An added benefit of this approach is that you will be able to filter out use cases that aren’t viable. This Deloitte report shows that 59% of organizations have executed between 11 to 50 GenAI experiments in the last quarter of 2024.

Technology foundation

GenAI’s effectiveness relies on a strong data infrastructure and governance framework. Organizations must ensure high-quality data and a scalable AI environment to maximize GenAI’s potential.

Data excellence and governance

GenAI’s effectiveness is closely tied to data quality and management. High quality data will produce accurate outputs. Poor data will cause vague and biased outputs. Hence data quality and governance must be prioritized. This means streamlining data collection, storage, and analysis to ensure GenAI systems have the necessary information for optimal performance.
Companies should also establish clear data governance, risk and compliance policies to protect their data and maintain its integrity.

Technology infrastructure and ecosystem

There are multiple options available today to run Generative AI workloads. Companies should create a standard operating environment for AI use cases. Such an environment will comprise a robust and scalable technology infrastructure capable of supporting GenAI and other types of AI workloads. The infrastructure should be certified across different hardware, software, and cloud platforms and built on open source technology. This ensures that companies have the flexibility to choose their preferred vendors and solutions, and access to rapid innovation.

Change management

AI adoption impacts people and processes just as much as technology. Organizations must prepare employees for the shift by promoting AI literacy and adapting workflows for GenAI integration.

Focus on people, processes, and change

While technology is a crucial aspect of GenAI implementations, success in AI initiatives greatly depends on people and processes. The automation that GenAI brings will make many jobs and processes redundant. However this also means that staff can be repurposed to focus on more value-added work alongside GenAI. Companies should focus on building AI literacy across the workforce, ensuring employees are equipped to work alongside AI systems. It is also critical to address potential resistance to change and ensure that the organizational structure supports AI adoption.

Execution and responsible implementation

Scaling GenAI from pilot to full-scale implementation requires a reliable operational model, security measures, and ethical compliance. Organizations must prioritize trust, performance, and sustainability in their AI deployments.

Scalable and reliable operations

Moving GenAI projects from pilot to production is a significant challenge. Successful companies invest in the infrastructure and tools needed to operationalize and scale GenAI usage. Specifically the following are some key requirements:

• A scalable GenAI infrastructure that is able to operate in a hybrid and multi cloud configuration
• Observability into the reliability and performance of the GenAI infrastructure
• Optimised resource consumption, thereby reducing CO2 emissions

Security, trust, and ethical considerations

As AI becomes more integrated into business operations, security, trust, and ethical considerations are paramount. Companies must prioritize data security and implement measures to protect sensitive information from unauthorized access, tampering and hijack. Issues such as bias, privacy, and compliance with regulations such as GDPR and the EU AI Act will also need to be addressed. Companies must also ensure transparency and accountability in AI systems to build trust with stakeholders.

GenAI implementation with SUSE AI

SUSE AI provides organizations with the technology foundation to build a scalable, secure, and flexible GenAI platform. It integrates securely with your infrastructure, allowing deployment on-premises, in the cloud, hybrid environments, or even air-gapped setups. SUSE AI is built with security and guardrails at its core, offering robust capabilities for compliance, threat detection, observability, and adherence to certifications required by highly regulated industries.

Download this whitepaper to learn more about SUSE AI.