GenAI integration is revolutionizing how businesses operate. GenAI tools can enhance efficiency, improve decision-making, and help organizations gain a competitive edge. However, the journey to successful AI adoption can present obstacles. Here are some common obstacles our team has come across when helping our partners integrate GenAI into enterprise software solutions:
Starting an AI implementation journey without a problem definition
Without a clear problem and outcome defined, we see organizations struggle to align AI initiatives with business goals.
Defining company needs and goals that align with your company's mission and objectives will help develop a comprehensive AI implementation strategy. By aligning goals and AI initiatives, the team can identify specific areas where AI automation can add value to the organization and identify areas where resources need to be directed.
Lacking access to diverse, high-quality unstructured data
Available consumer LLMs like ChatGPT are powerful productivity tools. However, if your organize has a niche use-case or requires a secure, internal solution, training a custom LLM or fine-tuning an existing model is critical. In these scenarios, the training data must be comprehensive, representative, and domain-specific to avoid skewed or inaccurate results.
For custom LLMs or fine-tuned models, it’s essential to curate high-quality, diverse datasets tailored to your specific use case. This involves carefully selecting and sourcing relevant data, addressing any biases, and ensuring the data is secure. By doing so, you’ll enable your custom LLM to provide accurate and secure insights aligned with your organization's needs.
Build metrics to measure success
Measuring the impact and efficiency of AI can be a complex problem especially when workflows are changing. Building benchmarks can be a complex task and critical to understand possible LLM flows.
Too many projects are built and deployed without sufficient test data and benchmarks and fail when edge cases are not handled properly. Building proper benchmarking and metric tracking strategy is critical to understanding the success of any AI project.
Look for incremental gains instead of major workflow changes
Focus on improving productivity in existing flows with AI. Summarize texts when necessary, highlight important parts of a document and gather feedback.
Build new review processes to let users quickly evaluate the outputs of AI systems.
Keeping an agile technology stack
A key reason for the failure of many AI projects is the complexity of the tech stack involved which frequently depends on several other disciplines like devops, cloud ops and integrating AI might require a significant effort from multiple teams and become an expensive endeavor.
AI projects need to use nimble and simple solutions where major architectural changes can be implemented quickly and gather initial results and feedback before focusing on devops and cloud-ops.
Misunderstanding of legacy systems and their integration capabilities
Incorporating AI into legacy systems can be complex due to compatibility issues, requiring a deep understanding of the current infrastructure.
It is essential to start your initiative with a deep understanding of current systems. This can be done through in-house technical staff or through external experts. An audit of current systems allows for an understanding of where your AI initiative will fit and what part of your business will benefit. Once a deep understanding is met, start small by integrating AI applications in controlled environments and gradually integrate further into your system infrastructure to minimize disruptions and manage risks.
Organizations lack resources and skilled personnel
Firms often lack skilled technical personnel with expertise in AI and data science to guide the organization through AI integration.
Understanding what resources are needed for your AI initiative and investing in training technical staff or outsourcing to specialized firms to guide AI implementation will close the gap between expertise and implementation.
Data privacy and security assessment
Data privacy and security needs to be forefront of any AI project and the impacts need to be assessed. Especially with LLMs, it is important to understand which data elements will be uploaded.
Implementing an AI initiative must be accompanied by training and rules regarding how client or company data can be used in AI tools and models. Outlining clear guidelines on what data can and cannot be used in AI tools is essential to maintaining strong data privacy and security standards.
Team members are resistant to change in workflows
Teams may resist adopting AI technologies in their day-to-day due to disruption of workflows and fear of the unknown.
Educating employees about how AI can enhance their work and involving key stakeholders early to foster acceptance of AI development will help reduce resistance among team members.
Ethical and regulatory capabilities of AI tools
While using AI in a single process might not have any visible impacts for ethical and regulatory requirements, there might be impacts in the end to end workflow and overall company compliance.
Helping your team understand ethical AI use through ethics training and implementing standardized regulatory compliance audits will instill trust in the initiative.
Being proactive when addressing AI adoption challenges, such as establishing a clear strategy, ensuring access to quality data, developing a clear understanding of legacy systems, investing in skilled personnel, safeguarding data privacy, and educating your team about GenAI, will advance your implementation journey. The key to a strong implementation journey is through planning, open communication with your team, and a willingness to adapt. By following these principles your team will overcome adoption obstacles and position itself to fully leverage the transformative power of GenAI.
