Our FACQ provides critical questions which you would love to hear. We will share more as we focus on capability building….
Strategic alignment can improve ROI by 25%. AI/GenAI projects should address specific Business Objectives/Challenges i.e. Value Acceleration or Process Optimization, etc. They must align with KPIs like cost reduction or customer engagement improvements or compare results to pre-AI performances.
Besides Business Objectives, focus should be on Assessing Data Readiness, Defining AI use cases and Measuring Outcomes, etc. Proper planning can increase the success rate of AI projects with a focus on low-hanging projects. Must heed to EXPERT VIEWS.
They can leverage cloud-based AI, open-source platforms i.e. LLMs, and low-code/no-code tools. SMBs are using AI to streamline business operations and market focus. Value analysis is a must.
First build AI solutions with a focus on compatibility with legacy systems using APIs. Integrating AI into existing infrastructure and workflows is a significant hurdle. Management or Practitioner should involve necessary stakeholders and domain experts. Roughly 65% of AI projects fail due to integration challenges.
Invest in AI talent, skills development or reskilling for existing staff and domain experts, hiring data scientists and GTM Kit development and organizational awareness.
AI adoption is increasing but with the technology influx, adoption is getting much R&D-to-solution-fit evaluation. A recent study by IBM found that 35% of companies are using AI in their business processes.
Autonomous agents are capable of handling complex tasks, reducing improper human interactions, etc. As per Stanford University, autonomous agents could automate up to 30% of current work activities.
Edge AI, processing data locally on devices, LLM agnostic solution deployment, and agent-driven deployment are going to trend. In future, enterprise-generated data may be processed outside a traditional data center or cloud. Key risks in deploying AI/GenAI in an organization include decision-making errors, lack of transparency, and ethical concerns. Proper oversight and human-in-the-loop models reduce deployment risks.