Successful AI Adoption
How to win
Many AI adoption efforts will fail, when starting from an uninformed place of fear or chasing the new shiny object. After studying decades of digital transformations across industries, a consistent thread emerges for success that can be applied to AI adoption.
Qualities of winning companies
Clearly-defined strategy that is well-communicated
Able to quickly create value
Strong data foundations
Innovation is embedded into their workplace cultures
Talented workforce with advanced skills and competencies
Workplace training for employees to work with AI as co-pilots
Have corporate level standards for ethical and responsible AI deployment
Generative AI pilots are used to introduce new behaviors and capabilities. Strategic leaders use Human and AI collaboration to create short-term value that build into long-term maturity.
Six steps to enterprise AI adoption
Identify business problems and use cases: The first step is to identify the business problems that can be solved using Human + AI collaboration. A clear understanding of their operations, processes, and workflows will determine which areas can benefit most from AI solutions.
Data quality and accessibility: Ensure that corporate data is accurate, complete, and accessible. Data governance policies must be established to maintain data quality over time.
Skilled workforce: Investment in training and upskilling employees positions work alongside AI to leverage its capabilities while reducing risks.
Change management: Significant changes can be expected to an organization's culture, processes, and workflows. Have plans in place address employee concerns, ensure transparency, and support the adoption of new technologies.
Collaboration between humans and machines: AI shouldn’t be seen as scary. Generative AI is a tool to augment and enhance human capabilities - not replace them. Successful adoption means automating some repetitive tasks, and augmenting how humans and machines work by learning from each other to achieve common goals.
Trusted AI: Data privacy and security regulations must be quickly established to prevent any breach in trust . Responsible policies and procedures must be established for monitor and mitigate any potential biases in AI algorithms.
