AI Training for Business
NC, USA
Despite 73% of companies investing at least $1 million annually in Enterprise AI, only about one-third see worthwhile returns on their investment.
Adopting AI is more about managing change than technology. This is why we emphasize helping people embrace AI transformation.
The gap indicates that simply implementing AI technology and providing basic training is not enough.
Leaders should start by clearly expressing the purpose of implementing AI within the organization.
Leaders need to evaluate an organization's risk tolerance and its capacity to scale AI initiatives beyond the pilot phase.
Leaders must balance enthusiasm for AI's potential with empathy for its disruptive effects.
Leaders must recognize AI skills gaps and prioritize employee training as the foundation of successful Enterprise AI adoption.
Our publications offer direct access to the key information you need to drive digital transformation within your organization.
Guides
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A trusted advisor that guides you along the path to Enterprise AI is our proven strategy for achieving rapid returns.
Workshops
Resource Calls
Planning
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Creating an AI-ready organization requires a fundamental shift that starts with active training initiatives.
Self-Serve Portals
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Live Q&A
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A people-first approach in digital transformation is crucial for success. We assist you in achieving it.
Deep Dives
Strategy Sessions
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Using AI effectively requires a clear, company-wide strategy rather than just separate projects in different departments. Without central oversight, AI efforts can become scattered and may fail to help achieve long-term goals. This focus on a central committee represents a significant shift in how organizations approach AI strategy.
A common problem in enterprise AI projects is the lack of a clear plan that connects to business goals. Organizations should develop an AI strategy centered on business outcomes, with specific key performance indicators (KPIs) to define success before initiating any technical work.
The planning phase is crucial for successful AI projects. It focuses on getting the organization's commitment, setting a clear direction, and developing a practical plan for implementation. This phase needs careful thought, teamwork, and a good understanding of the organization’s readiness.
The AI planning process aims to identify and mitigate risks, such as pushback, security issues, and compliance concerns. By assessing risks early in the vision-setting phase, the committee can identify initiatives that are high-risk and low-reward. This allows them to either eliminate these projects or create strategies to manage the risks from the outset rather than dealing with issues after deployment.
To show the return on AI investments, it's crucial to connect technical metrics with business outcomes. This involves translating AI performance into measurable results that matter. Balancing immediate feedback with long-term impacts is essential for a comprehensive evaluation of AI initiatives.
