I was reading this recently survey from Bain around why companies are struggling to do more with AI and predictive analytics. What stuck out to me the most, was I think you could rationalize most, if not all, of these into a lack of in-house talent as the real problem. When you know understand the solution, frameworks for use cases aren't that hard. Data security is an evergreen concern, poor data; evergreen concern.
The disruption of GenAI has come on us so fast in the last 18 months, most companies haven't had the time to build out the processes, understanding and governance to effectively manage it. Strategy and planning went out the door, and I think in the next 12 months alot of organizations are going to look around and go, "what did we get for all of this?"
This deficiency poses several substantial risks and challenges for companies looking to adopt and effectively utilize GenAI technologies in the short term.
1. Hindered Innovation and Competitive Edge: Lack of in-house expertise can significantly hinder a company’s ability to innovate and maintain a competitive edge. GenAI technologies offer transformative potentials such as automating processes, enhancing decision-making, and creating new products and services. Without the necessary knowledge and skills internally, companies may struggle to understand the full capabilities of GenAI and fail to implement these technologies effectively. This lag in adoption can put them at a disadvantage compared to competitors who harness the power of GenAI more efficiently.
2. Challenges in Tailoring Solutions to Business Needs: GenAI applications need to be customized to fit the specific requirements and contexts of a business to be fully effective. In-house experts with a deep understanding of both their company’s operational needs and the nuances of GenAI technologies are crucial in tailoring solutions that maximize benefits. Without this expertise, companies might find themselves implementing generic solutions that do not optimally address their challenges or leverage their data, leading to suboptimal outcomes.
3. Difficulty in Managing and Scaling AI Solutions: Deploying GenAI is only the beginning. Managing these systems as they scale and evolve requires continuous oversight, updates, and adjustments. In-house GenAI expertise is crucial for monitoring the performance of these technologies, interpreting their outputs accurately, and ensuring they continue to align with business goals over time. Lack of such expertise not only makes it difficult to manage these solutions but also impedes the ability to scale them effectively as the business grows and its needs change.
4. Impediments to Employee Adoption and Cultural Integration: Introducing GenAI into an organization’s workflow involves significant changes in job roles and business processes. Employees are more likely to resist adopting new technologies if they do not understand them or if they perceive them as threats to their job security. Having in-house experts who can provide training, clear communication, and ongoing support is essential to facilitate smooth integration of GenAI technologies into the workplace. This support helps in building a culture of innovation where employees feel confident and competent to work with new technologies.
Keep giving them hell out there,
Matt
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