Earlier this summer, Goldman Sachs released a report on AI adoption and spending that sent ripples through the stock market. Over the course of the next 30 days, Nvidia would lose $900 billion in market value with Microsoft, Meta, and Google similarly impacted.
After almost 2 years of focus on Generative AI, Goldman argues that most companies have little to show in the way of measured productivity and efficiency gains. The report – Gen AI: Too Much Spend, Too Little Benefit – questioned the amount of money being spent on ramping up AI capacity and capability.
With $1 trillion in CAPEX expected to be directed to Generative AI over the coming years, analysts asked - would the investment ever be recovered?
With so much promise in the potential for Generative AI, the primary reasons for this lack of results come down to a lack of comprehensive AI strategies, change management programs, and access, governance & training.
Ironically, access, governance and training are the lowest-hanging fruit and can lead to the fastest acceleration in use of the technology.
Employee Upskilling Critical to Unlocking Value
As a recent McKinsey Survey demonstrates, most companies and functional leaders still lack strong implementations plans or AI strategies. In the absence of strong top-down direction, employees are taking the lead. Close to 91% of respondents indicated that they are using Generative AI in some capacity within their organizations, even when those tools are not officially sanctioned for use.
Interestingly, across users and non-users of Generative AI, the survey revealed that enthusiasm for the technology is high. Nine of ten respondents indicated they are enthusiastic about AI and think it will help with “a range of skills from critical thinking to creativity”[1].
But there remains a gap in usage that does not match this enthusiasm - 70% of people consider themselves to be light users of Generative AI, while only 21% consider themselves heavy users.
To reap efficiency gains from Generative AI, companies need to focus on moving those light users into heavy users, or the ROI will not be realized. Take advantage of these high level of enthusiasm to enable and empower functional teams and accelerate adoption. Consider the following enablers:
- Access - If you haven’t given your employees access to Generative AI tools start looking at Enterprise plans. Open AI has offered one since last year and Claude just launched their enterprise subscription plan earlier in September. These enterprise plans protect confidential information from being ingested by the models.
- Governance - Whether or not you have provided organization wide access to these tools, it is important to have some governance in place. This includes AI usage policies that address topics including — data privacy, security, and intellectual property. They should ensure employees are aware of how to responsibly use AI-generated content. Policies should also address compliance with industry regulations and ethical considerations, such as bias mitigation.
Additionally, governance frameworks should define who has access to AI tools, under what conditions, and for which use cases. Establishing role-based permissions can help ensure that AI is used effectively and securely across different departments.
- Integration of Generative AI models into day to day workflows - To fully harness the potential of generative AI, it’s crucial for employees to shift from using these tools for isolated tasks to integrating them as companions throughout their daily activities. This transformation requires more than just access to technology — it requires a change in habits and behaviors.
Rather than turning to AI sporadically, employees should be trained to view these tools as integral parts of their workflow. This can enhance productivity across all tasks, from drafting emails to generating reports, and even during creative problem-solving sessions.
This seamless integration doesn’t happen on its own; it requires targeted training that embeds AI into every aspect of work. By fostering this continuous interaction, organizations can make Generative AI a natural extension of their employees’ capabilities, consistently driving value and efficiency across the board.
- Training and Upskilling to Extract Value - While integrating Generative AI into daily work flows is a crucial first step, the real value comes with advanced skills that allow employees to extract more value from the tools and minimize reworking content produced by generic generative AI models.
Prompt engineering, for example, goes beyond asking questions; it’s about crafting precise, contextually aware prompts that guide AI to produce meaningful, high-quality results. It involves a broader understanding of how Generative AI models work. Employees with these skills significantly enhance the utility of Generative AI, moving beyond surface-level interactions to harness the full spectrum of what these tools can offer.
Another critical skill is Train of Thought (ToT) prompting. This involves guiding the AI through a step-by-step reasoning process, similar to how a human would think through a problem. By prompting the AI to break down complex queries or tasks into smaller, logical steps, users can achieve more accurate and coherent outputs.
This method is especially valuable when dealing with multi-step problems or generating content that requires a well-organized flow of ideas. ToT prompting empowers users to better leverage AI’s capabilities for producing high-quality, thoughtful responses.
Organizations should invest in comprehensive upskilling programs. Beyond introductory courses, these programs offer advanced training that equips employees with the nuanced understanding necessary to unlock AI’s true potential.
- Process Reengineering in an AI-Driven World - While integrating AI into daily workflows enhances individual productivity, process reengineering takes a broader approach. It redesigns business processes to fully leverage Generative AI, especially in complex, collaborative environments.
Unlike simply embedding AI into existing tasks, process reengineering involves rethinking how work is fundamentally done. It creates new AI-driven processes that are more agile and efficient, rather than just improving current workflows. For instance, in project management, AI can dynamically allocate resources and optimize team collaboration in real-time, going beyond traditional methods.
This shift requires a cultural change, for organizations to see Generative AI not just as a tool, but as a core component of how work is structured.
The current and deepening gap between enthusiasm and effective usage highlights the need for comprehensive programs, thoughtful governance, and process reengineering. To bridge the divide between AI hype and tangible ROI, organizations should invest in these focus areas. These are key to transforming Generative AI from a buzzword into a powerful driver of innovation and efficiency.
