In less than a year, we’ve gone from 99% of the workforce not knowing what Large Languages Models (LLM) were, to now 50% of the workforce actively using or evaluating how to use LLMs in their day-to-day tasks (Salesforce Global Survey 2024).
Many forward-thinking leaders are feeling like they are unable to keep pace with the changing landscape. Their drafted strategies to overcome these deficits, typically focus on up skilling current employees or trying to hire additional, AI-skilled professionals. Those that have already ventured down that path have found that the market pressure has created scarcity and a new high in salary expectations. That then heightens the pressure to retain that retooled workforce.
As I have been talking with customers, peers, and mentors, the good news is there are teams that are succeeding. They are looking at problems in a new way. They are looking beyond the technical roles. They are collaborating in new creative was with their financial, HR, legal, and operational peers. They are shifting company culture by fostering continuous learning. In general, they are looking at this as a hurdle the entire company must address, not just the CTO/CIO.
Here are four things I have found to be in common with those that are quickly matching pace:
1. A company’s culture most allow for flexibility with technology budgeting. As discussed above, AI has moved into the market very quickly. It is no surprise that I have not spoken to anyone that feels they adequately budgeted for the growing demand in 2024. If you wait to accommodate with next year’s budget, you will be a year behind your competitors. A key to fostering IT budget flexibility is ensuring the conversation is about the investment (therefore ROI) and not a conversation about cash flow implications.
2. A company’s culture has to encourage, and the business has to incentivize continuous technical professional growth. This means businesses need to finally prioritize learning and development initiatives AND reward or recognize those that adapt to the progress. Don’t limit this to technical roles. An accountant that understands and champions the implementation of intelligent automation within their own systems, partnered with IT, will drive more efficacy than just IT by themselves.
3. AI should not be exclusively IT’s responsibility. While IT is needed to implement, much of the low handing fruit that drives the RIO conversation is business critical and is best championed by direct influencers. Like the example above, any employee with influence should be collaborating on how the team best leverages AI opportunities.
4. Define AI governance and policies immediately. Make the first milestone of your multi month, quarter, or year AI strategy be building an “Employee AI Use” policy that guides and directs employees on safe use of AI in the workplace. Salesforce’s recent global survey shows that without a policy, the outcome is only negative; employees are either not using AI out of fear or they are exposing the company to risk by using public/free AI generative tools.
At the end of the day, these complexities require a blend of innovation, adaptability, and a willingness to embrace change. I realize all of these are easier said than done, especially considering how competitive it is to get access to the right AI/ML resources.
At Advisor labs , we’re here to help. You’ll find our teams incorporating these same strategies and more. If you haven’t already met with one of our account executives, let me know and I’ll get you connected with someone. At the very least, we can jump on a free consultation call and help you get started with the Employee AI Use Policy.
