In a downward trending economy, getting management buy-in is no small feat. And for good reason. Resources are limited and expectations are mounting. Executives are under pressure from the board and investors to increase ROI or even just stay afloat. They might be hesitant to jump on new trends, including AI.
Why executives are skeptical of new technology:
- High costs
- Integration/training concerns or “how are we going to get everyone up to speed on this?”
- Fear of failure or “what if we roll this out company-wide and it fails miserably?”
Here’s the good news: unlike a lot of heavy-lift technology, AI is easy to integrate and starts out at a reasonable cost (you can always scale up down the line).
But simply stating that generative AI is easy to integrate or isn’t a financial strain isn’t enough. You have to present your appeal with regard to the executives’ decision-making systems.
After participating in an executive network for 40 hours a month over a year, I have noticed patterns in their decision-making. Most executives fall under one of four dominant personality types — analysts, trailblazers, the outcome-driven, and reactors. Your best bet is to present your case in a way that aligns with their motivations. Work with the grain, not against it.
Let me show you exactly how.
Types of decision-makers (and how to win them over)
1. Analysts
They’re driven by data. They’ll likely do a cost-benefit analysis of the situation. There sample questions will likely involve:
- Can you provide quantifiable metrics that demonstrate how GenAI can optimize processes?
- How does GenAI's performance compare to existing processes in terms of scalability?
- What is the cost to the company if this technology is deployed company-wide?
How to win over Analysts:
Stick to the facts and keep them handy. For example as an answer to one of the questions above:
Q: How does GenAI's performance compare to existing data analytics methods in terms of scalability?
A: Since our social media team has adopted GenAI, we’ve been able to repurpose our blog content quicker. We’ve gone from 1 post/week to 4 posts/week while freeing up our content specialists to write more tactful content.
Stats to help you out:
- 80% of tech executives indicate they will increase investment in AI in the next year. More than half of tech executives whose companies are experimenting with generative AI (56%) are doing so for economic savings. (Source: EY)
- 2023 is already a record year for investment in generative AI startups, with equity funding topping $14.1B across 86 deals, as of Q2 of 2023. (Source: CBI Insights)
- AI assistants and human machine interfaces (HMI) are taking up the bulk of funding, with 2.5B invested in this sector. (Source: CBI Insights)
2. Trailblazers
They’re risk-takers. They’ve often built their operations on taking calculated risks and coming out ahead. Way ahead.
Their concerns will revolve around early adoption, disruptive potential, and long term advantages. Their questions can take these forms:
- What novel applications of GenAI can help us break through existing barriers and create new market opportunities?
- How can GenAI enable us to pioneer new ways of engaging with our customers and reshape industry norms?
- Are there examples of other trailblazing companies in our sector that have successfully leveraged GenAI?
How to win over Trailblazers:
Speak their language of innovation and disruption. Show them that GenAI can be a transformative sidekick to their trailblazing hero. Highlight how it can be a competitive advantage, enabling them to lead the pack in an industry that's constantly evolving.
An idea of what that looks like in action:
- Show them how a generative AI tool can be deployed innovatively. For example, an AI tool can help the customer support team better understand concerns by paraphrasing customer statements and replying in customer-specific tones. Read how Wordtune does this here.
- Present a case study where a company integrated AI to launch in a new market or increase the TAM (total addressable market) in their current position.
Stats to help you out
- More than half of the respondents to a recent VentureBeat survey said their organizations are experimenting with AI, but only 18% of those companies have begun implementing it. (Source: VentureBeat)
- Only when firms increase their intensity of AI adoption to at least 25% — meaning that they are using a quarter of the AI tools currently available to them — do growth rates pick up and investments in AI start to pay off. (Source: MIT Research)
3. The outcome-driven
These decision-makers are influenced by results. To sway them towards GenAI adoption, showcase the clear and measurable outcomes it can bring to the table.
There concerns often look like this:
- How does GenAI perform on our current KPIs?
- What key performance indicators (KPIs) can we use to measure the direct impact of GenAI on our operational efficiency?
- How can GenAI contribute to reducing our production costs while maintaining or enhancing product quality?
- What concrete improvements in customer satisfaction or response times can we expect after implementing GenAI?
How to win over the outcome-driven
Present GenAI as a means to an end (not just an end in itself). Discuss how it can streamline processes, enhance productivity, and deliver tangible results that directly align with their goals. Illustrate scenarios where GenAI has already proven its ability to drive desired outcomes.
Here are a few ideas:
- Present case studies on tools that have brought in metric-based results. Wordtune, for instance, helped Hootsuite achieve optimal readability for help center content just six months after implementation. Read the complete case study here with the specific challenge and solution arc.
- Lead with KPIs that have been challenging to improve and present a tangible path to improvement through GenAI. For example, if social selling has been a challenge, show execs how writing assistants can help employees create social media content from their internal communication documents and campaigns.
Stats to help you out
- Companies that utilize AI in their operations can improve their productivity by up to 40%. (Source: Accenture)
- Using artificial intelligence for sales can result in a doubling of leads, a shrinking of call time by 60-70%, and a 40-60% cut in costs. (Source: Harvard Business Review)
4. Reactors
These are executives who are quick to respond to external circumstances. Whether it’s pressure from the board or a new tool being tested by their community, they react and adapt quickly to new expectations and offers.
Their expectations and concerns might look like this:
- How does AI fare in comparison to the methods we currently use?
- How can we use AI to safeguard against disruptions?
- Is GenAI scalable to handle sudden surges in demand or unexpected situations?
How to win over Reactors
When approaching reactors about GenAI, you need to demonstrate its immediate relevance and value in their changing landscape. You also need to understand the decision maker’s sphere of influence — what their community is championing and why.
Here’s how to go about it:
- Determine their sphere of influence and borrow inspiration and examples from them. For example if your CMO is part of a mastermind, find out how other members are using AI at work and lead with those use cases.
Stats to help you out
- For most of the technical capabilities like natural language generation, sensory perception, and coordination , GenAI will perform at a median level of human performance by the end of this decade. (Source: McKinsey)
- GenAI will have the greatest impact on marketing and sales functions in nearly all industries. However, high tech and banking will see even greater impacts due to its ability to accelerate software development. (Source: McKinsey)
In the long run
There is no guarantee that every executive will fit into one of these categories - they may fall into a combination of two, three, or all of them. To combat the uncertainty, make sure you have your stats handy and your nerves calm.
In the long run, your success in appealing to executives will boil down to tangible metrics and verifiable use cases.