Is AI Living Up to Its Promise? The People Side of AI Implementation
In my work I see some organizations struggle to justify their spend on AI tools, specifically Generative AI (GenAI).
A recent report from Coastal, on a survey to their Salesforce customers, found that only about 21% of firms believe they are seeing a return on GenAI investment. 21%! That's one in five.
Image from Coastal report, “AI Isn’t Delivering—Here’s What to Do About It”
Note: While this is a Salesforce report, I think it applies broadly to other AI tools too, including Microsoft Copilot. (I'm not picking on Salesforce AI)!
In my view, there are 2 fundamental issues that make seeing the value from GenAI difficult:
Investing in tech requires equal investment in people
It's hard to quantify 'value'
In this first post of a two-part series, I'll focus on the people aspect of successful GenAI implementation. In part two, we'll dive into measuring and quantifying ROI when rolling out AI tools.
Invest in People (with the Tech!)
A new technology comes along, and everyone expects that things will all of a sudden improve.
"Build it and it will be fixed," they said.
In reality, and we've seen this with digital transformation projects in the past, some problems get solved, and new ones crop up. The internet made communication easier and faster, but it also led to cybersecurity and privacy risks.
AI is not only making tasks easier and faster, but it's changing how work is organized and how jobs are defined.
This is a fundamental shift in the landscape of work.
Josh Bersin, a respected HR leader, writes about this topic and says:
“..our challenge is not implementing AI, but redesigning jobs, and business processes around AI. And that's why success with AI is a people problem, not a technology one. And if you don't get this right, your AI transformation will lag.”
If you rollout AI pilots or projects without in-depth training, supports and processes to help people adapt to the new change, the tech will fail. Guaranteed.
What's needed is:
Strong change management, consistently, over time
Various studies show that about 50-70% of change management programs fail, and of that, half of that is is because of “employee resistance.”
(Sources: Forbes, McKinsey, Josh Bersin)
Consider this striking reality:
"For each dollar spent on machine learning technology, companies may need to spend nine dollars on intangible human capital,”
Stanford researcher Erik Brynjolfsson wrote in 2022.
Let me repeat: He is suggesting to spend one dollar on AI, 9 dollars on investing in people and the change.
This investment includes:
Dedicating staff specifically to support the implementation including HR staff to support role changes
Creating clear communication channels to explain the change
Providing safe spaces for people to work outside their comfort zones
Allocating time to address concerns and resistance
Offering hands-on training and ongoing support
For example, a successful GenAI rollout might include weekly office hours where staff can bring real work problems and learn how to solve them with AI tools, rather than just generic training sessions.
Remember: Adoption of AI won't happen by itself.
Redesign jobs and plan to create new roles
Break down a job into activities or tasks. Evaluate AI solutions and decide how a human can work alongside AI to complete the tasks. Yes, this means changing job descriptions, maybe even expanding people's jobs and responsibilities.
Moving beyond static roles and job descriptions might be needed as well. With AI people can technically grow faster, maybe take on a wider variety of projects. Companies need to make moving internally easier and prioritize people gaining more experience across projects, teams and departments. New roles will emerge and job descriptions become dynamic.
Redefine performance
What does good performance look like with AI? It will likely be different than without the tooling.
Performance metrics and measurement will need to be updated alongside the implementation of AI.
And maybe…new roles that produce more and have more flexibility demand higher pay, not lower. Many people are worried about AI and how it will affect jobs. Some jobs will likely go away or pay less, while others may pay more as productivity rises.
Adapt leadership
AI means human-centered leadership is needed more than ever. What do leaders offer and how can their skills be honed?
It's those soft skills like listening, humility, learning, kindness...treating people like people and not robots who have widgets to produce and targets to hit.
Creating the spaces for people to try new things, innovate, experiment, sometimes succeed and sometimes fail is critical.
Leadership is hard because it means being brave enough to try new things and stick with it through the bumps and challenges—and that's why they get paid the big bucks.
Taking It to the Next Level: AI Reimagines Entire Jobs
The real breakthrough happens when we shift from thinking about AI as just helping us do the same things faster—writing emails or creating presentations more efficiently—to reimagining how AI fundamentally transforms how we work.
This doesn't have to be a scary prospect!
Let's look at a sales process example with this transformative lens:
Imagine a sales process where AI evaluates incoming leads and follows up with personalized emails to schedule meetings. Before the meeting, the AI conducts preliminary research on the prospect's company and drafts tailored questions and presentation materials. The sales rep reviews these materials and focuses on what humans do best during the meeting—telling compelling stories, asking insightful questions, and guiding meaningful conversations.
After the meeting, the AI generates a draft proposal based on the discussion notes. The sales rep adds comments and strategic direction, and the AI refines the proposal. By the end of the day, the prospect receives an impressive, customized proposal—striking while the iron is hot. When the prospect offers feedback, the AI incorporates it overnight, and a revised proposal is ready the next business day. The deal closes in record time, and the sales rep earns more commission than ever before.
Created with Napkin.AI
But what about all this "theoretical" free time?
So AI makes deals happen faster, and supposedly there’s less time spent on sales. Obviously, the value is in capturing that time for something useful.
The possibilities are endless. Sales professionals could:
Instead of doing the same amount of work, generate more sales and grow the business
Dedicate more time to thought leadership activities like industry presentations
Engage in proactive account management with existing clients
Spend more quality time building deeper client relationships
Develop specialized expertise in their industry vertical
Work fewer hours while maintaining or increasing productivity
Mentor junior team members and share best practices
This transformation represents a fundamental shift from viewing AI as a productivity tool to seeing it as a collaborative partner that allows professionals to focus on the highest-value aspects of their work.
Moving Forward
In part two of this series, I'll dive into practical approaches for measuring GenAI's value, share implementation frameworks that work, and address common objections to AI investment. I'll also outline realistic timeframes for seeing returns on your GenAI investments.
What's becoming clear is that the organizations seeing real ROI from GenAI understand that it's not just about the technology—it's about how people and systems adapt around it. The 20% of companies seeing returns are approaching implementation with a people-first mindset, reimagining work rather than just accelerating existing processes.