Leadership Insights on Effective AI Productivity: Understanding Successes and setbacks in the Field
In a candid AI productivity panel, the floodgates opened to a wealth of real-world insights, raw truths, and practical advice**.
At Hubstaff, we've witnessed how AI can revolutionize productivity, but it's not some sort of magic trick. During our AI Productivity Shift panel, I had the privilege to chat with four genius minds paving the way on AI, remote work, and organizational change:
- *Dr. Gleb Tsipursky* - a cognitive scientist and remote work expert
- *Eryn Peters* - Co-founder, AI Maturity Index
- *Nadia Harris* - Founder, Remote Work Advocate
- *Phil Kirschner* - Workplace strategist and former McKinsey & WeWork exec
We dived deep into what's working, what's not, and how to lead smarter, faster, and more human-focused with AI. Here are some highlights, tailored for immediate action.
Feeling visual? Check out the entire panel conversation here.
Craving the full scoop? Grab the AI Productivity Shift report to delve into the stats, trends, and insights crafting AI's impact on work teams.
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1. The usage gap is killer, and it's not a tech problem
One standout stat from our research: an astounding 85% of professionals claim to use AI, but a mere 4% of their time is spent actively using it. That's a humongous disconnect.
According to Dr. Gleb, the key problem isn't access, it's ignorance about the AI capabilities out there. For example, most people aren't aware they can switch models in ChatGPT or create custom copilots trained on internal templates. They're bumbling around in the dark without a flashlight.
And as Nadia pointed out, fear of governance and compliance (particularly in tightly regulated regions like the EU) is causing companies to tread lightly. They're unsure about how data is stored, who has access, and whether they're genuinely compliant.
Key Takeaway: Start with education. Train your team on the practical applications of AI. Build confidence with clear, role-based AI usage policies showcasing what'sauthorized and what's not.
2. Fear is the silent assassin of innovation
One thing that truly struck me: fear is doing much more harm than we realize.
Eryn highlighted how companies send mixed messages, at first warning employees not to use AI, then urging them to adopt it. That kind of dizzying flip-flop leads to confusion and resistance. Phil added another thought-provoking tidbit: some employees aren't keen to showcase their AI workflows, as it reveals too much about their work theatrics or what they're automating and improving.
It's a visibility problem and a psychological safety problem.
Key Takeaway: Encourage curiosity. Celebrate experimentation. Make AI visible, not secret. Kick off an "AI Week" internally, where teams share how they're using AI to enhance workflows.
3. Embedding AI into workflows unleashes true impact
This was my favorite part of the panel when real-life examples came to the fore.
Nadia shared her success in helping a startup centralize scattered data from several tools and automating core business processes using AI. The result?astronomical time savings and clearer collaboration across teams.
Eryn spoke about a small consulting firm that crafted custom agents to automate competitive analysis. What used to take weeks now takes hours, enabling them to seal deals previously out of reach.
Key Takeaway: Don't start with "AI use cases." Begin with broken workflows. Where are your teams drowning in manual tasks or research-heavy processes? That's the perfect AI opportunity.
4. Let your team guide
I felt really inspired by Dr. Gleb's unique approach. Instead of mandating a single AI tool or workflow, he trains teams on how to use AI effectively and leaves it to them to create their own AI copilots.
In an insurance company, claims agents developed their own Copilot agents to automate policy letters. Management picked the best one, scaled it, and scored major time savings across the board.
Key Takeaway: Empower your team to build what serves them. Give everyone fundamental training, encourage exploration, and let the best concepts surface naturally. That's the formula for innovation to scale.
5. AI is reframing productivity
This is where things got real. We exchanged some compelling stats from our survey:
- 77% of people affirm that AI reduces task time
- 70% confirm it increases focus
- 45% report a significant productivity boost
But what's the real definition of productivity today?
Nadia debunked the misconception that extended hours spent at the office equals productivity. It's not about conspicuous effort; it's about meaningful results. Phil emphasized that AI should give us more time to tackle the right things, not merely additional tasks. And Dr. Gleb warned that AI is, indeed, escalating workload, so we need to adapt or sink.
Key Takeaway: Change the metrics. Shift from measuring action to measuring outcomes, speed, and effectiveness. Don't penalize employees for using AI to work smarter.
6. The hiring, training, and salaries landscape is rapidly changing
One stat that stopped everyone in their tracks: 20% of companies are already adjusting salaries based on AI skills.
Eryn underscored the need to reevaluate hiring, focusing on lifelong learners, curiosity, and adaptability. And as Dr. Gleb demonstrated, companies need policies stating that AI's purpose is augmentation, not replacement. That fosters trust, fostering smart AI adoption.
Key Takeaway: Incorporate AI proficiency into performance reviews and hiring chats. Reward experimentation and adaptability. Set policies that empower, not restrict.
7. Why small teams move swiftly, and big ones can catch up
Small teams triumph with AI as they can act swiftly, take chances, and adjust quickly. Phil encapsulated the cultural disparity perfectly: in startups, "move fast and break things" is encouraged. In large enterprises, "take a risk and you’ll get fired" is the unspoken rule.
But large corporations aren't doomed. Eryn pointed to Accenture's internal AI app marketplace as an excellent example of balancing control with empowerment.
Key Takeaway: Establish your internal AI marketplace or sandbox. Offer employees an authorized way to experiment and share tools without triggering months of red tape.
8. What AI will transform for each of us in the next year
We wrapped up by asking: What one way will AI affect your work in the next twelve months?
Here's what the panelists predicted:
- Phil will harness AI to support personal writing projects and automate email workflows
- Nadia will use AI to spot compliance trends across companies and develop faster audits
- Eryn will transition from "creator" to "curator," sifting through the clutter to surface essential insights
- Dr. Gleb will use AI tools to educate himself, learn platform updates, and tailor learning to clients' use cases
As for me? I'm set on fostering these conversations to keep occurring, because AI's true power stems from the people controlling it.
Key Takeaway: Ponder what one aspect of your work could be faster, easier, or smarter with AI? Start there.
Final thoughts: AI's true worth lies within people
Moderating this panel reminded me of the simple but vital concept: AI functions best when it's kept human.
Train your teams. Inspire curiosity. Focus on outcomes. Automate what makes no difference, and protect what truly counts. The teams that thrive won't simply use AI. They'll use it well.
Thanks again to our panelists, our audience, and everyone joining us in moving this shift forward intentionally and compassionately.
Let's build smarter together.
Prefer to watch? You can catch the entire panel conversation here.
Keen for the full dataset powering this conversation? Grab the AI Productivity Shift report to explore the stats, trends, and insights shaping AI's influence on work teams.
- Integrating AI into a blog's content creation process could revolutionize productivity, reducing manual work and allowing for more efficient content delivery.
- To make optimal use of artificial intelligence on Hubstaff's blog, it's essential to educate team members about AI capabilities, empower them to experiment, and establish clear, role-based AI usage policies.
