How I use AI to boost productivity and revenue

“Organizations using AI to augment people are seeing growth in both innovation and revenues.” — Satya Nadella, Microsoft

When my day starts with a headache, it usually means I’m being asked to defend what I’ve spent. No, not my spouse. It’s usually our CEO asking what revenues or cost savings I’ve generated with my budget.

A close second is when I’m too busy fighting fires — instead of others fighting them (better yet: having no fires!) — to do the projects I actually want to do.

Coffee and AI usually help.

Seriously?

Yes. There are a few AIs I work with (or know well) that I find particularly useful.

AI to boost revenue

On the yes-our-new-AI-project-leads-to-revenues front, most CIOs are well aware of companion AI that suggests cross-selling or provides problem-solving help. There’s another, called StreamzAI, that provides each salesperson with a single metric of knowledge and confidence.

This PULSE score is directly correlated with customer experience and sales outcomes, and the management dashboard shows sales readiness in real-time for individuals, teams, and the full salesforce.

With learning essential to sales performance — and L&D budgets under constant threat — having a single, reliable metric tied to learning AND revenues is a game changer.

AI to cut costs and boost productivity

Some AI initiatives are all about automating what is currently human work. While that can improve accuracy, volume, and 24×7 access, it doesn’t always, and AI isn’t always cheaper than humans. It also usually requires some oversight by someone who knows what it’s trying to do (so, no, you can’t fire everyone).

I just heard a TED talk that said companies that augment rather than only automating achieve 5x ROI and a more engaged workforce. However, I haven’t been able to find a study that supports that, and my AI couldn’t, either.

What studies do show is that 79% of the world’s workforce is not engaged (or is actively disengaged), and this costs the global economy USD 9.6 trillion in lost productivity annually, i.e., 9% of global GDP.

How much of your company’s productivity is 9%? That’s the cost of non-engagement.

Why do I ask? Because studies show that human-AI augmentation is associated with increased employee engagement (as in my own organization), and workers collaborating with AI report higher job satisfaction and a stronger sense of purpose. Companies that use AI to augment humans (vs. eliminating) see the greatest performance improvements. Examples include AIG, GE and Unilever’s boost in productivity, efficiency, and innovation.

For numbers in your own organization, I’m afraid you’ll have to collect them as part of your existing push for data-driven decision making. It’ll mean tapping into all the productivity and engagement data you collect today, with special attention to data before, during and after you integrate humans with new AI systems.

AI for learning and performance

I work with an AI-enabled learning tutor (AI-ELT) as an MBA faculty member. It gives personal help in a natural, audio-visual, conversational format 24×7; coaches instead of giving answers; and (for gamification lovers) instantly gives scores throughout the conversation on subject mastery AND presentation skills. We track a plethora of learning outcomes, and they all went up immediately.

AI-ELT also helps with job interviews, again focusing on both content and presentation skills. Before AI-ELT, our MBAs interviewed with four companies on average to get an offer. Now they only need to interview with two.

In a corporate context, it would be useful not only for general employee learning and coaching but especially for sales teams, leadership-level media readiness, etc.

My own digital twin

I wrote another CIO article about how I digitally twinned myself and why you should, too (actually, that was the title), so I won’t elaborate here. That said, it’s been a delight to see readers, speaking attendees, workshop participants and even family members talk with her and ask her questions I’d otherwise have to field.

You might also find that such an SLM closed AI takes work away from your employees and yourself (so, a productivity boost) more securely than open AI. Visit  CJ2.personal.ai to talk with my digital twin yourself.

AI for workforce/skill mapping and promotions

I’m also running a pilot with SkillmotionAI to AI-analyze employees’ skills and share with them what skills they’ll need for the next step in their career. It also shares where to get the skills (publicly available programs). The analysis can be stacked, showing team skills (for plugging holes and building more robust teams) and skills in an organization overall.

That’ll be key when projecting the future workforce needed and estimating hiring or L&D.

AI for human SEO/AIEO

Since most people opt to base the skill assessment on LinkedIn profile rather than questionnaire, we’ve found that sometimes, people don’t need more skills. They’re just not presenting themselves well on LinkedIn or a CV. Although CVs have gotten shorter over the years, now reducing a 25-year career to two pages (four in certain fields), the trend is now reversing, thanks to AI.

We’re shifting from human-read LinkedIn profiles and CVs to AI-read. With AI’s appetite for data, now more is better. I was recently contacted by an organization that screened 1.25 million LinkedIn profiles. You know who read those profiles. I’m thankful I was too lazy to summarize the text I cut and pasted from consulting proposals 20 years ago (when more data was better).

Eventually, laziness wins.

Has the AI-HR trend really taken hold? Yes. Now, 82% of companies use AI to review resumes, and plans are in place to use AI to: ask interview questions (76%); collect facial recognition data (63%); analyze candidates’ language (62%); transcribe interviews (60%); and evaluate language, body language, or tone of voice (59%).

In fact, 24% of companies have AI conduct the entire interview process, and 70% of companies let AI eliminate candidates without human oversight.

And yet, nearly all companies surveyed report that AI makes biased recommendations.

I’ll just let that statement hang there without further comment.

If you’re involved in these AI-HR projects, do make sure you have an AI ethicist (or team) and that you’re best friends with your Chief Legal Officer.

And be sure to share with your mentees (and your kids) that now they need SEO/AIEO for their LinkedIn and CVs, as well as AI coaching to interview with AI’s.

AI agent revenues

Just as humans need to market themselves to AI, companies and products will increasingly need to market to AI agents, not humans. You’ll need to alert your CMO and head of ESG/CSR and help them get their messages out effectively, so humans who alert their AI agents to buy sustainable products from ethical companies will find their way to you.

Prepping your CEO

Remember to coach your CEO to clearly deliver the following message to the entire workforce: “Instead of AI taking jobs away, we’re using it to help you build skills, move up the ladder and engage in more human and productive work, focused on missions you care about. And my AI coached me on how to deliver this message really well. [smile here]”

Finally, you

For now, while using AI to build your skills and present yourself well to take your CEO’s job:

• Envision the revenue, cost, productivity and other impacts of the AI projects you’re doing, plus new ones you believe will have a forceful impact.
• Gather data (internally, plus external research across companies) to benchmark today and measure the impact of your initiatives.
• Deliver initiatives, rinse and repeat.

For finances, you’ll want data on revenue growth linked to AI-augmented teams; sales per employee; reduction in labor, time, materials, etc.; customer acquisition/retention (including faster onboarding, less churn, etc.). For productivity, you’ll want task completion rates, error rates, process/project cycle times and throughput. For quality and innovation, you’ll want customer satisfaction, product/service quality and innovation rates, e.g., number of new ideas, new ideas implemented, features released, patents filed, etc.

For engagement, collaboration and skills/growth it’ll be engagement scores from Gallup Q12 surveys, Glint, etc.; net promoter score (for employees recommending your organization); job satisfaction; absenteeism; turnover; frequency and quality of human–AI interaction (self-reported and system logs); AI tool adoption and utilization rates; skills development using/alongside AI; and role changes/promotions linked to AI skill adoption.

Remember to collect and share qualitative data/stories that’ll soon become legends and part of your culture, as well as making the numbers feel “real” and personal.

Do you really want to be collecting all this data? No, probably not all. But you do collect data already for data-based decision making, and you’ll need data to support the initiatives you know in your gut are the right way forward.

Then, after a little analysis, you can give your own TED talk.

And with a proven track record of measured business impact, your day should start with no more headaches — until the next technological revolution.

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