Generative AI Upskilling

It’s Time to Move From “Playing With AI” to Profiting From It

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Generative AI is no longer the shiny-new toy in the corner, after all its been around almost 10 years (GPT-1 from OpenAI released in 2016).

Yep, ChatGPT released in 2022 but its older brother is 6 years older.

ChatGPT, Microsoft CoPilot 365, Gemini, Claude, Perplexity, Falcon, Jais, Llama… they’re all sitting right there in our browsers, on our phones (on my android car device) and let’s be honest, the vast majority are barely looking at the surface, let alone scratching at the surface.

White collar workers, probably most of us reading this are getting just a few % utility out of it. Waving the magic AI wand with poorly constructed single prompts but massive expectations, using it for the most basic if used like redrafting emails (ineffectively) and for ideas of how to make a report better (and usually not using many of the suggestions).

After 18 months of workshops across the UAE, I can tell you this with confidence:

The average knowledge worker who “uses ChatGPT every day” is seeing maybe 10 % of its potential. Maybe. More like 5%.

That 90 % gap isn’t a tool problem. It’s a training problem.

Did you have driving lessons when you were younger?

Imagine at the age of 17 being given a set of keys to a new car and your parents saying, go practice, be careful of the main roads, don’t worry you’ll get the hang of it. There would be incidents and accidents, that’s why we get driving lessons.

Well with Generative AI at work, either training hasn’t been given at all, or when it has its been the traditional, feature-dump “how to press the buttons” session that doesn’t translate into real world results.

The reality is that those of us who are using Generative AI at work are using it by getting a username and password and just getting on with it.

So what does?

Contextualised enablement.

Showing people how to wield AI in the tasks they already live and breathe. Showing people how to perform their tasks and activities using the tool.

Don’t train people on how the tool works. Train people on how to use the tool in the context of their actual work. 

 

Why Generic Tool Training Falls Flat

Let’s call out the elephant in the meeting room:

Pitfall	What Actually Happens	Result
Slide-deck demos	Trainer showcases “cool” features on dummy data	Audience claps, then forgets 70 % of it by tomorrow
One-size-fits-all role examples	HR, finance, and sales get the same prompts	Relevance drops, engagement nosedives
Tick-box assessments	Pass the quiz, collect the certificate	Productivity metrics remain unchanged

The consequences are a story we hear from Abu Dhabi to Ajman, from Al Ain to Ras Al Khaimah and from London to Dubai.

CoPilot 365 trials take twice as long as planned because teams keep re-running training in search of traction. Some needed three attempts at getting CoPilot rolled out before meaningful usage appeared. Meanwhile, licences sit idle, budgets bleed and AI is unfairly labelled “overhyped”.

It’s the same for Gemini in Google Workspace and OpenAI ChatGPT.

The Numbers That Change the Conversation

These are field-tested figures from the last 18 months of our company’s engagements:

• Contextualised enablement yields a three-fold boost in both adoption and visible productivity gains versus generic tool training.
• Organisations that pair AI with real, role-specific tasks report triple-digit ROI inside the first six months.
• Basic proficiency (think “I can prompt my inbox, summarise minutes and draft content”) returns 1 hour per employee per day.
• Advanced proficiency (chaining prompts, automation, multimodal tasks) can claw back up to 3 hours every single day.

Multiply those hours by a 50-person finance team or a 200-seat contact centre and the benefit becomes crystal-clear.

 

Real Work. Real Results. 60 Seconds. A Recent Example.

During a workshop this month for an events company, one participant had an task for the following week that we wanted to try make more effective: design the food menu for their next event. His process was:

1. Dig through three months of emails hunting down feedback reports.
2. Open each report, scroll to the F&B section, copy notes into a spreadsheet.
3. Check audience demographics.
4. Draft a menu based on gutfeel, sticky notes and caffeine.

Days of manual effort (amongst other tasks).

Enter CoPilot 365 with the right prompt approach:

• “read my emails and locate all event-feedback reports from the last three months. Identity & summarise all F&B comments from all events into a table.”

• “The nationalities attending next months event are Chinese & Thai, analyse the demographic profiles, research the demographics food and beverage preferences and propose a 3 course menu aligned to these preferences, while taking the event-feedback F&B comments into consideration. Make this menu a centre piece that will delight the guests ”

60 seconds. End-to-end. CoPilot read the reports, extracted sentiment, cross-checked online cuisine trends for that demographic and generated a menu draft.

We ‘wasted’ another 60 seconds refining:

• Add trending dishes
• Optimise for lower-cost ingredients while improving perceived quality
• Deliver three menu options (3 starters • 5 mains • 3 desserts) complete with cost and prep notes

A task that once ate half a week distilled and digested into two minutes of effective prompting, with the training participant learning how to embed AI inside his workflow, not how to watch someone else click buttons and learn features with some ‘effective (yawn – standard) prompts’.

4-Step Framework for Contextualised Enablement

1. Map Roles, Tasks & Bottlenecks
2. • Interview staff, analyse calendars, screen-record workflows.
   • Identify the pain-points that steal hours (e.g. proposal writing, data-cleaning, presentation deck prep).
3. Design Hands-On, Task-First Exercises
4. • Convert each bottleneck into a real lab: “Write this month’s board report”, “Reconcile these invoices”.
   • Provide starter prompts but force participants to tweak until the output sings.
5. Iterate & Level-Up
6. • Basic ↔ Intermediate ↔ Advanced tracks.
   • Introduce chaining of prompts, chaining of tasks as that all important confidence grows.
7. Measure, Publish, Celebrate
8. • Track hours saved, error reductions, speed-to-insight.
   • Showcase early adopters; peer-to-peer social proof is rocket fuel Elon Musk would be proud of for adoption.

What 1–3 Hours Back Actually Looks Like

Team Size	Basic Proficiency (1 h saved)	Advanced Proficiency (3 h saved)
10 people	50 h per week	150 h per week
50 people	250 h per week	750 h per week
200 people	1,000 h per week	3,000 h per week

That’s the equivalent of hiring another full-time employee for every ten staff, without adding headcount.

Key Takeaways (Steal-These-Notes Version)

✔ Tools don’t create value, workflows do. Train people in the work, not just the software.

✔ Adoption follows relevance. If AI speaks the language of sales, finance or HR, teams lean in.

✔ Contextualised enablement triples ROI. Three attempts at generic training cost more than getting it right once.

✔ Small wins stack fast. 1 hour back today funds tomorrow’s 3 hour breakthrough.

✔ Think Big, Start Small, Start Now. Pilot on a single bottleneck, prove value, scale across departments.

Call to Action: Your Next 60 Seconds

Open your calendar and find the task you dread the most, writing the weekly sales deck, sifting through supplier quotes, building that budget model. Ask yourself:

“What would happen if CoPilot or ChatGPT could take the first 80 % of this off my plate?”

Then do something radical: test it. Prompt. Iterate. Break things. Learn.

If you need a guide to turn that 80 % into a daily habit, RAKEZ and Koshima are here in the UAE, delivering face-to-face and online. We’ll map your workflows, embed AI where it pays back fastest, and hand your team the ability to reclaim hours, not just watch a demo.

Turn your desk into the most productive seat in the building.