One year on. Reflections on a year of AI coaching

One year on. Reflections on a year of AI coaching


Published Nov 11, 2024 · Updated Apr 12, 2026

bGrant Paling. So welcome back to my blog. As I approach 12 months of being coached by AI Endurance there is a lot to reflect on! So let’s get to it.

First, let me recap my triathlon season:

  • Two European Championship races for Great Britain (at 70.3 and Olympic Distance)
  • Over 22 minutes taken off my 70.3 (half ironman) distance personal best
  • Over 8 minutes taken off my Olympic distance personal best
  • Personal best 10km run – at the end of a triathlon
  • Biggest UK race result with 3rd place in Age Group at Belvoir Castle
  • Had a lot of fun

That last one is important, probably the most important. I thoroughly enjoyed training this year. I had a new sense of purpose, a new sense of direction, a new confidence.

AI Endurance played a huge part. It gave me a structure I never had before and allowed me to reach new heights I honestly never thought I could reach.

So what happened? How did I make such gains? Well, I’ve been diving back into some of my data to have a bit of a review…

Did I simply train more?

It is a question I was intrigued to know. I didn’t really feel like I was training more – just training smarter. But what was the reality? To delve into this, I pulled my statistics from Strava (where I log everything, because if it’s not on Strava it didn’t happen right?) and looked at the period October to September for 2022-23 and then 2023-24. So how did it look?

Grant Blog 7 Monthly Volume

Figure 1: comparing month by month, training volume in the past two seasons

The blue shows my 2022/23 data, whilst the orange shows my 2023/24 data.

So did I train more? Well, yes. In fact, there was overall a 20% increase in hours over 12 months. But interestingly, not really during the triathlon season itself (April to September). In that period, I only increased by 14 hours, so just over 2 hours per month on average, which is 30 minutes more per week. Not really an increase, and as you’ll see from the chart above, in July and September I actually did more activity in the previous year!

But look on the left of the graphic. Every single autumn and winter month, I did more. In March I did a 10 day “run as many kilometers as you can” challenge so that explains that – although I did that as best I could within my programme set by AI Endurance. All of this set me up really well for the season ahead.

Normally in winter, my training has been very unstructured. The past few years Zwift really helped in maintaining my cycling fitness (previously I had really had to build it back up from spring onward) but swimming had always dropped completely and even running, I hadn’t had that balance across all 3 sports.

AI Endurance changed all that last November and the proof is in the pudding! Immediately I had set up a plan to take me all the way to the European Championships in June and with that pre-season structure in place, I thrived. I actually upped my hours in April in the app but as you can see from the graph, I had upped the amount of available hours without really increasing dramatically beyond what I would normally do in Spring and Summer. I have a family, I have quite a demanding job. It wouldn’t have worked. I needed to train smarter not harder. Ok, well a bit harder too…

So how did I train smarter? What does that mean?

I love running fast. I love pushing to my limits on a bike. I even quite like swimming now.

I think one of the things I first noticed with AI Endurance was the amount of training at endurance pace or power. Whilst I knew that in theory, I really liked the fact I could always see how much of the different type of training I was doing versus what I should be doing.

Grant Blog 7 AI Endurance Calendar

Figure 2: AI Endurance really visualizes your week ahead and the type of workout(s) on each day

That helped keep me grounded in the process and that was something I never really could track before.

So that structure – albeit with the flexibility to move workouts around, adapt when I missed the odd workout – it was really the foundation for the season ahead.

So how much was my successful season down to me and how much was it down to the AI?

Well, that is what I’ll be exploring next time…

Share on:

More Blog Posts


Running Training Zones and Cycling Training Zones

Running Training Zones and Cycling Training Zones

In this post, we give a short introduction to the running training zones and cycling training zones we use to structure your training. We use 5 training zones defined by pace for running activities and by power for cycling activities. AI Endurance calculates these zones for you individually based on your past training data.

Published May 9, 2020
DDFA Dynamical Detrended Fluctuation Analysis

DDFA Dynamical Detrended Fluctuation Analysis

by Markus Rummel. DDFA (Dynamical Detrended Fluctuation Analysis) is a new method to analyze the changes in your HRV data during exercise. It is an evolution of the DFA analysis based on the research in [1, 2] used by AI Endurance.

Published Sep 18, 2024
Your Customized Triathlon Meal Plan

Your Customized Triathlon Meal Plan

When it comes to triathlon training, nutrition plays a vital role in fueling your performance and optimizing your results. To help you reach peak performance, we have developed an advanced AI meal plan that takes into account your unique requirements, respects the calorie cost of your workouts, and accommodates your dietary preferences. With the power of evidence-based nutrition models, we ensure that your triathlon meal plan is tailored to support your goals.

Published Jun 12, 2023
Gear checklist for optimal data flow into AI Endurance

Gear checklist for optimal data flow into AI Endurance

AI Endurance is a data-driven training platform. In order to maximally benefit from the training and have the program be most personalized to you, you'll want the best possible data to flow into the platform. Here's a few recommendations on how to achieve this.

Published Sep 22, 2023