One thing you can try if you have enough forecasts (which may be the case in technological forecasting) is empirical recalibration of CIs: your model-based CIs only report sampling error without any kind of model or other error and will be overconfident. So you can expand the CIs by a certain amount corresponding to how bad that turns out to be. A particularly relevant forecasting example of doing is in “Disentangling Bias and Variance in Election Polls”, Shirani-Mehr et al 2018, where they observe that polling upsets like Brexit or Donald Trump are indeed surprising if you relied solely on sampling error CIs (which turn out to be only half the width of the total error CI including the systematic errors), but are normal overall.
One thing you can try if you have enough forecasts (which may be the case in technological forecasting) is empirical recalibration of CIs: your model-based CIs only report sampling error without any kind of model or other error and will be overconfident. So you can expand the CIs by a certain amount corresponding to how bad that turns out to be. A particularly relevant forecasting example of doing is in “Disentangling Bias and Variance in Election Polls”, Shirani-Mehr et al 2018, where they observe that polling upsets like Brexit or Donald Trump are indeed surprising if you relied solely on sampling error CIs (which turn out to be only half the width of the total error CI including the systematic errors), but are normal overall.