The idea of predicting future performance based solely upon the past values is called univariate time series modelling (as compared to also using predictor series which is called multivariate time series modelling). Time series modelling requires formulating a customized model that may have auto-projective components (e.g., last year at this time) and/or deterministic components (e.g., level shifts / multiple time trends /seasonal dummies). In the absence of good software or a strong analytic background you may find yourself using ineffective methodology that is based upon an assumed model or a process that tries a few models and picks the "best" from a pre-defined list. Neither of these work, as the model needs to be customized to the data in order to provide reasonable results.
Rather than telling your boss of your deficiencies, provide him/her with choices to select from. Some of those choices might be 1: find a local expert in time series / predictive models to help; 2: find a software company that specializes in this area and also provides consultancy/training in these methods. In either case you will need to implement a computer-based solution and the question will be "should you make or buy?" . People who try to make without knowledge of the subject are "planning to fail."
In summary I would take a small set of your time series and engage the software companies that offer "expert systems in time series modeling" and contrast/compare their methodologies and modeling strategies. You might find that the bigger the company, the poorer the solution, as big companies provide generalized solutions but often fail to provide specialized solutions. While you may think that your problem is "everybody's problem" and generic solutions using buzz words like "data mining" should be the answer, they simply don't provide good specific solutions, especially in the area that concerns you. Learn from the experts who design/market and support "time series modelling/forecasting" and learn from them what they are doing and more importantly how they are doing it! Black-box solutions are never acceptable: demand transparency, as transparency leads to understanding which could lead you to roll-your-own.
I am commercially involved with one of those companies, so my advice may be classified as either "good advice" or "self-serving." I firmly believe the former. Select the best in terms of price/performance. Evaluate which would be cheaper, to make or to buy. Faced with a similar decision, I recently decided to buy a new 2011 Lexus as compared to building my own car which I would have named "the SHAMROCK". Guess what decision I made!
One final point, if I had a simple problem like how to calculate an ANOVA/OLS model, a statistical generalist/computer program would be able to help me. If I wanted to find out where the statistically significant change points in a time series were or how to distinguish between level shifts and true trends or how to detect where either the parameters of the model changed or where the variance of the errors changed, I would not be using generalized data mining tools but would seek qualified help.