Aim #2a RCT (N=40): To estimate the effect of CRAFT-AI vs. TSF-CSO on IP treatment/healing entry (primary outcome) from intake to 3- and 6-month follow-ups. The primary outcomes of IP treatment/healing entry, and CSO functioning will be examined using an intent-to-treat analysis with a generalized linear mixed effects model with fixed effects of treatment and random effects of time. The model for IP engagement will be estimated using the binomial distribution and a logit link function. Additional analyses of CSO treatment sessions attended will be examined using a negative binomial hurdle model with a log link function, which will allow us to simultaneously test the effect of treatment on engagement (yes vs no) outcome and the count of sessions attended for CSOs with or without IP treatment entry. Demographic, substance use, and psychological variables for which the randomized groups differed significantly at baseline will be included as covariates in all models. In addition, attendance at other mutual help groups such as Nar/Al-Anon will be assessed and used as a covariate as necessary, especially for the CRAFT-AI CSOs. Models will be estimated using an intent-to-treat approach that analyzes data from all randomized participants. We will use maximum likelihood estimation for all analyses, which provides the variance-covariance matrix for all available data. While attrition in the previous RCT was less than 7% at 12 months, attrition analyses will determine whether there are any differences in study variables between those with missing and complete data. Study variables associated with missing data will be covaried in all analyses.