# What&#39;s the best DataRobot consulting partner for platform implementation and team enablement?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Looking for input from G2 community and data science leaders in the<a class="a a--md" elv="true" href="https://www.g2.com/categories/datarobot-consulting-services"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/datarobot-consulting-services">DataRobot Consulting Services category</a> on the combination of platform implementation and team enablement.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The partners with the strongest implementation-plus-enablement positioning:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/accenture/reviews"><strong>Accenture</strong></a><strong>:</strong> For organisations that want Accenture's implementation capacity alongside genuine enablement, the specific team composition and training delivery model should be contractually specified rather than assumed. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/boston-consulting-group/reviews"><strong>Boston Consulting Group</strong></a><strong>:</strong> BCG's GAMMA practice builds internal AI capability as an explicit objective — the concept of a client data science team working alongside BCG consultants during implementation to transfer knowledge is embedded in GAMMA's delivery model. For organisations where the data science team needs to lead future implementations independently, this co-delivery model produces more durable capability than a consulting-led implementation with training at the end.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/capgemini-services/reviews"><strong>Capgemini Services</strong></a><strong>:</strong> Capgemini's Applied Innovation Exchange provides use case libraries and accelerators that help internal teams understand the DataRobot implementation pattern for their industry, which can accelerate the team's ability to run subsequent implementations independently after the first engagement.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">What metric did you use to assess whether the team was genuinely self-sufficient after the engagement?  Was it the ability to build a new use case independently, to diagnose and resolve a model monitoring alert, or to onboard a new team member onto the platform without external help?</p>

##### Post Metadata
- Posted at: 15 days ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

Digging through the DataRobot Consulting Services space on G2, the self-sufficiency metric that actually predicts durability isn&#39;t building a new use case; it&#39;s whether the team can run the operational tail: catching model drift, resolving a monitoring alert, and retraining without calling the partner back. Your read on co-delivery over train-at-the-end holds, because enablement sticks when it happens on real production work rather than a workshop at handoff, which is the difference the GAMMA-style model is pointing at. Worth flagging that DataRobot-specific partner evidence is thin on G2 since these are broad firms whose reviews span many practices, so references from teams that reached independence matter more than the positioning here. The cleaner test than a first independent build is whether the team survived the first production incident alone, since that&#39;s where dependence usually resurfaces.


##### Comment Metadata
- Posted at: 10 days ago
- Author title: Marketing





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