Generative AI consulting from operators who have shipped at enterprise scale

Our generative ai consulting brings operator-level judgment to your content and your roadmap. Use-case discovery, stack selection, leadership immersion, and commercial deployment of generative AI. Senior operators with deployment experience at McDonald's, AbbVie, Humana, and Abbott Labs.

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Our generative ai consulting brings operator-level judgment to your content and your roadmap

Why brands engage Chameleon for generative AI

Enterprise deployment, not demos

Akash Pathak has shipped AI personalization at McDonald's that built mobile commerce from launch to $1bn+ topline. AI for channel conversion at Humana saved 15-percent in ad cost while lifting conversion. AI immersion for 60-plus executives at Abbott Labs produced an aligned acceleration roadmap. He's an Adjunct Professor at Northwestern on AI marketing transformation.

Tool-agnostic stack design

We are not a generative-AI tooling vendor. We pick the stack that fits the engagement, evaluate ChatGPT vs Claude vs Perplexity vs custom RAG honestly, and run procurement on your behalf when the right stack is multi-vendor.

Use-case discovery before tooling

Most generative-AI engagements start with the tool and reverse-engineer a use case. We invert that: a 30-day diagnostic identifies the commercial use cases where generative AI moves a real number, then picks the tools and the workflow to deliver them.

What generative AI consulting actually requires in 2026

Most brands have run generative-AI pilots that did not survive contact with production. The gap is not the tools, which keep getting better. The gap is the operating model around them. Generative AI deployments succeed when three things are true.

The use case moves a real number

Customer acquisition cost down. Conversion rate up. Content production cost-per-asset down without quality erosion. Customer-service deflection up without satisfaction dropping. Akash Pathak's enterprise work at McDonald's, Humana, and Abbott Labs sequenced AI deployments to specific, measurable commercial outcomes; the discipline of starting with the number is what separates a working program from an interesting demo.

The stack fits the use case, not the other way around

Most brands inherit a stack instead of choosing one. The right tool for a content workflow is different from the right tool for a customer-service automation, which is different from the right tool for a sales-prospecting motion. Stack-design work also includes the integration surface, the data preparation, the security posture, and the procurement leverage with the vendor on your behalf.

The organization can actually operate the system

Generative-AI deployments fail more often on operating-model issues than on technical issues. The human-in-the-loop checkpoints, the escalation path when the model gets something wrong, the training and immersion required for the team to use the system effectively. Akash Pathak's Abbott Labs immersion is a representative reference for the leadership-development side of this work.

How engagements get sequenced

Engagements open with a 30-day use-case diagnostic, then stack design and procurement, then deployment sprints with leadership immersion in parallel. Interim leadership is available for brands without an internal AI-transformation lead.

What generative ai consulting actually delivers:

What our generative AI engagements cover

Use-case discovery and prioritization. Identify where generative AI moves a real commercial number; deprioritize the demos that don't.
Stack design and procurement. Tool-agnostic evaluation of ChatGPT, Claude, Perplexity, custom RAG, and content-ops tooling. We run procurement on your behalf when the stack is multi-vendor.
Marketing and content use cases. Editorial workflows, brand-voice fingerprinting, GEO/AEO for AI search, content personalization at scale.
Sales and customer-service use cases. AI-assisted prospecting, predictive lead scoring, automated outbound, customer-service deflection programs.
Data infrastructure and personalization. Vu Pham brings ML and data-science depth: predictive models, recommendation engines, automated dashboards.
Leadership immersion and team training. Akash Pathak's Abbott Labs program is a representative reference; we run similar programs scaled to mid-market through enterprise.
Interim and fractional senior leadership. Head of AI, head of digital transformation, fractional CMO with AI fluency.
Permanent senior-leader recruiting. AI-fluent transformation, data, content, and marketing leaders.

How We Compare

Feature	Chameleon Collective	Generative-AI tooling vendor	Big-4 consulting practice
Who runs the engagement	Former CMO, transformation lead, or AI operator	Customer success rep	Senior partner sells, juniors deliver
Enterprise AI deployment experience	McDonald's, AbbVie, Humana, Abbott Labs	Their own tool only	Variable, often academic
Tool-agnostic stack design	Yes	No	Yes but slow
Use-case discovery rigor	30-day diagnostic with commercial-number framing	Their tool's standard use cases

Frequently asked questions

Common questions from leaders evaluating Chameleon Collective for generative AI consulting.

No. We are a senior-operator consulting practice. We are tool-agnostic and pick the stack that fits the engagement instead of advocating for a tool we sell.

Typically a 30-day use-case diagnostic. We identify where generative AI moves a real commercial number (customer acquisition cost, conversion rate, content cost-per-asset, customer-service deflection) and deprioritize the pilots that don't.

Yes. Akash Pathak's pre-Chameleon work at Abbott Labs included delivering AI training and immersion for 60-plus global executives. We run similar leadership-immersion programs scaled to mid-market through enterprise.

Both, situationally. Most engagements use a mix of off-the-shelf tooling (ChatGPT, Claude, Perplexity) and custom RAG or workflow tooling where it's warranted. We run procurement on your behalf when the stack is multi-vendor.

Against a commercial number. Customer acquisition cost, conversion rate, content cost-per-asset, customer-service deflection rate, or whatever number the use case is meant to move. We set the baseline during the diagnostic and measure against it throughout.

Two to four weeks for interim or fractional leadership. Use-case diagnostic outputs typically land in 30 days. Deployment timelines depend on the use case and the existing stack.

Directly. No account-management layer between you and the operator.

Related Services

Recruiting a permanent senior AI or transformation leader

Our recruiting practice places AI-fluent transformation, data, content, and marketing leaders at the VP, SVP, and C-suite levels. Interim leaders frequently transition the permanent hire personally.

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