AI Transformation and Enablement Manager

Posted 18 Days Ago
Be an Early Applicant
Hiring Remotely in Belarus’, Minsk, BLR
Remote or Hybrid
30K-80K Annually
Mid level
AdTech • Agency • Digital Media • Marketing Tech • Social Media • Analytics • Big Data Analytics
Delve Deeper is a Performance Media Agency that helps clients grow their customer base by integrating the power of Tech.
The Role
The role involves leveraging AI to optimize workflows and processes within the agency, utilizing tools like n8n for automation and conducting thorough process analysis to enhance efficiency. It requires hands-on tool evaluation, effective implementation, and tracking of outcomes to ensure successful AI adoption across the organization.
Summary Generated by Built In

WHO WE ARE

Delve Deeper is a performance media agency focused on the charity and nonprofit sector, partnering with organizations that invest $5M–$20M annually in media. We help mission-driven teams maximize impact through advanced digital strategies that drive measurable, scalable results.

Our expertise includes advanced analytics, intent-based audience segmentation, full-service media management, and personalized creative—delivering a fully integrated, data-driven approach to growth.

More than a vendor, we serve as a strategic partner, helping organizations solve complex media challenges and turn them into clear outcomes. With decades of leadership experience, Delve Deeper is a trusted voice in the charity space.

We’ve also been named Built In Colorado’s “Best Places to Work” for five consecutive years, reflecting a culture that values performance, growth, and people. As a privately owned company, we move quickly, support our team holistically, and create meaningful opportunities for advancement.

ROLE OVERVIEW

You are accountable for making AI work across the agency — in practice, at scale, producing results people can feel. The foundation of this role is a rigorous understanding of how the business actually operates: where time goes, how decisions get made, where information stalls, and which processes are ripe for change.

This role has two connected areas of ownership. The first is fast, iterative workflow automation: mapping processes analytically, identifying where the agency is losing time, and getting working solutions built and adopted quickly via n8n. The second is broader AI enablement: identifying where the right AI tool — applied to the right operational problem — can change how the agency stores knowledge, surfaces information, or supports decisions.

Both areas start from the same place: a clear-eyed read of how work actually flows. This person brings business operations experience and process mining skills to that analysis — the ability to observe a workflow, decompose it analytically, identify where value is being lost, and design the right intervention. AI is the toolkit. Business acumen is what determines where to point it.

Speed and judgement are the twin engines of the role. On the automation side, the backlog moves fast and working solutions reach people fast. On the broader AI enablement side, you prototype and test before recommending — enough hands-on work to make confident calls about what actually belongs where. Across both, you are accountable for outcomes. Deployed tools that go unused are not counted as wins.

WHAT ARE YOU ACCOUNTABLE FOR

PROCESS ANALYSIS AND OPPORTUNITY IDENTIFICATION

You map how work actually flows across the agency — where time is spent, where handoffs break down, where decisions stall, and where the same effort repeats. You bring process mining discipline to this analysis: decomposing workflows into their component steps, quantifying the cost of each, and identifying precisely where AI intervention creates the most leverage. This analysis feeds both the automation backlog and the broader AI evaluation pipeline.

WORKFLOW AUTOMATION VIA N8N

From the process analysis, you identify and prioritise the workflows best suited for automation. You score opportunities by recoverable time and implementation effort, brief the implementation team to build in n8n, and sign off before anything reaches the people using it. The backlog is always live and prioritised. The team always has clear direction.

BROADER AI USE CASE IDENTIFICATION AND RECOMMENDATION

You are continuously scanning for AI opportunities that fall outside the automation backlog — the knowledge management problems, the information retrieval gaps, the workflow friction that structured tooling could solve without a custom build. You prototype and test candidate solutions hands-on before recommending them, and you own the recommendation. Examples: evaluating whether call transcripts belong in NotebookLM or a structured database; deciding how client information should be stored and surfaced to the working group; assessing whether a shared Claude Project or a purpose-built integration better serves a team's needs.

TOOL EVALUATION AND SELECTION

When a new need surfaces — from a team conversation, a champion observation, or your own analysis — you evaluate the right tool to address it. That means picking up the candidate tools, running them against real agency content and workflows, and forming a considered view before any recommendation is made. You build enough to know what you are recommending and why.

ADOPTION AND OUTCOME TRACKING

You track usage and outcomes across everything deployed — automations, knowledge tools, AI-assisted workflows. You measure what is working and what is getting traction, identify friction early, and direct iteration before patterns calcify. The measure of success is the agency operating differently: time recovered, knowledge accessible, decisions faster.

WHAT GOOD JUDGEMENT LOOKS LIKE

A significant part of this role is making good technology decisions quickly. The agency will surface problems. Your job is to evaluate the solution space, prototype where needed, and recommend the right approach. These examples illustrate the kind of thinking the role requires.

A process that looks simple but is not

A team reports spending several hours a week on a reporting workflow. Before recommending an automation, you map the full process: every step, every decision point, every handoff. You discover that two of the six steps are genuinely repeatable, two require contextual judgement, and two exist only because of a structural gap in how information is shared upstream. The automation brief covers the two repeatable steps. The structural gap becomes a separate recommendation. The judgement steps are left to the person doing them. That kind of decomposition — separating what can be systematised from what genuinely requires a human — is the core analytical skill this role demands.

Call transcripts

The agency generates call transcripts regularly. The question is how to store them, search them, and put them to use. You prototype the leading options — NotebookLM as a knowledge base, a structured folder system with AI retrieval, a Claude Project with uploaded sources, direct database storage with tagging — and you form a view based on how each performs against real agency content. You recommend the approach that is most useful for the people who need to access the information, and you own the implementation of that recommendation.

Client knowledge and working group access

Client-related information is scattered across emails, documents, and people's heads. The question is how to centralise it in a way the working group can actually use. You evaluate whether a shared workspace, a structured Notion setup, a Claude Project, or an AI-enhanced document repository best fits how the team works. You test the leading options against real client content before recommending, and you own the rollout.

Automation vs. AI-assisted workflow

A team is spending significant time on a repeatable task. You assess whether this is an n8n automation opportunity, a prompt playbook, a Claude Project workflow, or a combination. You make the call based on the nature of the task, the technical overhead of each approach, and the team's actual working patterns. Speed of the right solution matters more than elegance of the perfect one.

AI FLUENCY REQUIREMENT

This role requires someone with genuine, current fluency across the AI tool landscape — someone who uses these tools daily, has strong opinions about where each one excels, and reaches for the right one instinctively when a new problem surfaces.

AI Productivity & Knowledge

  • Claude / Claude Projects
  • NotebookLM
  • Gemini / Gems
  • Google Workspace AI
  • Notion AI
  • ClickUp AI
  • Other platforms as needed

Automation & Integration

  • n8n — hands-on capable
  • Webhook & API integrations
  • Conditional logic & branching
  • Google Sheets as a data layer
  • Error handling & monitoring
  • Other platforms as needed

CANDIDATE PROFILE

  • Change management experience
  • n8n — workflow building, reviewing, and quality sign-off
  • Genuine working fluency across the current AI tool stack
  • Hands-on tool evaluation — prototypes against real content before recommending
  • Prompt engineering — design and evaluate for specific, production-grade use cases
  • Claude Projects and custom AI workspace configuration and deployment
  • NotebookLM and knowledge synthesis tools
  • Google Workspace AI (Docs, Meet, Gmail AI features)
  • Structured data layers: Google Sheets, Notion
  • API and webhook concepts in automation environments
  • Process mining — decomposes workflows into steps, identifies value loss, quantifies automation opportunity
  • Business operations experience — understands how agency functions actually work, not just how they are described
  • Workflow analysis and process mapping across complex, cross-functional operations
  • Leverage prioritisation — scoring and sequencing opportunities under time pressure
  • Technology selection judgement — matches tool to operational problem based on real testing
  • Brief writing — translates process analysis into tight, actionable implementation specs
  • Outcome accountability — tracks adoption and results, not just delivery
  • Change management — moves resistant teams from scepticism to habitual use
  • High-trust relationship building at every level of seniority
  • Clear written communication — briefs, process maps, recommendations, escalation documents

WHAT WE OFFER

  • Work schedule: 12 pm- 8 pm
  • A competitive salary and an annual bonus opportunities
  • A promote from within culture and the chance to define your career growth
  • Health and dental insurance (after trial period)
  • 28 calendar PTO days
  • Flexible sick days policy backed by full 100% short-time disability coverage
  • Brand new office in Minsk, built and designed exclusively for DELVE
  • English language tuition covered for specific positions
  • Compensation of sports facilities (after trial period)
  • Mental health reimbursement (after trial period)
  • Generous employee referral bonuses
  • Hybrid Working Model: Tuesdays, Wednesdays, Thursdays in office with the option to work from home on Mondays and Fridays

Please submit your CV in English!

    Top Skills

    APIs
    Claude
    Clickup Ai
    Gemini
    Google Workspace Ai
    N8N
    Notebooklm
    Notion Ai
    Webhooks
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    The Company
    HQ: Louisville, CO
    150 Employees
    Year Founded: 2011

    What We Do

    Delve Deeper delivers digital marketing management, first-party data science and consulting, and adtech/martech systems integration & reselling on a global scale. We connect the dots between data and technology in media by identifying our clients’ super fans, deterministically finding more of them, and converting them online in the most effective way.

    Why Work With Us

    We act as one highly functioning team that is powered by our professional “Fire in the Belly”, with a passion for creating exceptional value by delighting our clients and creating an engaging work environment for our team members. Our culture emphasizes professional development in an environment where everyone can have an impact.

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