Sara Davison

AI Build Lab

Co-Founder

Maivenly

AI Strategy | Implementation | Enablement

The AI Agency Where ROI Comes Before AI. 

At Mavenly, we prioritize real business results over just adding more technology. We’ve seen AI in action and know its potential, but our focus is on driving substantial ROI above the technology itself.

Our systems-based approach minimizes risks and ensures sustainability, especially important with the changing technology landscape. Here’s how we do it:

	•	Strategy: Starting with your goals & objectives & aligning AI to that - not the other way around
	•	Small & Compounding Implementations: We start with small, compounding implementations, removing bottlenecks and building space for growth, ultimately scaling with compounded AI capabilities
	•	Enablement: Equipping your team with the skills they need to continue to be successful long after our engagement 

We start small, removing bottlenecks and building space for growth, ultimately scaling with compounded AI capabilities.

We partner with ambitious, growth-oriented businesses who know the power of AI and need to partner with the right people to harness it.

Founder & Owner 

Maven

Teaching professionals how to implement AI and agentic workflows that scale businesses

AI Educator & Instructor

Light Magic AI

A group of AI Practitioners that come together to work on projects

AI Practitioner

The AI Exchange

This role is with The AI Exchange as part of the AI Ops Fellowship program asAI Ops Curriculum Developer.

AIOps is an emerging field that will play a critical role in every company's AI adoption strategy since it is centred on the act of *applying AI* in our work & organisations.
It's a huge privilege to be part of a team that is at ground level, defining & leading the direction of this highly demanded skillset & expertise which is only set to grow.

In this role, I'll be helping develop curriculum for AIOps courses working closely with the AIX team to create engaging educational content and resources that will support the learning, AI adoption, and career advancement for AIOps professionals.

With demand skyrocketing for skillsets of professionals who can guide companies into actually applying AI within their work, there is an urgent need for accessible, impactful educational programs to support this a new generation of AIOps practitioners & orgs looking to adopt this function.

Previous to this fellowship I was a very active leader in the AIX community & hosted weekly sessions with some exceeding 100 attendees.

AI Ops Curriculum Developer 

Kinly

I founded Kinly, an online marketplace & ecosystem where parents can easily discover and instantly book local services that support their families, when becoming a mom myself, I was looking for a convenient way I can get things done online for my family. I was astounded in this day and age, that there was nothing on offer for new moms to find the support they needed easily. The new mom economy is a $50bn a year market opportunity in the US (Forbes). 
I also believe that Every Home & Every Child deserves to have access to the services that support their wellbeing & help them thrive.  Every booking on our platform enables us to support children and communities around the world with life-supporting impacts (food, clean water, access to medication & education).  
By connecting thousands of parents to local services in both Australia & the US we have been able to impact thousands more lives across the world.

Founder

Responsible for partnering with Customer Strategy, third parties and product development to ensure Customer Value Propositions requiring a product and / or technology component can be commercialised and designed into tangible business opportunities.
A strong focus on consumer cards and rewards programs.
Accountable for designing and delivering CVP strategies into tangible and commercial opportunities through product design and technical integration, with an appreciated application of customer insights, market, competitive and commercial considerations.
Leading the scoping, recommendation and business casing of key strategic initiatives, with a focus on third party relationships.
Leading the formulation of negotiation strategies and contracts management for key third party relationships.
Responsible for evaluating, scoping, engaging and managing strategic partnerships and third parties to determine suitability and synergies from a strategic, commercial, product value and technical perspectives.

Manager - Proposition Commercialisation, Product & Customer Solutions

Responsible for developing and managing the largest corporate alliances and partnerships on behalf of the Bank. This role is focused on the development of rewards programs and products, enhancing commercial value and innovative solutions by leveraging our alliance and partnership relationships; their assets, capabilities, infrastructure and technology. 

Alliance Assessment & Development
- Assessing new alliance and partnership opportunities
- Evaluating and reporting on the commercial value and profile of the relationship
- Identifying and executing on strategies to develop further commercial value 
- Developing an intimate understanding of the partner business, its objectives and strategies, identifying key areas of synergies and opportunities
- Development of commercial strategies for the relationship that align with the Bank's priorities and objectives 

Contract Management
- Assessing and reviewing contractual arrangements
- Developing negotiation strategies to deliver value for both parties
- Leading contract negotiations in collaboration with other business devisions, e.g. legal & procurement

Solution Development
- Identifying and developing key areas of opportunities between the parties
- Working alongside marketing, product and innovation teams to progress the development of opportunities to solutions
- Contributing to and informing requirements gathering
- Ensuring all legal and contractual arrangements and protections are in place 
- Overseeing the end to end process of third party engagements for the development of solutions, from concept creation to Go Live

Relationship Management & Stakeholder Engagements:
- Overall responsibility for partnership and alliance relationships
- External engagement, typically with Heads of, GMs and CEOs
- Internal stakeholder management, to Director and CEO level
Internal stakeholder management is a significant focus of this role due to the substantial nature of these relationships.

Manager - Commercial Development, Strategic Alliances

Kiwibank

Responsible for engagement and acquisition of large corporate clients for a significant national digital identity verification solution.
Complex technical solution selling with a focus on the financial services and utilities sectors.

Business Development Manager, Agency Services

Responsible for managing a multi million dollar portfolio of clients for the provision of payment solutions.

Key Account Manager, Payment Services, Kiwibank

Responsible for corporate sales of prepaid cards with a national territory.

Business Development Manager - Prepaid Cards

Scale A Business With AI & Agentic Workflows -> 8,000+ People Educated In 2025's Most In Demand Skill | CEO & Founder | AI Agents & Agentic Workforce | Done For You Or I Show You How | AI Speaker & Educator

I upskill individuals and teams on how to become the 1% with agentic AI

honestly, AI didn’t change my life overnight.
(but when success happened, it shifted gears fast)

it was messy.
i doubted myself a hundred times.
i almost quit more than once.
sometimes in the beginning i thought, maybe i’m just making this all up

every step forward came when i decided to just keep going. 
not because i had the “right” prompt or because i had a plan that made sense.

because i sat in the messiness of it all and was stubborn enough to figure it out.

and that’s what i see now in the 330 people who started with us Monday.
most of them aren’t technical.
some are nervous. 
some don’t know where this will take them.
they just have a hunch.

and that’s exactly the point.

the tools aren’t the hard part.
it’s deciding you’re in and staying in long enough to bend reality your way.

This is the skill to develop as an agentic AI practitioner - so every time the topic gets airtime i feel the need to talk some more about it ...

Palantir & now many other companies call this role & skillset the Forward Deployed Engineer (YC's job board shows 100+ startups hiring for this role - up from zero just 3 years ago).

We've been calling this the new agentic practitioner (because we don't believe you necessarily need to identify as an engineer)
essentially - the people who bridge AI capabilities with real-world workflows.

You don't need a CS degree to think like an FDE. You need to embed with customers and understand the actual workflow being automated. Sit side-by-side with the tractor sales regional manager to understand how promotions really work. 
Map the real decision trees, the reality of the workflows, not the ones in the org chart or limited to the SOPs.

This deep workflow understanding is what separates AI systems that actually work from flashy demos that never scale.

That's what gets you the : "Holy heck take my money!!" effect

What this means for your AI practice:
- You're not focused on just the technical skill of writing prompts - your prompts are deeply informed and designed by your findings 
- You're creating evaluations based on actual user behavior, not theoretical benchmarks
- You're building with guardrails because you know where things break  in daily ops,  that make AI outputs reliable in production
-You're practicing the deep customer discovery that turns AI capabilities into business value vs building what you think they want

The gap between AI capability and adoption is huge.

 "Capabilities race ahead while the world feels increasingly banal." 

This is in bridging the gap between what AI can do and what organizations can actually adopt.

The best agentic practitioners think like Forward Deployed Engineers: they understand that the hardest part isn't the technology, it's the human workflow integration.

They act as translators of the capability of the technology with the very acute business need and actual reality, and they create desire in their solution.

Instead of starting with “what do I want AI to do?”
Start with “what does good look like when it’s done?”

This shift isn’t cosmetic for the sake of it, it’s actually foundational to your workflow design.

And when you’re building AI agents, it’s non-negotiable. 

Agents aren’t just spitting out rote answers or automating clicks. 

They’re reasoning, planning, executing with degrees of autonomy.

It’s squishier. 

Which means the definition of “good” matters even more.

If you don’t define it upfront, you’ll end up in an endless loop of tweaking prompts and still wondering why it feels off.

Before you start building, think through:
– What does a good draft look like?
– What does a good decision look like?
– What does a good handoff to a human look like?

Once those criteria are clear, you design backwards. 

Prompts, context, guardrails, evaluations, they stop acting as disconnected tactics and instead align as a system serving the definition of “good.”

You might be surprised someone like me would say something like this, but ...

Most people think all the power of AI comes from the model.
It doesn’t.

The real power comes from things you can control including: context, structure, and evaluation.
	•	Context is the fuel you feed it. The background, the examples, the raw data that makes its answers relevant. 
	•	Structure is the shape you demand back. Formats, templates, or roles that make outputs predictable instead of random. (Example: “Summarize this call in 3 bullets: pain points, opportunities, next steps.” That’s structure at work.)
	•	Evaluation is the standard you hold it to. Defining what “good” looks like so you can measure and improve instead of endlessly tweaking prompts.

When you combine these three, you stop playing hours of trial-and-error with your outputs.

McKinsey dropped their lessons from 50+ AI agent implementations. 
Here's what we learned building alongside their insights.

Their research confirms what practitioners see repeatedly: 
most AI projects fail because of approach, not tech limitations.

McKinsey identified key patterns - workflow focus, evaluation systems, human collaboration design.

Building on their findings, here are 3 tactical insights from our experience:

1. The Real Workflow Lives Beyond Documentation
McKinsey emphasizes workflow mapping. 
What we've found is there's often a gap between documented processes and operational reality.
Real work happens in Slack DMs, institutional memory, and workarounds people use when systems break.
 Understanding this requires embedding with users, not just interviewing stakeholders or copy pasting and SOP.

2. Depth Before Breadth Creates Better Scale
McKinsey highlights reusable components. 
IMHO yes, and timing matters.
An approach that we found works is build for one person's workflow first. 
Make their day 10x better. 
Then extract patterns to apply broadly.
Starting too broad often creates generic tools that work poorly everywhere.

3. Evaluations Drive Everything, Not Just Quality
McKinsey discusses evaluation & highlights for trust-building. 
We see it as the core intelligence system.
Your evaluation framework captures what separates top performers from average ones. 
It informs your prompts, system instructions, and agent design - not just final quality checks.

These insights point to a specific capability:
people who understand domains deeply, build from reality, and translate between tech capability and business needs.

YC's job board shows 100+ startups now hiring "Forward Deployed Engineers" - up from zero 3 years ago.

We call this the new agentic practitioner - bridging the gap between what AI can do and what organizations can actually adopt; their current reality, and quickly prototyping the solution that drives that: 
"holy heck, take my money" effect

Practitioners who master domain immersion and evaluation design, bridging the gap between the capability of the technology and what the clients actually want, will build the agents that actually transform work.

Will this be you in 2026?

This is why we say to our crew to think past using AI to just 'go faster', that's not the name of the game we're playing here ...
"The lesson here is that these teams just tend to be far more ambitious in what they push agents to do. Most existing teams and companies will be happy about incremental gains and stop short of doing the really big changes in how they work. And doing so is going to provide at least a temporary advantage to those that adapt to these news ways of working."

CEO at Box - Intelligent Content Management

There’s going to be a huge gap in execution velocity for the foreseeable future between the individuals, teams, and companies that adapt (or create for the first time) their workflows to work with AI agents vs. those that don’t. 

In the past few months I’ve talked with a number of new startup founders who are operating in a totally different way than the rest of the world right now.

Most of their work, particularly in engineering to start with, is oriented around how to make agents effective. There’s a focus on hyper specific prompting, a bigger orientation around getting specs perfectly right, running many agents in the background in parallel, focusing on code reviews vs. coding, and a bunch of other new workflow practices are actually what it takes to make agents work at scale.

And while coding is ahead of the curve right now in agentic workflows, it’s clear this pattern will start to emerge across most other domains over time. Leverage is going to show up everywhere: the ability to generate 10X more marketing output, process deals way faster due to automated legal workflows, handle customer support and success operations in faster ways, and so on. 

The lesson here is that these teams just tend to be far more ambitious in what they push agents to do. Most existing teams and companies will be happy about incremental gains and stop short of doing the really big changes in how they work. And doing so is going to provide at least a temporary advantage to those that adapt to these news ways of working.

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