Siva Dosapati founded AI20 Labs, where the team builds and embeds AI products and workflows in businesses, with a focus on getting systems into production and improving how work gets done.
Most useful for practical AI adoption advice and examples of moving pilots into production.
Best for
B2B software companies selling to operations teams with stalled AI pilots.
Not for
Consumer or direct-to-consumer brands seeking paid sponsored posts with reach targets.
Key takeaways
They split AI into assistant, workflow, and agent based on the task.
They recommend starting with a workflow when the process already exists.
They report B2B CRM onboarding completion rising from 17% to 50%.
Check first: Confirm whether sponsorship is available and what terms apply before planning a placement.
Main ideas
Most businesses do not need an AI agent yet · Measure AI by the friction that disappears, not by the model · The pilot-to-production gap is the problem worth organizing around
Siva Dosapati founded AI20 Labs, a team whose work spans AI infrastructure and AI products for businesses.
In their LinkedIn posts, they focus on a practical question: how does AI change the work people actually do?
Their examples range from simplifying product workflows and improving onboarding to building systems for enterprise operations.
The posts present shipping and adoption—not a model demo—as the point where AI proves useful.
Follow their work for concrete examples of AI in production and a clear-eyed case for choosing an assistant, workflow or agent based on the task.
Dosapati makes AI choices easier to reason about by starting with the work, not the technology label.
In one post, they distinguish an assistant that helps a person move faster, a workflow for a repeating process, and an agent for work whose next step requires judgment.
They recommend starting with a workflow when the process already exists, because the time saved is easier to measure.
Other posts show how they apply that principle: reducing a solar-tool design workflow from 22 inputs to 8, and reporting B2B CRM onboarding completion rising from 17% to 50%.
They also point to the harder tests of production AI: latency and cost at scale, and whether experienced operators will adopt a system.
The strength of this work is its connection between a specific operational problem and a measurable change—not a claim that every business needs an agent.
Siva Dosapati argues that AI adoption fails less from model quality than from shipping and workflow design.
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They say most businesses do not need an AI agent yet and sort work into assistants, workflows and agents. AI20 Labs, which they founded, measures results by the friction that disappears, embeds with clients instead of handing over a deck, works at both the inference-infrastructure and enterprise-operations ends of the market, and invests its own team in established businesses in exchange for equity or a share of net-new revenue.
Most businesses do not need an AI agent yet
Dosapati argues that asking for an AI agent is usually the wrong starting point.
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They say they make this point on almost every first call, right after prospects ask for an agent.
Their test sorts work into three shapes: an assistant when a person needs to move faster but will still approve the output; a workflow when the same process runs every day; and an agent when the next step changes each time, after someone has decided what it may touch and when a person takes over.
The rule: needs help, use an assistant; repeats, use a workflow; needs judgment, consider agents.
Most teams should start with the repeating process, they say, because it already exists and the hours saved are easy to count.
One qualification: AI20 Labs also builds persistent agents for engineering organizations, so the advice is conditional, not a blanket rejection of agent work.
A home services company gets a request through its website.
Someone has to read it, log it in the CRM, write a reply and pass it to the right technician.
Dosapati treats that as a workflow, not an agent: the steps repeat and the saving is measurable.
Measure AI by the friction that disappears, not by the model
Dosapati's recurring examples show workflow before-and-after results, not model benchmarks.
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They say the key number in each case is the friction that disappeared, and that results came from redesigning how the work gets done, with the model as one component.
AI20 Labs positions itself as an operating partner that stays past launch until the system is part of how the company runs.
The posts describe the solar installer result in two ways: a design workflow on a tool reduced from 22 upfront inputs to 8, and a 22-step workflow cut to 8.
Confirm the exact metric before quoting it.
Reported outcomes include a solar installer design workflow cut from 22 inputs to 8 with 55% fewer interactions; B2B CRM onboarding completion moving from 17% to 50%; an edge AI platform running 1.9 million inference requests with 876,400 device registrations; and a fintech client going from idea to production MVP in six to eight weeks.
The pilot-to-production gap is the problem worth organizing around
Dosapati says AI20 Labs started after watching too many companies spend a year on AI that never left a pilot.
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They cite an MIT figure that 95% of generative AI pilots deliver no measurable impact on the P&L.
Their conclusion is that the technology works, but shipping is where it breaks, so the company builds what they call the other 5%.
The priority is embedding with the client and shipping production systems rather than handing over a deck.
The post does not source the MIT figure; check the underlying report before repeating it.
The post describes production AI shipped across solar, fintech, CRM and edge compute by a small senior team that embeds with the client. It also announces a two-week run of posts on taking AI from idea to production.
AI20 Labs works at both ends of the market, with a different failure mode at each, Dosapati says.
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At the infrastructure end, it builds for AI companies powering hundreds of models used by millions of users.
There, latency budgets are measured in milliseconds and cost per token decides whether a product has a business; a system that works in a demo but buckles at scale has taught nothing.
At the enterprise end, it builds for operators in healthcare, manufacturing, financial services and energy.
There, the system has to survive contact with people who have done the job for twenty years and will route around anything that slows them down.
The priority is adoption among experienced operators, not model quality alone.
Shipped work includes inference infrastructure; configuration and quoting tools that collapsed a dense multi-step workflow into a single dashboard for a manufacturer's distributor network; natural-language analytics that let non-technical staff ask questions about their sales and margin data and get charts back; and persistent agents placed inside an engineering organization, where they attend meetings and accumulate context over months until they can answer questions a new senior hire could not.
Give founders a low-risk path from idea to tested demand
One post addresses founders, creators and business leaders with ideas that never launch.
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Dosapati lists the constraints: being caught up in daily work, limited budgets, missing technical skills or a team, and uncertainty about whether the idea is worth pursuing.
The offer is to build a proof of concept, validate it with prospective customers, market-test it publicly to gauge demand and viability, and make payment depend on whether the delivered work is seen as valuable.
The post names example categories rather than clients: AI models that unlock revenue from data, tools that sync data across multiple CRM systems, AI assistants for solopreneurs and business leaders, engineering productivity systems, sales forecasting for manufacturing, and bank-health monitoring from public data.
A founder with an unlaunched idea can get a prototype, customer validation and a public market test before paying for perceived value.
For anyone deciding what to build inside a product, this post gives the clearest answer on which kind of AI suits a given task.
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It is the conceptual entry point for the rest of the posts.
Siva Dosapati writes that most businesses do not need an AI agent yet, a point that comes up on almost every first call, usually right after someone asks for one.
The post sorts work into three kinds.
An assistant helps a person move faster, such as by drafting a proposal from discovery-call notes, with the owner still reviewing it before it goes out.
A workflow handles a process that runs every day, such as a home services request arriving on a website and needing to be read, logged in the CRM, answered and routed to a technician.
An agent handles work where the next step changes each time, such as prospecting: one company is an obvious fit, another needs more digging and a third is not worth the time.
That only works if it is clear what the agent may touch and when a person takes over.
The rule: if the work needs help, use an assistant; if it repeats, use a workflow; if it needs judgment, look at agents.
The post recommends most teams start with a workflow, because the process already exists and the hours saved are easy to see.
For brands planning a campaign
For a SaaS company trying to raise AI adoption in its product, this offers a scoping framework: automate an already-defined, repeating workflow with measurable savings before adding an agent. It also signals the advice this creator would give a partner, a useful measure of fit for an advisory or build engagement.
Read it before accepting general statements about production results.
The post opens with the observation that most AI conversations start with a company knowing AI should change how it operates but unable to picture what that looks like on a Tuesday morning.
It lists outcomes from published work: a design workflow on a solar installer tool cut from 22 upfront inputs to 8, with interactions down 55%; onboarding completion on a B2B CRM up from 17% to 50%; an edge AI platform that has executed 1.9 million inference requests with 876,400 device registrations; and a fintech client taken from idea to a production MVP in six to eight weeks.
The argument is that the number that matters is the friction removed.
Those results came from redesigning how the work gets done, with the model as one component.
The post frames AI20 Labs as an operating partner that stays past launch until the system becomes part of how the company runs.
For brands planning a campaign
These are figures a brand can benchmark against its own funnel: activation and completion rates, interaction counts and throughput. The framing also clarifies what the creator sells: workflow redesign that measurably reduces friction, rather than model selection or prompt work.
This post explains what the team builds and for whom in more technical detail than the results posts.
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Check it to see whether its working standards match your product's.
The post describes work at two ends of the market.
At the infrastructure end, the team builds for AI companies powering hundreds of models used by millions of users, where latency budgets are measured in milliseconds and cost per token can determine whether a product has a business.
A system that works in a demo but buckles at scale has taught you nothing.
At the enterprise end, it builds for operators in healthcare, manufacturing, financial services and energy, where systems have to work for people with twenty years on the job who will route around anything that slows them down.
Shipped work includes inference infrastructure; configuration and quoting tools that collapsed a dense, multi-step workflow into a single dashboard for a manufacturer's distributor network; natural-language analytics that let non-technical staff query their own sales and margin data and get charts back; and persistent agents that sit inside an engineering organisation, attend meetings and accumulate context over months until they can answer questions a new senior hire could not.
The post says the team is investing in a small number of established businesses, with returns tied to what it produces.
For brands planning a campaign
The post names two acceptance bars a product team can adopt: technical performance (latency, cost per unit) and adoption among experienced users who will abandon slow tools. It also shows the creator works with both AI vendors and non-technical enterprises, helping buyers judge whether their context is represented.
The commercial arrangement is a first-order question before any partnership conversation, and this post states it plainly.
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AI20 Labs says it is investing its own team—including AI strategy, product design and engineers—in established businesses, with no consulting fee, in exchange for equity or a share of the net-new revenue the work creates.
The stated rationale is that the best work came from spending enough time inside a business to find the unnamed constraint.
That takes months and a team willing to be measured on what happens afterwards, which a fee-based engagement rarely provides.
The post says returns depend on being right and that the team is looking at established businesses with $2 million or more in ARR where AI could clearly change the economics.
For brands planning a campaign
This sets the deal shape: alignment on measured outcomes rather than fixed-fee delivery, with a qualification threshold that may exclude smaller products. For a larger SaaS company, it suggests a possible equity or revenue-share engagement; for a smaller one, the creator may not take the work.
The clearest statement of the problem the creator addresses: the same one faced by brands trying to improve AI adoption.
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The post says Dosapati started AI20 Labs after watching too many companies spend a year on AI that never made it out of a pilot.
It attributes a figure to MIT from that year: 95% of generative AI pilots deliver no measurable impact on the P&L.
The post concludes that the technology works, but shipping is where it breaks.
The team says it builds the other 5%, having shipped production AI across solar, fintech, CRM and edge compute while embedded with the client rather than handing over a deck.
The post announces a two-week run of posts on taking AI from idea to production.
For brands planning a campaign
The failure mode named here—pilots that never reach production—is often what product teams hope outside help will prevent. The post attributes the MIT figure to that year; check the underlying report before repeating it in campaign materials.
Build-in-public snapshots as the publishing format4
The pilot-to-production gap as the defining problem2
Measure AI by the friction that disappears2
Building for people who will route around anything slow2
Outcome-tied commercial model instead of consulting fees2
Match the AI tool to the work: assistant, workflow, or agent1
Two audiences: inference infrastructure and enterprise operations1
A validation path for founders sitting on an idea1
Match the AI tool to the work: assistant, workflow, or agent
Dosapati argues that most businesses do not need an AI agent yet, and says so on almost every first call, usually right after a prospect asks for one.
Read more
The post splits AI work into three shapes: a person who needs to move faster and wants a first draft (an assistant, with the person still approving the output); a process that runs the same way every day, such as a home services request that must be logged, answered, and routed (a workflow that runs the moment the request arrives); and a next step that changes each time, such as prospecting, where one lead is an obvious fit and another is not (an agent, once someone has decided what it may touch and when a person takes over).
The stated rule: help means an assistant, repetition means a workflow, judgment means an agent.
They recommend most teams start with the repeating process because the hours saved are already countable.
A three-way test—needs help, repeats, or needs judgment—with a recommendation to start with an existing process so the savings are visible.
For brands planning a campaign
For brands selling AI features, position each capability by the job it replaces, not by whether it qualifies as an agent. Promising an agent for work that simply repeats invites the pushback Dosapati gives prospects.
The pilot-to-production gap as the defining problem
Dosapati says AI20 Labs started after watching companies spend a year on AI that never left a pilot, and cites an MIT figure that 95% of generative AI pilots deliver no measurable impact on the P&L.
Read more
The stated position is that the technology works and the shipping is where it breaks, so the company builds what it calls the other 5%. Supporting claims in the same material: production AI shipped across solar, fintech, CRM, and edge compute, delivered by a small senior team that embeds with the client instead of handing over a deck.
For brands planning a campaign
Credibility rests on what happens after launch, not on pilot results. A partner promoting AI services should expect questions about post-launch outcomes and lead with shipped systems and measured change.
Dosapati reports results as before-and-after workflow numbers: a solar installer design tool cut a 22-input setup to 8 and reduced interactions by 55%; a B2B CRM moved onboarding completion from 17% to 50%; an edge AI platform has run 1.9 million inference requests with 876,400 device registrations; a fintech client went from idea to a production MVP in six to eight weeks.
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The stated thesis is that the number that matters in each case is the friction that disappeared, and that those results came from redesigning how the work gets done, with the model as one component inside that. A separate company snapshot repeats the 22-step-to-8 and 55% figures and lists eight products shipped across seven industries.
Before-and-after workflow metrics: input steps, interaction counts, completion rates, and time from idea to production.
For brands planning a campaign
Concrete before-and-after metrics travel further than capability claims. For a campaign about workflow improvement, ask for workflow-level numbers—steps removed, completion rates, and time to production—rather than model benchmarks.
Building for people who will route around anything slow
On the enterprise side, Dosapati describes work for operators in healthcare, manufacturing, financial services, and energy, where the test is different: the system has to survive contact with people who have done the job for twenty years and will route around anything that slows them down.
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The same post describes persistent AI agents placed inside an engineering organization that attend meetings and accumulate context over months, to the point where they can answer questions a new senior hire could not.
For brands planning a campaign
Adoption, not model quality, is presented as the deciding factor. Messaging aimed at experienced operators should show how the tool removes steps; anything that adds one gets abandoned.
Two audiences: inference infrastructure and enterprise operations
AI20 Labs is described as working at both ends of the market.
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At the infrastructure end it builds for AI companies running hundreds of models used by millions of users, where latency budgets are measured in milliseconds and cost per token decides whether a product has a business, and where a system that works in a demo and buckles at scale has taught nothing.
At the enterprise end it builds for operators in healthcare, manufacturing, financial services, and energy.
Named shipped work includes inference infrastructure; configuration and quoting tools that collapsed a dense multi-step process into a single dashboard for a manufacturer's distributor network; and natural-language analytics that let non-technical staff ask questions of their own sales and margin data and get charts back.
For brands planning a campaign
The creator speaks to two distinct audiences.
An AI infrastructure company buys latency and cost-per-token competence; an enterprise operator buys workflow change and adoption.
A campaign should pick one audience rather than blend them.
Outcome-tied commercial model instead of consulting fees
Dosapati describes AI20 Labs investing its own team (AI strategy, product design, engineers) into established businesses with no consulting fee, taking equity or a share of the net-new revenue the work creates.
Read more
The stated target is businesses with $2M+ ARR where there is a clear place AI could change the economics. The stated reason: the best work came from being inside a business long enough to find the constraint nobody had named yet, which takes months and a team willing to be measured on what happens afterward.
Before a campaign, ask whether equity or revenue share applies, how net-new revenue is defined, and how long the team stays.
For brands planning a campaign
A partner considering this creator should know the model is outcome-tied rather than fee-for-service. Campaign copy should not describe a consulting engagement if equity or revenue share is how the work is structured.
One post addresses founders, creators, and business leaders with ideas that never launch, listing the usual constraints: daily work, budget, missing technical skills, and uncertainty about whether the idea is worth pursuing.
Read more
The offer is a prototype of the concept, validation with prospective customers, a market test in public to gauge demand, and payment only if the delivered work is seen as valuable. Example categories named include AI models that unlock revenue from data, tools that sync data across multiple CRM systems, AI assistants for solopreneurs and business leaders, engineering productivity systems, sales forecasting for manufacturing, and bank-health monitoring from public data.
Prototype, then validate with prospective customers, then test demand publicly, with payment tied to perceived value.
For brands planning a campaign
The entry offer is a low-risk prototype plus customer validation, suited to campaign angles about moving an unbuilt idea to tested demand. Brands targeting early-stage founders and small business leaders can brief around that path.
Build-in-public snapshots as the publishing format
The recurring format is a short company snapshot: a statement of position, concrete figures and a closing ask.
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Examples include eight products shipped across seven industries, with a note that the company is still early, and a two-week run of posts on taking AI from idea to production, with an invitation to follow along. Closing asks range from a question in the comments—name one weekly task and find out which of the three tools fits—to an open inbox and a link to a work page with the prompt "tell me what you would want built for your operation."
For brands planning a campaign
The format suits serialized, milestone-based storytelling built around a measurable claim and a direct call to action. Partners should expect numbers in each post, not abstract thought leadership, and plan a follow-up series rather than a single placement.
AI20 Labs is the business Siva Dosapati says they started and the only brand the posts name in a relationship.
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They describe it as an operating partner that builds and embeds AI products and workflows: it works with AI companies on model-serving and inference infrastructure, and with enterprises in healthcare, manufacturing, financial services, and energy.
They report shipped work across solar, fintech, banking, CRM, edge AI, and engineering.
Client work is described by industry and result, not company name; ask for client references if needed.
The posts do not mention a sponsorship, affiliate, client, or investment relationship with another named brand.
AI20 Labs
Owned business
Dosapati says they started AI20 Labs, an operating partner that embeds its strategy, product, and engineering team inside established businesses in exchange for equity or a share of net-new revenue, rather than a consulting fee.
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The company also builds for AI companies running inference at scale and for operators in healthcare, manufacturing, financial services, and energy.
Shipped tools include configuration and quoting software, natural-language analytics, and persistent agents.
Examples name industries and outcomes, not client companies; ask for client references if those matter to your decision.
Siva Dosapati writes about why AI projects stall between a working demo and a system a business actually runs on.
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The clearest entry point is their argument that most businesses do not need an AI agent yet: they sort work into assistants for tasks a person needs help with, workflows for processes that repeat, and agents only where the next step changes each time.
The posts then turn to before-and-after numbers — a design workflow cut from 22 inputs to 8, CRM onboarding completion from 17% to 50%, a fintech MVP in six to eight weeks — and the claim that the metric that matters is the friction that disappeared, not the model.
AI20 Labs, which they founded, is described as an operating partner that embeds with clients, works both on inference infrastructure for AI companies and on enterprise operations in healthcare, manufacturing, financial services and energy, and takes equity or a share of new revenue instead of a consulting fee.
Company snapshots — products shipped, industries covered, a two-week series on taking AI from idea to production — offer a direct way to follow the work.
For other angles, readers can look at Nikolina Belic on scoping an AI request before anything is built, Chris Hill on what AI agents change about how businesses are found and sold to, and Matt Leta on outcome-tied delivery and the missing orchestration between agents.
Leta makes the same core argument as Dosapati from a different seat: value sits in the process around the model, not the model itself.
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Leta writes that the intelligence of an AI system lives in "the embedded rules, procedures, precedents, and feedback mechanisms" and that copying a vendor template only produces the same company as everyone else — close to Dosapati's claim that results come from redesigning how work gets done, with the model as one component.
Both also challenge incumbent delivery.
Leta describes a COO who spent nearly two years paying a big-name integrator for assessments and slides, then got a working project in 12 weeks from a small AI-native pod paid on outcome.
Dosapati describes AI20 Labs investing its own team for equity or a share of net-new revenue and building what they call the other 5% of AI pilots that reach production.
Leta frames the problem as symbiosis between humans, machines and their environment, and as an organisational design question about the roles, rules and feedback loops a company must build for itself.
Dosapati frames it as a delivery and adoption problem, with named before-and-after metrics and a test for whether work needs an assistant, a workflow or an agent.
Leta targets COOs and large-company transformation buyers, and describes replacing an incumbent vendor; Dosapati also addresses founders with an unlaunched idea, offering to prototype and validate it.
Leta writes long, argument-led pieces around published research and surveys; Dosapati writes shorter company snapshots with client numbers.
Belic and Dosapati use the same first move: refuse to take a request literally and return to the question underneath it.
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In a watch-retailer example, Belic shows that "can you add AI to our CRM?" sounds like one feature, while the tool was quietly running six parts of the business — retail, service, inventory, quality control, repairs and customer operations.
Scoping the AI feature inside that label would have built the right feature in the wrong place.
Dosapati applies the same approach to agent requests, asking what a team actually does all week and sorting the answer into assistant, workflow or agent.
Both also teach through a reusable test: Belic asks a client to write one sentence covering what gets better, for whom, and what would make them decide not to build it; Dosapati offers the short rule of help/repeats/judgment and asks readers to name one weekly task to get sorted.
Belic writes for product managers and the scoping side of the work: dissecting a problem, forming a hypothesis, recording decisions, and building portfolio case studies that show judgment rather than shipped features.
Dosapati writes for business owners, founders and operators about shipping and measuring AI inside an operating company.
Belic's published examples come from a conference talk and a course, with a downloadable mini framework; Dosapati's come from AI20 Labs' client work and company metrics.
They overlap on method — decide whether to build at all before scoping — but differ in audience and examples.
Hill reaches the same conclusion as Dosapati about where AI work actually breaks.
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Hill writes that most enterprise AI problems are not model problems but translation problems, and uses a quoting example close to Dosapati's territory: pricing lives in three places, exceptions are approved in Slack, some customers have special terms, and nobody agrees which Salesforce field is correct.
The hard part is defining what "create a quote" means, not asking a model to produce one.
Dosapati's equivalent claim is that the number that matters is the friction that disappeared, and that AI creates value when someone sits in the workflow and builds for adoption.
Both argue for keeping a human in the loop and deciding what a system may touch and when a person takes over.
Hill's frame is commercial infrastructure and go-to-market: buyer agents querying businesses, machine-readable product data, pricing and trust signals as GTM assets, and the rise of translator roles such as forward-deployed engineer.
Dosapati's frame is delivery and adoption inside an operating business, with named client results and an equity-or-revenue-share model.
Hill addresses RevOps and GTM leaders on how AI changes buying and selling; Dosapati addresses founders and operators on building, shipping and keeping a system in use.
Siva Dosapati’s LinkedIn content is relevant to B2B companies considering practical AI adoption, with examples covering production systems, workflow redesign, and choosing between assistants, automation workflows, and agents.
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The account had 3,142 followers on October 7, 2026. For a campaign targeting SaaS product or AI decision-makers, ask for recent audience demographics, including followers’ roles, industries, and seniority, before assessing reach.
LinkedIn followers
3,142
LinkedIn account metadata; audience size, not audience demographics · 2026-10-07
Siva Dosapati's posts do not describe brand-sponsorship or creator-collaboration formats.
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Across seven LinkedIn posts, the only partnership-shaped activity is through AI20 Labs, the company they founded: instead of charging a consulting fee, it invests its own strategy, product design, and engineering team in established businesses and takes equity or a share of the net-new revenue the work creates.
The stated target is companies with at least $2M ARR and a clear opportunity for AI to change the economics; the company's return depends on the work producing results.
This is a commercial arrangement between AI20 Labs and the businesses it embeds with, not a sponsorship offer to brands commissioning content.
The posts do not set out sponsored-post packages, rates, deliverables, deadlines, exclusivity, or production requirements, and no policy page, media kit, or rate card was available.
Before planning a campaign, ask directly whether they offer paid collaborations and, if so, for the formats, rates, and terms.
Equity or revenue-share build partnership with AI20 Labs
Buyer requirement
The posts describe an arrangement for established businesses with $2M+ ARR and a clear opportunity for AI to change the economics.
Instead of charging a consulting fee, AI20 Labs takes equity or a share of the net-new revenue its work creates.
Its own strategy, product design, and engineering staff embed with the business rather than handing over a plan.
Buyers must agree in advance on how to define and measure net-new revenue, commit to working with an embedded team over several months, and provide access to the workflows and people the team needs.
The company's return depends on getting the outcome right.
A B2B software company selling to operations teams in healthcare, manufacturing or financial services wants to raise AI adoption inside its own product and cannot get a stalled pilot past launch.
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Decision: a strong fit on content, with the condition attached to the commercial arrangement rather than the material.
Why it fits.
This is the problem Siva Dosapati already speaks about in their own terms: work that never leaves a pilot, and products that experienced users abandon because they add steps.
The most directly transferable piece is the three-way split between an assistant, a repeating workflow and an agent.
It is written as a first-call diagnostic, recommending that teams start with the process that already exists so the hours saved are countable.
For a product team deciding what to build next, that is a usable scoping rule rather than a slogan.
The measured outcomes give the company something to benchmark against: onboarding completion on a B2B CRM moving from 17% to 50%, a solar installer design workflow cut from 22 upfront inputs to 8 with interactions down 55%, and a fintech client going from idea to a production MVP in six to eight weeks.
The stated acceptance bar for software that meets experienced operators — that it has to survive contact with people who have done the job for twenty years and will route around anything that slows them down — is the same bar the company is trying to clear, aligning the advice with the audience.
Sector range is wide enough to find reference work: eight products across seven industries including CRM, fintech, healthcare and engineering.
Condition.
Siva Dosapati's posts do not document sponsored-post packages, rates, deliverables, exclusivity terms or deadlines.
The only partnership-shaped activity described belongs to AI20 Labs, which takes equity or a share of the net-new revenue the work creates instead of a consulting fee, and looks at established businesses at $2M+ ARR where there is a clear place AI could change the economics.
Plan this as an advisory, build or outcome-tied engagement, or as co-marketing built around shipped work, not as a paid creator placement.
Agree early how net-new revenue is defined and whether the ARR threshold applies to the buyer.
When to choose a specialist
A consumer app or direct-to-consumer brand wants a paid sponsored series of posts to drive sign-ups, with a contracted number of deliverables and a reach target.
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Decision: not a fit based on what Siva Dosapati's posts say, for three reasons.
No commercial format exists to buy.
Across seven posts there is no media kit, rate card, sponsor policy or inquiry route, and nothing that sets out sponsored content packages, deliverables or pricing.
The posts describe AI20 Labs investing its own team into client businesses in exchange for equity or a share of new revenue.
Ask directly whether sponsorship is available and what terms apply before planning a placement.
The audience information does not support a reach target.
The LinkedIn account had 3,142 followers as of October 7, 2026, but no demographics, role or seniority breakdown, geographic split or engagement data are published.
Ask for audience and engagement data before setting a defensible reach or penetration target.
The published content points the other way.
The posts are dense, technical and company-focused: latency budgets measured in milliseconds, cost per token deciding whether a product has a business, 1.9 million inference requests with 876,400 device registrations.
The recurring format is a build-in-public snapshot that states a position, gives figures and closes with a link, an inbox invitation or a question to the comments.
That format works for operators evaluating AI systems; it is a weak vehicle for consumer awareness or install driving.
A worked campaign example
Campaign angle for the B2B software company's AI-adoption campaign: “the workflow you already run.”
Start from the delivery format visible in Siva Dosapati's posts rather than inventing one.
Each post states a position, gives concrete before-and-after numbers, and closes with an ask.
Build three posts on that shape.
Post one — position.
Lead with the diagnostic, not the feature: if the work needs help, use an assistant; if it repeats, use a workflow; if it needs judgment, look at agents, and start with the repeating process because it already exists and its saving is countable.
For the product team, this framing lets them talk about a job being replaced rather than an agent label, addressing the same pushback buyers give prospects.
Post two — before and after.
Take one repeating workflow inside the product, publish the baseline (steps removed, interaction count, completion rate) and the result after redesign, and frame the number around the friction that disappeared rather than the model.
The 17% to 50% onboarding move and the 22-inputs-to-8 redesign are the benchmarks the audience will hold the numbers against.
Post three — the adoption test.
Run the redesigned workflow past five people who have done the job for years, record where they route around it, and publish what changed.
This is the acceptance bar Siva Dosapati states for enterprise software, and it is a concrete, inspectable exercise rather than a claim.
Close the series with an invitation to bring one weekly process and get told which of the three it is, mirroring the comment prompt in the posts, and route the replies into a scoped friction audit: name the repeating process, baseline the step count and completion rate, and agree what the system may touch and when a person takes over. That gives the buyer a low-commitment next step and gives any follow-on work a measurable start.
Before commissioning, settle two things the posts do not answer: whether this is paid content or an outcome-tied build, given that the described model is equity or revenue share rather than a fee, and which client work can be named publicly, because the posts report figures across industries but do not name clients. Ask for substantiation of the reported figures; the posts state no third-party verification.
Choose Matt Leta when the stalled pilot is really a stalled transformation programme and the question is who should run and be paid for the work.
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The posts describe the failure from the partner side: a global manufacturer kept an integrator of more than a decade on the account while two years produced assessments and slides and the problem kept costing money every week.
A replacement delivered a working project within twelve weeks, with the client's own finance team setting the value baseline the work was paid against.
They also offer a governance test a product team can apply before spending more: take the most important decision the team makes each week and ask whether it would still come out the same if the person who makes it left tomorrow, or the model behind it were swapped for a newer one.
That is the same question as whether an adopted feature survives its champion.
Another post argues that intelligence will arrive as many models and many people with assigned roles, rather than one superintelligence.
It cites 66% of companies running agents in production, while only 7% have proper orchestration between them and roughly one in four agents run unwatched.
That framing helps when the need is to coordinate several AI features rather than ship one.
The trade-off is product-level scoping detail.
These posts focus on choosing and paying a transformation partner, not on deciding what to build inside a product.
They don't offer the assistant, workflow and agent split in Siva Dosapati's material, which directs product teams to start with a repeating process whose saved hours can be counted, or before-and-after product metrics such as onboarding completion rates and reduced step counts.
If the buyer needs a build decision rather than a partner decision, ask for a feature-level rule; this material offers an operating-model argument instead.
Choose Nikolina Belic when the pilot stalled before it was scoped: the request arrived as a feature, and nobody wrote down why it deserved the money.
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The watch-retailer post comes closest to this pilot's failure mode: a request to add AI to the CRM, which quietly ran six parts of the business — retail, service, inventory, quality control, repairs and customer operations.
Taking the request literally would have built the right feature in the wrong place.
The method is to answer three questions before scoping anything: what gets better for the business and the customer; whether that justifies this solution, a different one or no new build; and whether people are willing to change how they work.
The practical test is a single sentence — what gets better, for whom, and what would make you decide not to build it — a cheap way for a product team to sharpen or kill a stalled initiative before another sprint.
Her second post applies the same judgement-over-outputs standard to product hiring, arguing that portfolios should record decisions rather than catalogue shipped screens.
That matters if the need concerns how the team is staffed.
The trade-off is proof of delivery.
The posts offer a framing exercise and a talk, but no shipped production figures or operator-acceptance bar comparable to the one Siva Dosapati states for enterprise software: a system has to survive contact with people who have done the job for twenty years.
They also say nothing about engagement structure, including whether work would be paid for against outcomes.
If the buyer already knows what to build and needs evidence it will be adopted and delivered, ask for shipped results and the proposed payment structure.
Choose Chris Hill when the adoption blocker sits between the product and the buying process, rather than inside the product.
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His case is that the next buyer may never visit the website: a procurement agent given requirements for Salesforce integration, SSO, security standards and a sub-$150,000 annual price will research, compare and shortlist before a human talks to Sales.
It will drop vendors whose pricing is impossible to understand, whose integrations are undocumented and whose security detail is buried in PDFs.
That creates a concrete agenda — structured product data, accurate pricing, APIs, documentation and machine-readable rules — to make a product legible in a form software can evaluate.
The second post explains why so many enterprise AI projects stall at the definition stage.
Its quoting example has pricing in three places, exceptions approved in Slack, some customers with special terms, Sales maintaining a spreadsheet Finance will not accept, and nobody agreeing which Salesforce field is correct.
If the pilot failed because the organisation never agreed what the feature was supposed to mean, this material addresses that problem.
The trade-off is delivered proof.
The posts argue about where buying is heading and classify roles — forward-deployed engineer, AI workflow engineer, GTM engineer, AI product engineer — rather than report work with measured results.
They offer no before-and-after product numbers to assess a build against and state no delivery arrangement.
The agentic-buying claims are predictions the buyer would be taking on trust; Siva Dosapati's material, by contrast, reports figures from shipped work and states an acceptance bar for software that meets experienced operators.
If delivery evidence or a defined engagement matters, ask for both.
Dosapati's seven full LinkedIn posts describe AI20 Labs' work and its delivery model: shipped products across solar, fintech, banking, CRM, edge AI, healthcare and engineering; a split of AI tasks into assistants, workflows and agents; and an engagement model that trades capacity for equity or a share of net-new revenue instead of a consulting fee.
The posts have no publication dates and do not cover the account's full history.
Reported client results — such as a design workflow cut from 22 inputs to 8 and CRM onboarding completion rising from 17% to 50% — are the creator's own figures and were not independently verified.
Client names, contracts, engagement data and audience demographics are not available.
Dosapati's posts do not include sponsored-post packages, rates or a media kit; they also do not say that sponsorships are refused, so readers cannot determine that policy.
