# Siva Dosapati

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.

- Profile: https://b2binfluencer.marketing/creators/siva-dosapati/
- LinkedIn: https://www.linkedin.com/in/siva-dosapati
- LinkedIn followers: 3,142
- Topics: artificial-intelligence, agentic-ai, ai-adoption
- Based on 16 dated posts, latest 2026-10-09

## Summary

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.
- Check first: Confirm whether sponsorship is available and what terms apply before planning a placement.

### 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%.

## Main ideas

- **Most businesses do not need an AI agent yet**: Dosapati argues that asking for an AI agent is usually the wrong starting point. 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.
- **Measure AI by the friction that disappears, not by the model**: Dosapati's recurring examples show workflow before-and-after results, not model benchmarks. 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.
- **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. 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.
- **Two audiences, two definitions of hard**: AI20 Labs works at both ends of the market, with a different failure mode at each, Dosapati says. 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.
- **Invest capacity instead of charging a consulting fee**: Dosapati describes AI20 Labs as investing its own team—AI strategy, product design and engineers—in established businesses, with no consulting fee. The return is equity or a share of the net-new revenue the work creates. They target businesses with $2M+ ARR where AI could clearly change the economics. Their reasoning: the best work they have been part of came from being inside a business long enough to find the constraint nobody had named yet. That takes months and a team willing to be measured on what happens afterward; a fee-based engagement rarely buys either. Before a deal, ask whether the return is equity or revenue share, how net-new revenue is defined and how long the team stays.
- **Give founders a low-risk path from idea to tested demand**: One post addresses founders, creators and business leaders with ideas that never launch. 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.

## Work to read

- [Most businesses don't need an AI agent yet: the assistant, workflow and agent split](https://www.linkedin.com/posts/siva-dosapati_three-types-of-ai-automation-activity-7506087425593610240-aRSB)
- [Measured outcomes: onboarding from 17% to 50%, and 1.9M inference requests](https://www.linkedin.com/posts/siva-dosapati_most-ai-conversations-i-have-start-in-the-activity-7498046983878623232-URMs)
- [Both ends of the market: inference infrastructure and enterprise operations](https://www.linkedin.com/posts/siva-dosapati_when-we-say-we-are-investing-our-capacity-activity-7493350969213177856-N3n-)
- [Portfolio snapshot: eight products across seven industries](https://www.linkedin.com/posts/siva-dosapati_buildinpublic-aitransformation-agenticai-activity-7485194184711159808--JIj)
- [Investing capacity instead of charging a consulting fee](https://www.linkedin.com/posts/siva-dosapati_we-opened-something-today-that-i-have-wanted-activity-7492686321501487104-Ll8w)
- [Why AI20 Labs exists: the 95% pilot failure figure and the shipping gap](https://www.linkedin.com/posts/siva-dosapati_aitransformation-agenticai-buildinpublic-activity-7480251928778547201-h8Qc)

## Brand relationships

- **AI20 Labs** (owned_business)

## Partnerships

Siva Dosapati's posts do not describe brand-sponsorship or creator-collaboration formats. 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.

## Alternatives

- Matt Leta: 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. 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.
- Nikolina Belic: 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. 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.
- Chris Hill: Choose Chris Hill when the adoption blocker sits between the product and the buying process, rather than inside the product. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/