AI for Business: Three Ways to Buy It
An honest map of the market, including when we are not the right choice.
This page is about buying AI capability for a business decision, not a .ai domain name or an off-the-shelf app subscription, those are different questions with different answers. There are three sensible ways for a company to buy that capability, and each one is right for somebody. Most bad purchases happen when a company buys from the wrong column, not from a bad vendor. Here is the map as we see it, with our own position marked honestly.
Path 1: DIY tools and no-code platforms
Off-the-shelf SaaS, chatbot builders, workflow automation tools, and the AI features already inside software you own. Fast to start, cheap to try, and genuinely good for standalone tasks: a website chatbot, meeting summaries, marketing drafts, simple automations between apps.
Where it strains: when the task has to read from and write to your core systems, when your data needs real engineering before any tool can use it, and when the answers have to be right enough to commit money to (purchasing, inventory, pricing). At that point you are no longer configuring a tool; you are doing custom work through a keyhole.
Buy here when: the problem is self-contained, the cost of a wrong answer is low, and someone in-house will own the tool.
Path 2: Enterprise platforms and large integrators
Large consultancies and platform vendors running organization-wide programs: hundreds of users, change management, procurement frameworks, multi-year roadmaps. If you are rolling out AI across thousands of employees, or you need a program with governance boards and a PMO, this is the right column, and no boutique should pretend otherwise.
Where it strains: minimum engagement sizes, platform dependence, and scoping built for organizations rather than for one sharp operational problem. A mid-market manufacturer that needs demand forecasting wired into its ERP is usually too small a story for this column, and pays for structure it does not need.
Buy here when: the initiative is organization-wide, budgets are in the millions, and you need program infrastructure as much as technology.
Path 3: A specialist boutique
A small senior team, or a single senior engineer, that takes one operational problem end to end: characterization, feasibility on your real data, and implementation into the systems you already run, with you owning the result. This is where we sit: a boutique data science practice in Israel. The trade is depth for breadth: a boutique will not staff your program or resell you a platform, and its capacity is finite by design.
Where it strains: scale. A boutique cannot run a 100-person rollout, should not pretend to, and the good ones will tell you so in the first meeting.
Buy here when: the problem is specific and operational, it touches your core systems and your real data, the answer has to survive production, and you want to own what gets built.
The map in one table
| Criterion | DIY tools and no-code | Enterprise platform or integrator | Specialist boutique |
|---|---|---|---|
| Best for | Self-contained tasks | Organization-wide programs | One operational problem, end to end |
| Typical starting point | Subscription | Program budget, usually seven figures | Scoped project or hourly advisory |
| Who does the work | You | A delivery organization | The senior person you met |
| Integration with core systems | Limited, connector-based | Deep, at program scale | Deep, for the problem at hand |
| Who owns the result | The vendor’s platform | Varies by contract | You: models, code, pipelines |
| Wrong fit | Decisions that commit real money | One sharp mid-size problem | Large-scale rollouts, staff augmentation |
When not to hire us
We would rather tell you now than in a kickoff meeting:
- You need a website chatbot or a simple automation. Buy a SaaS tool. Paying boutique rates for this is a waste of your money, and we will say so.
- You need an organization-wide transformation program with a seven-figure budget. Go to an enterprise integrator. That is genuinely their game.
- You need ten developers for eight months. That is staff augmentation, and we are not that.
- Your data is not ready and you want to skip that part. We will not build on sand, and a characterization that concludes “not yet” is a legitimate outcome here.
If you are unsure which column you are in, that question alone is a fine reason for an hourly conversation. Sometimes the most valuable thing we can tell you is which of the other two paths to take, or whether your data is ready for any of them yet.
Is this page about buying a .ai domain name, or an AI app?
No. This page is about buying AI capability for a specific business decision, a forecasting system, an inventory tool, a knowledge agent, not a web address or a subscription. If you want a .ai domain, your registrar sells that. If you want a ready-made app, its own website or app store is the better fit, and Path 1 below covers exactly that case.
How do I know which of the three paths is right for my company?
By the shape of the problem more than the size of the company. A self-contained task with a low cost of error fits DIY tools. An organization-wide, multi-year program fits an enterprise platform or integrator. One operational problem that touches your core systems and your real data, where you want to own the result, fits a specialist boutique. The table below breaks the same three down by starting point, integration depth, and who ends up owning the outcome.
Is a specialist boutique cheaper than an enterprise integrator?
Not necessarily by the hour, but the entry point is smaller. Scoping is a bounded, fixed-price work package sized for one operational problem, not a program budget, and it ends with a written scope and a binding quote for the next stage before you commit further. An enterprise integrator usually carries program infrastructure, governance boards, a PMO, whether or not your specific problem needs it.
We're a mid-market Israeli company with one specific operational problem, which path fits?
Usually the third: the problem is specific and operational, it touches your core systems and real data, the answer has to survive production, and you want to own what gets built. If your scope is closer to an organization-wide rollout across thousands of employees, the honest answer is the second path, and we will say so rather than stretch to fit.