Introduction: Every Salesforce Team Is Asking the Same Question

Your org wants AI inside Salesforce.

Leaders are asking for:

• Faster case resolution

• Smarter routing and triage

• Auto-generated emails and summaries

• Better visibility across customer interactions

And at some point, someone says:

“We could just build this ourselves on top of Salesforce with an LLM and some Apex, right?”

That’s the crossroads:
Do you adopt a Salesforce AI managed package (like ConvoPro), or build a custom AI solution from scratch?

This article breaks down costs, tradeoffs, and timelines so you can make a grounded decision, not one based on hype or wishful thinking.

What Do We Mean by a “Salesforce AI Managed Package”?

A Salesforce AI managed package is an app installed from the Salesforce ecosystem (e.g., AppExchange-style package) that:

• Runs natively in your Salesforce org

• Provides prebuilt AI capabilities (workflows, components, UI, configuration)

• Handles much of the plumbing: auth, data access, prompts, error handling, logging, etc.

• Is maintained and updated by a vendor, not your internal dev team

Instead of wiring up APIs, LLM prompts, Apex controllers, and Flow actions yourself, you configure and extend a managed package that already knows how to “speak Salesforce.”

Custom Development: What “Building It Yourself” Really Entails

“Let’s just build it internally” often sounds like:

• “We’ll call an LLM API from Apex.”

• “We’ll add some Flows and we’re done.”

In reality, a production-ready AI solution inside Salesforce usually requires:

• Architecture & integration

  • Secure LLM connectivity (via middleware or direct APIs)

  • Data access patterns that respect sharing rules and object security

  • Handling rate limits, retries, and failures

  
  

• Prompt & workflow design

  • Prompts tuned for your data, objects, and use cases

  • Guardrails for hallucinations, PII leakage, and unsafe outputs

  
  

• User experience

  • Screens, Lightning components, or console utilities

  • Intuitive admin controls for configuration

  
  

• Governance & observability

  • Logging and audit trails for AI calls

  • Monitoring quality, cost, and usage

  • Ability to roll back quickly if something misbehaves

  
  

• Ongoing maintenance

  • Updating prompts and workflows as your org changes

  • Adapting to new LLM models and pricing

  • Bug fixes and performance tuning

All of that is doable. The question is: should you be the one building and maintaining it?

Build vs. Buy at a Glance

Here’s a simple side-by-side comparison

Both paths can be valid. The question is which constraints matter more to your organization right now: speed and risk, or absolute control?

Cost Breakdown: Where Organizations Underestimate “Build”

Most teams compare license fees vs. no license fees and conclude custom development looks cheaper. That’s almost never the full picture.

1. Direct vs. Indirect Costs

Direct costs of custom build:

• Solution architect time

• Salesforce developer time (Apex, Flows, LWC)

• DevOps / platform engineering

• QA and UAT cycles

Indirect costs (often ignored):

• Context switching for already overloaded teams

• Opportunity cost: features you don’t ship because devs are busy building AI plumbing

• Support overhead when something breaks

• Knowledge loss when key engineers leave

With a managed package, more of that cost is externalized and predictable.

2. The “Hidden” Maintenance Tax

AI systems are not “set and forget.” You’ll likely need to:

• Update prompts as you learn more about failure patterns

• Adapt to changes in LLM APIs and pricing

• Extend to new objects or processes (e.g., adding opportunities after starting with cases)

• Patch issues when Salesforce releases introduce breaking changes

A managed package vendor is doing this for many customers at once — you benefit from that scale. With a custom build, you are the vendor.

Timelines: How Long Does Each Path Really Take?

Managed Package Timeline (Typical)

• Week 1–2: Install, connect to your AI provider(s), configure core use cases

• Week 3–4: Pilot with a small group of users, collect feedback

• Week 5–8: Iterate and expand to more teams / processes

Even with stakeholder approvals and security reviews, many orgs see value in 1–2 months, often faster.

Custom Build Timeline (Typical)

• Month 1: Requirements, architecture, vendor selection (LLM, middleware, etc.)

• Month 2–3: Build core flows, prompts, components, integration logic

• Month 4: QA, security reviews, user testing, refinements

• Month 5+: Production rollout, followed by inevitable fixes & enhancements

In practice, it’s rare to see production-grade AI features in less than 3–6 months from scratch, especially in larger enterprises.

Tradeoffs: Control, Flexibility, and Risk

When Custom Development Shines

Custom development might be appealing if:

• You have very unique workflows that don’t resemble anything off-the-shelf

• You already maintain a dedicated internal AI platform used across many systems, and Salesforce is “just another client”

• Your org has strict constraints that require deep, proprietary logic baked into the AI layer

In those cases, building a tailored solution can create strategic advantage — as long as you’re prepared to own it fully.

When a Managed Package Wins

A Salesforce AI managed package is usually a better fit if:

• You want faster time-to-value and lower implementation risk

• You’d rather focus on configuring AI workflows than building infrastructure

• You need governance features (logging, approvals, guardrails) out of the box

• Your Salesforce team is stretched thin and can’t absorb a new multi-month project

Think of it like this:

• Custom build is laying your own railroad tracks.

• Managed package is buying a high-speed train ticket on an existing line and deciding which stations to stop at.

Governance, Security, and Compliance: Not Optional

For many enterprises, this is the make-or-break category.

With custom development, you must define and implement:

• Standards for prompt design and testing

• Logging and traceability for AI outputs

• Approvals and review workflows for critical actions

• Data residency and retention controls

With a managed package, much of this is:

• Exposed as configuration (turn features on/off, define conditions)

• Provided as built-in logs, dashboards, or objects

• Supported by documentation and vendor best practices

You still own the final risk posture, but you’re not starting with a blank page.

A Practical Way to Decide: Key Questions to Ask

Here are questions you can use internally to guide the decision:

1. What’s our true deadline for seeing AI value in Salesforce?

   • If the answer is “this quarter,” custom is risky.

     
     

2. Do we have Salesforce dev capacity for a 3–6 month AI project?

   • If not, are you willing to pause other roadmap items?

     
     

3. Who will maintain the AI solution 12–24 months from now?

   • Is there a clear owner beyond the initial champions?

     
     

4. How critical are custom, proprietary workflows vs. proven patterns?

   • If most of your needs are common (case summaries, triage, email drafts, knowledge lookup), a managed package likely covers 80–90% with configuration.

     
     

5. How important is vendor support and roadmap?

   • Are you comfortable being your own AI product team?

If you’re unsure how to answer most of these, start with a managed package. You can always add targeted customizations on top.

Where ConvoPro Fits In (Without the Hype)

ConvoPro is built for organizations that want to:

• Bring AI into Salesforce quickly

• Avoid owning all the low-level plumbing themselves

• Keep options open as models and AI platforms evolve

Instead of building everything in-house, you can:

• Install and configure AI-powered workflows directly in Salesforce

• Leverage patterns that have already been tested across multiple orgs

• Iterate faster with admins and business users in the loop, not just developers

You’re not giving up control, you’re choosing to stand on a platform that’s designed for AI inside Salesforce.

FAQs

1. Is it cheaper to build AI in Salesforce than to buy a managed package?

It might look cheaper on paper if you only compare license cost vs. $0, but once you factor in developer time, architecture, QA, security reviews, and ongoing maintenance, custom builds are often more expensive over the full lifecycle. Managed packages convert a lot of that into a predictable operational cost.

2. What exactly is a Salesforce AI managed package?

It’s a prebuilt application that lives inside your Salesforce org and provides AI capabilities (like summarization, routing, content generation, etc.) through configuration rather than writing large amounts of custom code. It’s installed, updated, and maintained like other Salesforce apps, but focused on AI workflows.

3. How long does AI deployment in Salesforce take?

• With a managed package, many orgs see their first live AI use cases in weeks, not months.

• With custom development, realistic timelines to a stable production rollout are often 3–6 months, especially in larger or regulated environments.

4. Can we start with a managed package and still add custom code later?

Absolutely. Many teams use a hybrid approach:

• Start with a managed package for core workflows and governance

• Add targeted customizations via Flows, Apex, or Lightning components where they need special behavior

This approach maximizes speed while preserving flexibility.

Conclusion: Don’t Let “We’ll Just Build It” Delay Your AI Strategy

AI inside Salesforce is no longer a nice-to-have it’s quickly becoming table stakes.

The real question isn’t whether you should use AI, but how quickly and safely you can get it into the hands of your users.

For most organizations, a Salesforce AI managed package offers:

• Faster time-to-value

• Lower risk and maintenance burden

• Better support for governance and security

• A smoother path to iterate and expand over time

If you’d like to see what this looks like in practice, explore how ConvoPro can bring AI into your Salesforce org without a year-long engineering project and with the flexibility to grow as your use cases evolve.