The Four Levels of AI Benefit in Sales: The GSP TSAR Framework
Last updated August 24, 2026
In This Guide
TSAR stands for Table Stakes, Solutions, Automation, and Reimagine, and it's a GSP framework for the four escalating levels of AI benefit available to a sales organization.
Each level demands more ambition, organizational effort, and cross-functional commitment than the last — and each offers a larger, more durable competitive advantage in return. Knowing which level you are operating at, and which you are aiming for, is the starting point for any AI-in-sales initiative.
Across our work with sales organizations, one pattern keeps repeating:
Companies that struggle with AI in sales aren't struggling because the technology doesn't work, but because they don't have a clear picture of the benefit they're trying to achieve — and what it takes to get there.
TSAR Levels Increase Less About Technology More People.
The Four Levels of AI Benefit at a Glance
There are four benefit levels in the TSAR framework:
The table below summarizes what each level involves, who drives it, and what it delivers.
| Who drives it | ||||
|---|---|---|---|---|
| Table Stakes | Off-the-shelf LLMs (ChatGPT, Claude, Teams or Zoom note-takers) used for isolated, everyday tasks | Individual salespeople | Low | None — the tools are universal |
| Solutions | AI capabilities built into the sales platform: account summaries, next best action, AI role-play coaching | The organization (a focused project) | Moderate | Real, but with a shelf life |
| Automation | AI agents executing at a scale humans can't sustain — nightly account scans, automatic opportunity generation | Cross-functional teams | High | Meaningful and durable |
| Reimagine | An end-to-end process rebuilt around what AI now makes possible (usage-based pricing, predictive dispatch, AI-built into product features) | Business transformation (cross-functional sponsor) | Highest | Sustainable — from implementation quality, not the tech |
Level 1: Table Stakes — Individual AI Use
Table Stakes is the entry point of the TSAR framework. This is where individual salespeople pick up off-the-shelf AI tools — ChatGPT, Claude, or the note-taker built into Teams or Zoom — and use them for everyday tasks.
Let's be blunt: there's no competitive advantage at this level. These tools are everywhere; they're cheap or free, and the barrier to picking them up is almost zero. Your competitors already have access to them. The bigger risk is that your own salespeople might not be using them as well as they might.
The usual suspects are familiar by now.
Asking ChatGPT or Claude to draft a prospecting email. Letting Teams or Zoom auto-generate meeting notes and actions. Using an LLM to get up to speed on a prospect's market position before a first call. None of this is sophisticated, but all of it saves time — and time, in sales, is the one thing there's never enough of.
Salesforce's State of Sales survey(opens in new tab) polled 4,050 sales professionals in August and September 2025 and found that 87% of sales organizations were already using some form of AI for activities such as prospecting, forecasting, and drafting emails. The real number today is almost certainly higher.
What makes this level distinctive is how it emerged.
Salespeople didn't wait for a strategy or a policy — they just started using the tools, often well before their employer had formed any view on the matter. As of 2026, at many companies, that's still true: AI adoption running ahead of AI governance.
Measuring the value is difficult. These tools sit so far upstream from a closed deal or a cost saving that attribution is nearly impossible. And yet experienced salespeople often say they'd be reluctant to give them up — which is its own kind of evidence.
There is one lever organizations can pull to get more from something they're probably already paying nothing for: training people to prompt better. Not more — better. A well-constructed prompt saves time and changes the quality of what comes back.
[VISUAL PLACEHOLDER] IMAGE – SALESPERSON ENTERING A PROMPT
A Harvard Business Review piece(opens in new tab) (February 2025) on how sales teams can use generative AI to understand client needs illustrates this well. Rather than a generic "tell me about this company" search, salespeople are coached to ask: "Review the following three earnings call transcripts for [Company]. Identify questions from analysts that seem to recur from one quarter to another. Highlight these questions and indicate how senior leaders responded."
That's a different kind of output entirely — one that surfaces the questions challenging the C-suite and is handed to a salesperson before they've even picked up the phone.
At the Table Stakes level, the ceiling is low. But a modest investment in prompt training is one of the few ways an organization can extract a little more value from tools that everyone else uses with default settings.
Level 2: Solutions — Organization-Built Capabilities
Solutions is the level at which the organization, not the individual, implements AI capabilities, requiring technical skills and a focused project.
Many of these capabilities live inside the sales automation platform the business is already running.
The most common examples are more powerful than they first appear:
- Account summaries that pull together service tickets, payment history, product holdings, and recent contact activity give a rep context before a customer conversation. (In the Salesforce survey cited above(opens in new tab), 89% of sellers using AI said it deepens their understanding of customers.)
- Next-best action recommendations combine the logic of the sales playbook with patterns from historical deals to give reps real-time guidance on where to focus and what to do next.
- AI-powered role-play coaching — where reps rehearse presentations, work through likely objections, or practice closing — offers an on-demand training environment (which organizations have rarely been able to deliver consistently).
Attribution at the Solutions benefit level remains elusive. There are too many variables between a rep receiving an AI recommendation and a deal closing to draw a clean line.
What you can do is listen to the people using the tools. In our experience, salespeople are quick to tell you whether a solution is genuinely helping or adding to their admin load. That feedback is data worth taking seriously.
Adoption at this level is harder than implementation because it requires a change in habit and earning trust in the output.
You aid adoption by making the tools easy to access within the tools they already use and building feedback loops that improve quality over time. If reps see the system getting better based on their input, they engage with it. If they don't, they stop.
Solutions-level benefits give you a competitive edge — for now. But that edge has a shelf life. These capabilities will eventually become standard, just as CRM itself did. So, the window to extract differentiated value won't stay open forever.
The organizations that get the most from this level follow what a 2026 McKinsey article calls a flywheel of amplified expertise(opens in new tab): rather than configuring a next-best-action solution generically, anchor it in the knowledge of your best people. Their instincts, objection handling, and deal judgment are built into the model and scaled across every rep on the team, rather than relying on occasional coaching sessions.
That's the real promise at the Solutions level: capabilities that would have required significant effort, budget, or expertise to deliver consistently — available to every rep, on every deal, every day.
Level 3: Automation — AI at Scale
Automation-level AI executes sales process steps autonomously, at a scale, speed, and consistency no human team could sustain.
The first two levels of the TSAR framework are about making salespeople more effective. Automation is different. Here, AI isn't augmenting what a salesperson does. It's doing things a salesperson could do, but never would.
Consider how one of our clients — a manufacturing company based in Atlanta — put this into practice. Every night, multiple AI agents analyze data points across the entire customer base: product holdings, warranty status, hardware assets, software subscriptions, service tickets.
When an agent finds something worth acting on, it acts.
A customer service ticket raised against an asset not covered by a service contract? It creates an opportunity and assigns it to the right salesperson. A warranty agreement lapsed beyond the grace period without renewal? Another opportunity, automatically generated.
This prevents the system from generating a warranty renewal opportunity for a customer with a history of late payments or an unresolved dispute.
Automation also delivers a genuine, attributable feedback loop that you don't find at lower benefit levels. You can track whether an agent-generated opportunity was won or lost and use that signal to refine how agents behave.
The competitive advantage at this level is meaningful and durable. Automation is built on unique proprietary data, so competitors can't replicate it by buying the same software.
Level 4: Reimagine — Rethinking the Process
Reimagine is the top level of the TSAR framework: rebuilding an end-to-end sales or service process from the ground up around what AI now makes possible.
At the Automation level, you're identifying specific steps where AI can do what humans do — but faster and at greater scale. Reimagine asks a different question: if we were to build the process from scratch, what would we build instead?
Automation gave our manufacturing client nightly scans of the customer base, surfacing opportunities that would otherwise have gone unnoticed.
But take the thinking further.
What if every piece of their hardware ships with embedded AI feeding back real-time usage data? A few things become possible:
- Predictive replacement: The system cross-references that data against patterns from a wider customer base, spots when a specific internal component is approaching failure, and automatically dispatches a replacement before it breaks.
- Dynamic support pricing: Support pricing adjusts to reflect usage patterns instead of sitting at a flat rate.
- Usage-based contracts: With granular consumption data for every customer, the company can replace fixed-fee contracts with pricing that tracks actual usage.
The same logic reaches into service and product development:
- Automatic issue resolution and troubleshooting: When a customer makes the same software mistake twice, the system notices and sends a targeted video immediately, without a ticket and without a human in the loop.
- Build-on-request features: The customer describes the feature they want, by text or voice. A tool automatically mocks up the interface. The customer tweaks it back and forth until it's right. Then an AI agent drafts the code and comes back with a price. Once a human signs off, it ships — either into the general release, or as a one-off upgrade just for that customer.
Each of those scenarios is a different sales and support model entirely, not a better version of the existing one.
And unlike the earlier levels, Reimagine is inherently cross-functional. You're rethinking a process that runs across sales, operations, finance, and customer service, not optimizing a single sales task. That requires a different kind of project and a different kind of sponsor.
The competitive advantage at Reimagine level is tangible and sustainable, but depends on the quality of implementation: how well the solution is deployed, how thoughtfully the change is managed, and how effectively the organization brings its people on board.
How the Four Levels Relate to Each Other
The TSAR levels aren't a ladder you have to climb rung by rung — but there's a logic to the sequence.
Working through Table Stakes and Solutions builds the organizational AI literacy, trust, and data foundations that Automation depends on.
When you reimagine an end-to-end process, trust, adoption, and implementation problems are bound to arise. If you skip the previous levels, you won't have the necessary experience to address them.
Where to Start with the TSAR Framework
Think of the TSAR framework less as a checklist to complete, and more as a way to be honest about where your AI ambition sits today and what the next level would ask of you.
Table Stakes and Solutions make individual salespeople and teams more effective. Automation and Reimagine change what the sales organization is capable of in the first place.
The organizations pulling ahead are the ones that know which TSAR level they're at, deliberately pick the next one, and build the literacy, trust, and data foundations to get there.
If you need help assessing your current AI adoption level, identifying which TSAR level to target next, and planning for how to get there, contact GSP and book your consultation.(opens in new tab)
TSAR Framework FAQs
Pick a benefit level first. That sounds obvious, but most projects skip it and go straight to buying a tool. The TSAR framework by GSP splits benefit into four levels: Table Stakes, Solutions, Automation, and Reimagine.
Whichever one you're aiming for changes how you scope the project, what technology you need, which processes you touch first, and how you talk to the people affected.
Once you've settled on a level, our 10 Best Practices for AI in Sales Projects covers what happens next.
- AI in Sales: A Complete Guide for Sales Leaders — The full pillar guide
- Why AI in Sales Goes Wrong — Failure modes at each TSAR level
- Best Practices for Implementing AI in Sales — Ten practical recommendations
- The GSP ARMS RACE Framework — How to build AI in sales the right way