GXA® AI Foundation Series · Part 2 of 3
Which AI Should Your Business Actually Buy?
Part 2 of the GXA AI Foundation Series. Jason Knight and GXA CIO Cory Buls walk through the model landscape, what licensing tiers actually change, how AI cost behaves, and the governance to have in place before the next purchase.
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What Part 2 Covers
Most companies do not overspend on AI in one big decision — they creep into it through personal subscriptions on credit cards, overlapping tools doing the same job, and no one owning the number. Part 2 of the GXA AI Foundation Series gives business leaders a way to choose on purpose: the five model names you keep hearing (Claude, ChatGPT, Copilot, Gemini, Grok) and the public-versus-private split that decides whether your data trains someone else’s model; why the same product name comes with very different contracts; how to match the licensing tier to the task instead of paying frontier prices for light work; the three reasons AI spend gets out of control and who should own the budget line; and a four-question funnel — value, cost, business impact, done — for evaluating any AI request.
It is the split that decides whether your data trains someone else’s model. The session covers where each of the five major models sits and what that means for a business handling client or regulated data.
The same product name comes with very different contracts. Match the tier to the task — not every job needs the expensive frontier model — and recognise where the 20-to-250-employee sweet spot sits.
Three reasons, covered in the session: personal subscriptions on credit cards, overlapping tools doing the same job, and no single owner of the AI budget line.
A four-question funnel: what is the value, what is the cost, what is the business impact, and what does done look like. Then five readiness questions to score yourself against before you buy.
Jump to a Chapter
- 0:00 Welcome and what this session covers
- 2:20 Format, Q&A, and the complimentary discovery call
- 5:20 The model landscape: Claude, ChatGPT, Copilot, Gemini, Grok
- 6:50 Licensing and access: same product name, very different contracts
- 9:10 Choosing a tier: not every task needs the expensive model
- 12:50 Why AI spend gets out of control
- 16:50 Evaluating AI opportunities: one funnel, four questions
- 20:20 Budgeting and governance: AI as a managed investment
- 23:30 AI readiness: five questions, score yourself honestly
- 27:00 Q&A
Session Recap
Part 2, in Writing
The model landscape
The session starts with the five model names business leaders keep hearing — Claude, ChatGPT, Copilot, Gemini, and Grok — and the distinction that actually matters underneath them: the public versus private split, which decides whether your data trains someone else’s model.
Licensing and access: same product name, very different contracts
A consumer subscription, a team plan, and an enterprise agreement can carry the same product name and completely different terms on data handling, administration, and control. Part 2 walks through where the 20-to-250-employee sweet spot sits and what changes as you move up the tiers.
Choosing a tier: not every task needs the expensive model
Matching tier to task is the single easiest place to stop overpaying. Light, repetitive work does not need frontier pricing; the session covers how to decide which work does.
Why AI spend gets out of control
Companies rarely overspend on AI in one big decision. They creep into it. The session names three reasons it happens, and who needs to own the budget line so it stops.
- ✓ Personal subscriptions expensed on individual credit cards
- ✓ Overlapping tools quietly doing the same job
- ✓ No single owner accountable for the AI number
Evaluating AI opportunities: one funnel, four questions
Every AI request — from a department head, a vendor, or the board — goes through the same four questions: value, cost, business impact, and what “done” looks like. It turns an endless stream of AI pitches into a queue you can rank.
Budgeting, governance, and readiness
The session closes by treating AI as a managed investment rather than a series of one-off purchases, then gives five readiness questions to score yourself against honestly before committing the budget.
In This Session
Your Presenters
Jason Knight
AI Advisor to CEOs
Three decades building, securing, and scaling tech businesses — including a national IT firm he grew to $50M before selling. Now helps CEOs adopt AI the right way: fast where it creates value, controlled where it matters.
Cory Buls
Chief Information Officer, GXA
A technology leader with 15+ years in IT leadership, managed services, and digital transformation. As GXA’s lead vCIO he aligns IT with business goals across roadmapping, cloud adoption, cybersecurity, AI, and governance.
Your Next Step
Book a Complimentary AI Readiness Call
A 30-minute, high-level review of your licenses, spend, and data posture with GXA. You leave with a one-page AI exposure report covering your specific risks, opportunities, and the practical next step.
Prefer to talk now? Call (972) 630-3323
Questions
Part 2: Frequently Asked Questions
Which AI model should a small or midsize business use?
Part 2 of the GXA AI Foundation Series covers the five names leaders keep hearing — Claude, ChatGPT, Copilot, Gemini, and Grok — and argues the choice turns less on the brand than on the public-versus-private split, which determines whether your data trains someone else’s model, and on matching the licensing tier to the task rather than paying frontier prices for light work.
What is the difference between public and private AI models?
It is the split that decides where your prompts and data go, and whether they contribute to training someone else’s model. For a business handling client, financial, or regulated data, that distinction — not the interface — is the deciding factor in which tool gets approved.
Why does AI spending get out of control?
Three reasons named in the session: individual subscriptions expensed on personal credit cards, overlapping tools doing the same job, and no single owner accountable for the AI budget line. Companies rarely overspend in one big decision — they creep into it.
How should a business evaluate an AI request or vendor pitch?
Run it through a four-question funnel: what is the value, what is the cost, what is the business impact, and what does done look like. Part 2 pairs that with five readiness questions to score your own organisation against before committing budget.
Does the same AI product name mean the same contract?
No. The session makes this point directly: the same product name comes with very different contracts across consumer, team, and enterprise tiers, and those differences govern data handling, administration, and control — not just price.
Keep Going
All three sessions are free and on-demand. Policy first, then the buying decision, then running it day to day.