The AI Briefing

Tom from Concept to Cloud is back with another AI Briefing. This episode covers the three things that make or break AI adoption in organisations running on legacy systems: getting your data AI-ready (integrity, alignment and consistency — garbage in, garbage out still applies), managing cost with an AI FinOps mindset, and choosing the right model for the right job rather than always reaching for the most expensive one.

Concept to Cloud helps organisations modernise their systems and data to leverage AI effectively and cost-efficiently.

What is The AI Briefing?

The AI Briefing is your 5-minute daily intelligence report on AI in the workplace. Designed for busy corporate leaders, we distill the latest news, emerging agentic tools, and strategic insights into a quick, actionable briefing. No fluff, no jargon overload—just the AI knowledge you need to lead confidently in an automated world.

Hi, folks. Welcome back to another AI briefing.

I know it's been a while,

so for those of you who do not

know me, my name is Tom, and I

work for Concept to Cloud.

And we deal an awful lot with

legacy

systems engineering enhancements, improvements

to be able to leverage data more effectively

inside an AI ecosystem, along with helping enable

other companies leverage their systems as

effectively as possible.

Since I last did an AI briefing, the

world has moved on a reasonable amount still.

And so what you've got to think about

is how you're going to leverage

the data, the systems, and

the applications you currently use in

a modern AI centric environment.

And of course, data remains key because without

data, there isn't really any AI enablement

that you can do because there's nothing for

it to be able to deal with.

Even if you just think about a frontier

model, it's trained with a huge

corpus of data.

And so if you want to be able

to leverage that inside of your organization, not

the huge corpus of data, but leverage AI

to better the discovery

of information inside of your organization, then you

need to think about things like data

integrity, data alignment, data consistency,

because as ever, even in an AI world,

you will get garbage in and garbage out.

On top of the garbage out, it will

cost you more money for systems that are

not designed to

enable AI from a token perspective.

Now, every time you run a query using

AI, ask it a question, whatever, there's a

number of tokens that are used and that's

starting to cost money.

And so the other thing you need to

bear in mind is also like FinOps, trying

to understand how much AI is going to

cost you as an organization going forward so

that when you come to budget for it,

you can use it in the most cost

effective manner. It brings me on to point

number three and the one is finding the

right model for the right job because running

Opus 4 .8 for a small text

generation project is probably not what you're after.

Whereas having you understand an awful lot

of data engineering, for example, or write code,

Opus is probably what you're after.

And those things cost different amounts of money.

So if you're using Heiku versus Opus, the

amount that costs you to write that query

and get that response is different.

So make sure that whilst you're leveraging AI,

you use the right model and you pick

the right model, do some research, start with

the most expensive and step back until that

quality drops off because that is how you

optimize your FinOps for an AI environment.