The AI Revolution in Kitchen Design: How to Evaluate What Actually Matters for Your Business

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Hey there, it's Brandy Lawson with Elevated! Today we're talking about something that's creating both excitement and anxiety in the kitchen and bath industry – artificial intelligence. 

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Episode Transcript

(00:05):
Hey there, it's Brandi Lawsonwith Elevated.
Today we're talking aboutsomething that's creating both
excitement and anxiety in thekitchen and bath industry.
Artificial intelligence.
There's so much noise about AIright now.
It's like trying to have aconversation at a construction
site.
What I'm seeing across theindustry is fascinating.
While everyone's talking aboutAI for design, vis visualization

(00:27):
and renderings.
Which has its place, the realgame changer is happening on the
business side.
Yet that's exactly where thereseems to be the most resistance
and confusion.
The truth is, most kitchen andbath professionals are leaving
money on the table by focusingon the wrong AI applications.
Today I'm giving you a practicalframework to evaluate which AI

(00:49):
capabilities actually delivervalue for your business so you
can cut through the hype.
And focus on technologies thatimpact your bottom line.
Let's cut through the noise andbreak down what matters when
evaluating AI technologies foryour kitchen and bath business.
First, identify what I call AIthat matters versus AI that
markets real.

(01:10):
AI solves specific businessproblems.
It's not just a flashy demo.
You might be using AI forautomatic cabinet placement
optimization.
Designers still make thecreative decisions, but the AI
handles the mundane spatialcalculation.
This can save approximatelythree hours per kitchen design
while improving accuracy.
That is AI that matters.

(01:30):
By contrast, I've seen softwarewith impressive looking AI
visualizations that lookfantastic in demos, but don't
integrate with actual workingdrawings.
That's ai, that markets it looksgood in a sales pitch, but
doesn't improve your workflow.
Second, evaluate the quality ofthe AI's training data.
All AI is only as good as thedata it was trained on.

(01:52):
Ask vendors, what data trainedyour AI for kitchen design?
Specifically, was it trained onactual architectural plans and
building codes or just internetimages?
Third, consider the human AIworkflow.
The most effective AI tools inour industry don't replace
designers.
They augment them.
Look for software where AIhandles repetitive tasks while

(02:14):
keeping humans in control ofcreative and client facing
decisions.
The sweet spot is AI thathandles the procedural work.
Freeing your team to focus onthe work that requires human
creativity and relationshipbuilding.
Fourth, assess integrationcapabilities.
Standalone AI tools often createmore work than they solve the
most valuable AI solutionsintegrate seamlessly with your

(02:36):
existing design and projectmanagement workflow.
Finally evaluate the learningcurve versus benefit ratio.
Some AI tools deliver amazingresults, but require weeks of
training to use effectively.
Others might be less powerful,but can be implemented
immediately.
Be realistic about your team'scapacity to adopt new
technology.

(02:57):
The best AI is the one your teamwill actually use.
Let me share a practicalframework that you can use to
evaluate AI capabilities in yoursoftware selection process.
Create a simple grid with yourkey business processes.
Down one side, let's say designcreation specifications, client
presentation, revisions,ordering, and these evaluation

(03:18):
questions across the top.
What specific task does the AIautomate or enhance?
How much time could thisrealistically say per project?
Does it require extensivetraining or setup?
How does it integrate with ourexisting workflow and.
What creative control do wemaintain for each potential AI

(03:38):
feature?
Score these questions from oneto five.
This helps transform vaguepromises into concrete
evaluation.
For example, when evaluating AIrendering tools, you may
discover that one optiongenerates photorealistic images
70% faster than the existingmanual process.
It requires minimal training,works directly with your

(03:59):
existing design files, and stillallows designers to control
material selection and lightingthat with got a score high
across the board.
Meanwhile, an AI specificationwriter that promises to
automatically generate completespecifications.
But requires extensive manualdata entry, doesn't integrate
with the current designsoftware, and frequently makes

(04:20):
errors requiring humancorrection.
This one would score poorly onthe evaluation grid.
This framework can help identifywhich AI features deliver
genuine value versus which onesmight just be expensive
distractions.
This week, you can map your wayto AI feature clarity.
Head to fiery effects.com/chooseand download the worksheet.

(04:41):
Take a software solution you'realready considering or maybe
already using, and put itsclaims to the test.
Ask these specific questions.
What measurable business outcomewill this AI feature improve?
How much setup and training isrequired before seeing benefits?
What control do we maintain overthe process and output?
How does this integrate with ourexisting workflow and what

(05:05):
happens when the AI makes amistake?
Because it will remember, thegoal isn't AI for AI's sake.
The goal is solving realbusiness problems and creating
competitive advantage.
Some of the most valuable AItools in our industry right now
focus on the mundane tasks likeautomated measurements,
specification generation anderror detection, not the flashy

(05:25):
stuff that makes for great demo.
Next week we'll coverimplementing your chosen
software and creating a roadmapthat ensures successful adoption
across your team.
If this episode helped you cutthrough some of the AI
confusion, share it with anotherdesign professional who's trying
to sort out this AI mystery.