June 26, 2025 • 19 mins
The agricultural landscape is evolving rapidly, and artificial intelligence (AI) stands at the forefront of this transformation. Jacqui Fatka, farm supply and biofuels economist with CoBank, takes us on a comprehensive journey through AI's emerging role in agricultural retail and farm supply cooperatives.
Far from threatening the traditional relationships between farmers and their trusted advisors, AI offers powerful tools to strengthen these connections. As Fatka explains, "That relationship is paramount. Farmers really depend on that trusted partner with those ag retailers." The technology enables agronomists to develop more precise prescriptions, capture critical field observations, and respond proactively to emerging threats – all while preserving the human touch that agriculture demands.
The accessibility of AI continues to grow, with entry points spanning from simple front-office applications to sophisticated supply chain optimization. Microsoft Teams' Co-Pilot feature, for instance, can streamline communication and documentation, while more advanced implementations might connect divisions within organizations that previously operated in silos. Fatka emphasizes the importance of privacy considerations and finding partners who truly understand agriculture's unique challenges rather than generic AI providers promising unrealistic returns.
Perhaps most significantly, AI offers a solution to one of agriculture's persistent challenges: preserving institutional knowledge when experienced staff members retire or change positions. By capturing detailed customer profiles and operational insights, AI systems create continuity that benefits both businesses and the farmers they serve, especially in today's tight labor market. As Fatka notes, "The relationships and how you really lean into knowing that producer, that grower, having it captured in an AI system, allows that easy transition."
Discover how this powerful technology is reshaping agricultural service delivery while honoring the human connections that remain at the heart of farming communities.
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Episode Transcript
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Sarah Muirhead (00:07):
Back office front office.
Will AI empower or disruptagriculture, retailers and farm
supply cooperatives?
Welcome to Feedstuffs in Focus,our podcast taking a look at
the big issues affecting thelivestock, poultry grain and
animal feed industries.
I'm your host, Sarah Muirhead.
This episode is sponsored byUnited Animal Health, a leader
(00:29):
in animal health and nutrition.
You can learn more about UnitedAnimal Health and how they're
working to advance animalscience worldwide by visiting
their website at www.
unitedANH.
com.
Joining us to talk about thepotential influence of AI on
agriculture, retailers and farmsupply cooperatives is Jacqui
Fatka, farm supply and biofuelseconomist with CoBank.
(00:53):
Jacqui, you've completed ananalysis of the value that AI
can have for agriculture,retailers and farm supply
cooperatives.
Let's talk about that.
Let's start by in the generalsense what opportunities does AI
offer farm suppliers?
Jacqui Fatka (01:11):
Yeah well, CoBank is a major partner in financing
a lot of our ag retail space,from your local grain co-ops,
you know, all the way throughthe supply chain, and I think
every day we are hearing in ourregular news cycle just this
expansion of AI and artificialintelligence and we're starting
(01:31):
to use it on our phones and oureveryday work tasks, and so I
really wanted to dig into someof the ways that ag retailers
can use this as a way to reallyenhance what they're doing,
improve operational efficienciesbut, you know, the most
important thing for them is tobe able to serve their farmer
customers and how they can usethese tools to simplify things,
(01:56):
enhance their offerings andreally just bring together a lot
of those tools that aren'tquite as scary as maybe they
once were, or even as high of acost as they once were several
years ago.
Sarah Muirhead (02:11):
Now, as you mentioned, ag co-ops and
retailers.
They serve kind of as thatcritical relationship bridge
between farmers and inputsuppliers, but there's kind of
some new distribution models anddisruptive technologies in some
ways that are kind ofchallenging that.
So it sounds like AI then is anopportunity for kind of the
preservation of thatrelationship that's long existed
(02:33):
and really a way to keep agco-ops and retailers as an
important part of that overallag supply chain.
Is that kind of the way to lookat that?
Jacqui Fatka (02:42):
Absolutely.
I mean that relationship isparamount.
You have to have a goodrelationship.
Farmers really depend on thattrusted partner with those ag
retailers and so being able tohave the best agronomic advice,
understanding what you know,what that farmer may need.
(03:05):
But you can't just enter in allthat data into an AI system,
right, that relationship is whatreally matters.
And so you know, say, anagronomist goes out to a farm
and is talking with that farmerand maybe they've worked with
that farmer for several years.
They may know some of theparticular nuances of that farm
(03:27):
or what that farmer maybeprefers, and so we like to call
it a ground truth of you know.
You may have the AI spit out arecommendation, but that
agronomist still needs to lookover that recommendation and
give that to the farmer.
So the AI might speed things up, enhance things.
So that's one thing, right,just developing the
(03:49):
prescriptions.
But every time an agronomistgoes to a farm, they can hit
record and start recording thatconversation, track some details
, or maybe they can instantlysay what are the open orders for
this farmer, and that's anotherway to do.
It can instantly say you knowwhat are the open orders for
this, this farmer, and you knowthat's.
That's another way to do it.
Also, we know that there'sdifferent scouting that happens.
(04:11):
That can happen with AI or weeddetection.
You know that those identify,identifying those, sense and
respond use cases, like we knowthat there's a heat, a heat, you
know, extra heat coming thisweek, or we've seen some pests
identified in this neighboringfarm.
So being able to reach out tothat farmer and say you know,
hey, we identified that maybesome potential concerns could be
(04:34):
coming, would you like to lookat that?
And again, it's all aboutbridging that relationship with
the grower, your customer, andand being able to really lean
into.
You know all of the details too, even you know knowing that
maybe someone just had abirthday or you know one of
their kids graduated.
Right, like capturing some ofthat information to really lean
(04:57):
into that, that thatrelationship, right, so you see
a farmer is calling you and youcan quickly enter into maybe the
AI system of this contact andyou may have a whole profile on
that particular farmer and beingable to just really lean into
that importance of therelationship.
It's not just dollars andproducts sold.
(05:18):
A lot of it is an ongoingunderstanding of who that farmer
is, what they like and alsosome of those even personal
information to really meet theirneeds and what they need on
their farming operation.
Sarah Muirhead (05:33):
So there's value in adopting AI, but how does
one determine when to getinvolved and where to start?
Jacqui Fatka (05:41):
Yeah.
So that was one big reason whyI wanted to look into this
report, because I think againback to the fact that AI is
becoming more common just ineveryday life for people.
So there's kind of a lot ofdifferent entry points that ag,
retailers, co-ops, people withinthe supply chain can start to
look at.
You know one of those is justin the front office.
(06:03):
You know the folks who areworking on meetings.
You know Microsoft Teams has anability to.
You know co-pilot allows, youknow, a lot of simplification of
writing emails better.
You know, maybe writing a jobdescription more specifically,
recording your online meetingsor just if you have even a
(06:23):
meeting where people are all inthe same room recording that,
instantly having key takeawaysthat are pulled from that and
action items being able tocapture some of that.
So you know that's one easykind of entry point is some of
the front office things.
You know.
Obviously there's someopportunities to.
You know the customerrelationship management systems.
(06:46):
A lot of those CRMs aren'talways designed to really look
at the selling inputs and theroutine duties of an agronomist,
but if we can use AI to helptalk to those together, right.
But then there's even the factthat we have a lot of times.
A grain co-op, for instance,has an agronomist and has a team
(07:08):
that's working to help thatfarmer grow the highest yield,
best performing on their land,right, but then they're not
necessarily working with theirgrain merchandisers to say, okay
, well, we know that this farmis going to have X amount of
bushels, likely this fall, basedon all of these variables that
(07:29):
we've helped them determine.
So then also within your ownorganization, talking between
divisions, helping them identifywhere things might, you know
how you could say, hey, we justsaw a $4 increase.
You know, we hit $4 corn or$4.50 corn.
You know, let's try to priceout some of that, looking at
(07:50):
working together withindivisions.
But then also, too, the supplychain management.
We know that AI can help usidentify maybe the most
efficient routes on things.
If we know that last year wesold X amount of product out of
this facility and we didn't sellas much out of a different
facility, but we had the equalamounts at both.
(08:13):
Right, being able to shift andidentify where we might need to
have an increase in supply ormake sure we're ready for that,
or back to that identify and usescenarios.
We see a disease or a pastpressure in a specific area.
We want to make sure that oursupply chain is ready to meet
(08:34):
that too.
So having AI helps kind ofpiece all of those different
variables together and put it atour fingertips.
Sarah Muirhead (08:45):
I know some AI tools are free, you know, and
some you pay for.
But how do you determine whatthat level of investment should
be, and is it best to be anearly adopter or kind of to, you
know, wait in a little bit andsee how things are progressing?
Any kind of advice or overallthinking in that regard?
Jacqui Fatka (09:05):
Yeah.
So I mean, one of the biggestthings that I kept hearing is
make sure you understand theprivacy of the different tools
you're using.
You know, we hear a lot aboutChatGPT and Grok and some of
those tools.
The problem is, if you put acustomer information in there
that then becomes public, and sowhat we, you know, knowing
(09:27):
those guardrails andestablishing those guardrails
within your company is reallyreally important to know that if
you are going to, you knowpractice different things or put
in input different information,know where it's going.
There's a there's a case,apparently, that a dentist put
in all the personal informationinto a letter so that he could
(09:49):
send out something to everybodyyou know, letting them know when
their next appointment was, andthen, all of a sudden, all of
that personal information wasthen public.
And so you know, that's one ofthe most important things is
establishing those guardrails,understanding privacy and then
also too, I think, findingpartners that really understand
(10:09):
agriculture.
The whole world is just kind ofexploding, with different
companies who are saying, youknow, promising huge ROIs and
offering the world.
But finding those ag partners,those companies within this AI
space that really understandagriculture, is really important
(10:31):
because you and I know howunique and as well as diverse
the ag industry is.
So knowing some of those uniquechallenges within this business
system with a partner is reallygood.
And also, too, if you're anorganization, having somebody
within the organization thatkind of is the AI point person,
(10:51):
being able to help everybody onstaff kind of navigate some of
the opportunities, is alsoreally beneficial to be able to
again help just ease into thisspace because the cost is
actually gone down quite a lot.
There's some tools that aren'tvery much.
You know there's some agspecific companies that are out
(11:13):
there that really do work withyou, regardless of your size.
So you know it doesn't have tobe a huge cost.
You know some of those biggercompanies are able to capture
larger ROIs because there's moreopportunity for them to capture
those savings if you're lookingat supply chain management.
(11:34):
But in some ways it really doesenhance it kind of levels the
field in some ways as well.
Sarah Muirhead (11:43):
You mentioned ROI.
Is there something a level ofROI in terms of what you can
expect to achieve, or how bestdo you go about measuring that?
It probably depends on how muchyour adoption is of AI and how
quickly you adopt it, but isthere a general rule that folks
should think about?
Jacqui Fatka (12:01):
You know when I was talking with people, it
really does, as I just mentioned.
Sometimes those largercompanies can see, you know,
even 70 times ROI if they reallybasically if it was able to
shine a light on an extremeinefficiency that they didn't
see before.
You know you think about supplychain and how it moves and
(12:22):
you're thinking about trains andunloading.
You know there's a lot ofopportunity.
So it kind of depends on whatlevel of adoption.
You are right.
So you know, incorporating aco-pilot, recording, a
transcription right and keytakeaways, you're not going to
have a huge ROI on that right,but you know some of those
supply chains.
You're able to see a lot morevalue.
(12:45):
But you know also to the,sometimes it's not necessarily
savings, it's just amplifyingwhat somebody can do.
Right, like you think about anagronomist and a lot of times
agronomists feel reallyoverwhelmed with all the data
that they're trying to capturewhen they go visit a farm or
(13:05):
they're entering things in andso being able to they don't have
to manually enter it and havingsome kind of system that would
record, condense and then youknow, create that, you know
generate that kind of profile ofthat customer.
(13:28):
That kind of profile of thatcustomer that saves a lot of
time.
That probably saves even maybehaving to hire an administrative
assistant for some of thoseagronomists, right?
So there's varying levels ofcost savings, time savings, and
sometimes it's not necessarily asavings, but again it goes back
to how do you better serve yourfarmer customers and how do you
do your job better.
And the other thing too and wehaven't talked about this yet is
(13:51):
we have a lot of turnover inthis space too, and so the
relationships and how you reallylean into knowing that producer
, that grower, having itcaptured in an AI system, allows
that easy transition.
If an agronomist decides toleave or retire or go to another
company, that profile is stillthere for that next person that
(14:12):
they come in.
So it helps just augment whatthey're doing on the ground too.
So you know you can't reallyput an ROI on that, but we know
how valuable it is because,again, it goes back to how you
best serve your farmer customers.
Sarah Muirhead (14:25):
Yeah, there's lots of information and
knowledge you can lose.
Valuable it is because, again,it goes back to how you best
serve your farmer customers.
Yeah, there's lots ofinformation and knowledge you
can lose when someone retires ormoves on, and if you can
preserve that, that certainlyputs the new person at a good
starting point.
So it sounds like there couldbe.
I know people always fear this,but in terms of a staffing
standpoint, there could be somestaffing advantages as well from
AI adoption.
Jacqui Fatka (14:46):
You know, it's kind of been a fear and an
opportunity, right.
And so, again, right now,especially AI is an augmented
intelligence, right, like, westill know that there's some
hallucinations, we still knowthat there's some issues.
I talked earlier about thatground truthing, being able to
have somebody who reallyunderstands what is going on and
(15:11):
verifying it.
We can't just flat out trustwhatever is being spit out by.
You know, these different AItools they're getting better and
they're improving.
Ai tools they're getting betterand they're improving.
So, you know, I think wedefinitely see that there's
opportunities for it to augmentour labor, maybe allow
(15:32):
agronomists to maybe cover moreacres, you know, serve more
farmers, be able to maybe havefewer people.
But you know, we're challengedright now with meeting all of
these labor, filling all theselabor spots on some of these,
you know, very rural, remotegrain locations, and so you know
(15:53):
, there's always a certain levelof jobs that may be replaced or
, you know, made differentbecause of this.
Right, like, even a droneoperator, they are going to need
different skills than maybe anoperator fly, you know.
Or a sprayer unit, right, butit's different, but you need
(16:16):
both of those skills.
Sarah Muirhead (16:19):
So some opportunities, probably a few
challenges just how you manageAI adoption and execution as you
move forward and bring it intoyour operation.
You mentioned grain operations,but I'm assuming, too, there'd
be a lot of application when itcomes to livestock operations,
and nutritionists andveterinarians could use a lot of
(16:39):
these tools, perhaps in thesame way that you're talking
about agronomists using thesetools.
Jacqui Fatka (16:44):
Absolutely, and it's funny because in the
livestock industry, ai has atotal different meaning, right,
artificial insemination orartificial intelligence.
But you know, we definitelythere's a lot of tools in the
livestock space that are alreadyhelping us know what the
animals are, you know if they'realmost sick or you know their
(17:05):
levels are changing, and that'sthe great thing is, there's so
many tools that we've started todabble in in the overall ag
space.
But just the bigger steppingback and seeing how technology
worldwide is advancing so fast,we want to make sure that ag is
(17:28):
able to participate in thisright.
We we don't want there to be adigital divide and you know, an
AI divide because the ag sectorisn't able or willing to to to
to dig into this.
And you know one of the, thefolks that I talked to, said it
so well to to to dig into this.
And you know one of the folksthat I talked to said it so well
he's like sometimes we'rewaiting for people, we're we're
not giving the next technology,but we should really encourage
(17:53):
our, our producers, the peoplethat we're, that we're servicing
, to go where we want them to go, not meet them where they are
right, like we.
Ag is very good about technologyadoption but looking back,
they've also been burned in someof these right Like bad
technologies or technologiesthat were really cost costly,
(18:14):
that maybe didn't have a goodreturn on investment.
But now we're starting to seethings decrease in cost.
The opportunities for everyonein the ag space is really
expanding and at the end of theday that's going to really be
important as we look at somewhattighter margins you know, just
(18:47):
a constrained overall agfinancial environment able to
continue to farm another day, tohave that money, to continue to
reinvest and be profitable, andthat just kind of snowballing
effect of technology adoptionand the ways that it can trickle
through the whole supply chain.
Sarah Muirhead (19:05):
Our thanks to Jacqui Fatka, farm supply and
biofuels economist with CoBank.
This episode has been sponsoredby United Animal Health, a
leader in animal health andnutrition.
You can learn more about UnitedAnimal Health and how they're
working to advance animalscience worldwide by visiting
their website at www.
unitedANH.
com.
(19:26):
I'm Sarah Muirhead and you havebeen listening to Feedstuffs in
Focus.
If you would like to hear moreconversations about some of the
big issues affecting thelivestock, poultry grain and
animal feed industries,subscribe to this podcast on
your favorite podcast channel.
Until next time, have a greatday and thank you for listening.
Back office front office.
Will AI empower or disruptagriculture, retailers and farm
supply cooperatives?
Welcome to Feedstuffs in Focus,our podcast taking a look at
the big issues affecting thelivestock, poultry grain and
animal feed industries.
I'm your host, Sarah Muirhead.
This episode is sponsored byUnited Animal Health, a leader
(00:29):
in animal health and nutrition.
You can learn more about UnitedAnimal Health and how they're
working to advance animalscience worldwide by visiting
their website at www.
unitedANH.
com.
Joining us to talk about thepotential influence of AI on
agriculture, retailers and farmsupply cooperatives is Jacqui
Fatka, farm supply and biofuelseconomist with CoBank.
(00:53):
Jacqui, you've completed ananalysis of the value that AI
can have for agriculture,retailers and farm supply
cooperatives.
Let's talk about that.
Let's start by in the generalsense what opportunities does AI
offer farm suppliers?
Jacqui Fatka (01:11):
Yeah well, CoBank is a major partner in financing
a lot of our ag retail space,from your local grain co-ops,
you know, all the way throughthe supply chain, and I think
every day we are hearing in ourregular news cycle just this
expansion of AI and artificialintelligence and we're starting
(01:31):
to use it on our phones and oureveryday work tasks, and so I
really wanted to dig into someof the ways that ag retailers
can use this as a way to reallyenhance what they're doing,
improve operational efficienciesbut, you know, the most
important thing for them is tobe able to serve their farmer
customers and how they can usethese tools to simplify things,
(01:56):
enhance their offerings andreally just bring together a lot
of those tools that aren'tquite as scary as maybe they
once were, or even as high of acost as they once were several
years ago.
Sarah Muirhead (02:11):
Now, as you mentioned, ag co-ops and
retailers.
They serve kind of as thatcritical relationship bridge
between farmers and inputsuppliers, but there's kind of
some new distribution models anddisruptive technologies in some
ways that are kind ofchallenging that.
So it sounds like AI then is anopportunity for kind of the
preservation of thatrelationship that's long existed
(02:33):
and really a way to keep agco-ops and retailers as an
important part of that overallag supply chain.
Is that kind of the way to lookat that?
Jacqui Fatka (02:42):
Absolutely.
I mean that relationship isparamount.
You have to have a goodrelationship.
Farmers really depend on thattrusted partner with those ag
retailers and so being able tohave the best agronomic advice,
understanding what you know,what that farmer may need.
(03:05):
But you can't just enter in allthat data into an AI system,
right, that relationship is whatreally matters.
And so you know, say, anagronomist goes out to a farm
and is talking with that farmerand maybe they've worked with
that farmer for several years.
They may know some of theparticular nuances of that farm
(03:27):
or what that farmer maybeprefers, and so we like to call
it a ground truth of you know.
You may have the AI spit out arecommendation, but that
agronomist still needs to lookover that recommendation and
give that to the farmer.
So the AI might speed things up, enhance things.
So that's one thing, right,just developing the
(03:49):
prescriptions.
But every time an agronomistgoes to a farm, they can hit
record and start recording thatconversation, track some details
, or maybe they can instantlysay what are the open orders for
this farmer, and that's anotherway to do.
It can instantly say you knowwhat are the open orders for
this, this farmer, and you knowthat's.
That's another way to do it.
Also, we know that there'sdifferent scouting that happens.
(04:11):
That can happen with AI or weeddetection.
You know that those identify,identifying those, sense and
respond use cases, like we knowthat there's a heat, a heat, you
know, extra heat coming thisweek, or we've seen some pests
identified in this neighboringfarm.
So being able to reach out tothat farmer and say you know,
hey, we identified that maybesome potential concerns could be
(04:34):
coming, would you like to lookat that?
And again, it's all aboutbridging that relationship with
the grower, your customer, andand being able to really lean
into.
You know all of the details too, even you know knowing that
maybe someone just had abirthday or you know one of
their kids graduated.
Right, like capturing some ofthat information to really lean
(04:57):
into that, that thatrelationship, right, so you see
a farmer is calling you and youcan quickly enter into maybe the
AI system of this contact andyou may have a whole profile on
that particular farmer and beingable to just really lean into
that importance of therelationship.
It's not just dollars andproducts sold.
(05:18):
A lot of it is an ongoingunderstanding of who that farmer
is, what they like and alsosome of those even personal
information to really meet theirneeds and what they need on
their farming operation.
Sarah Muirhead (05:33):
So there's value in adopting AI, but how does
one determine when to getinvolved and where to start?
Jacqui Fatka (05:41):
Yeah.
So that was one big reason whyI wanted to look into this
report, because I think againback to the fact that AI is
becoming more common just ineveryday life for people.
So there's kind of a lot ofdifferent entry points that ag,
retailers, co-ops, people withinthe supply chain can start to
look at.
You know one of those is justin the front office.
(06:03):
You know the folks who areworking on meetings.
You know Microsoft Teams has anability to.
You know co-pilot allows, youknow, a lot of simplification of
writing emails better.
You know, maybe writing a jobdescription more specifically,
recording your online meetingsor just if you have even a
(06:23):
meeting where people are all inthe same room recording that,
instantly having key takeawaysthat are pulled from that and
action items being able tocapture some of that.
So you know that's one easykind of entry point is some of
the front office things.
You know.
Obviously there's someopportunities to.
You know the customerrelationship management systems.
(06:46):
A lot of those CRMs aren'talways designed to really look
at the selling inputs and theroutine duties of an agronomist,
but if we can use AI to helptalk to those together, right.
But then there's even the factthat we have a lot of times.
A grain co-op, for instance,has an agronomist and has a team
(07:08):
that's working to help thatfarmer grow the highest yield,
best performing on their land,right, but then they're not
necessarily working with theirgrain merchandisers to say, okay
, well, we know that this farmis going to have X amount of
bushels, likely this fall, basedon all of these variables that
(07:29):
we've helped them determine.
So then also within your ownorganization, talking between
divisions, helping them identifywhere things might, you know
how you could say, hey, we justsaw a $4 increase.
You know, we hit $4 corn or$4.50 corn.
You know, let's try to priceout some of that, looking at
(07:50):
working together withindivisions.
But then also, too, the supplychain management.
We know that AI can help usidentify maybe the most
efficient routes on things.
If we know that last year wesold X amount of product out of
this facility and we didn't sellas much out of a different
facility, but we had the equalamounts at both.
(08:13):
Right, being able to shift andidentify where we might need to
have an increase in supply ormake sure we're ready for that,
or back to that identify and usescenarios.
We see a disease or a pastpressure in a specific area.
We want to make sure that oursupply chain is ready to meet
(08:34):
that too.
So having AI helps kind ofpiece all of those different
variables together and put it atour fingertips.
Sarah Muirhead (08:45):
I know some AI tools are free, you know, and
some you pay for.
But how do you determine whatthat level of investment should
be, and is it best to be anearly adopter or kind of to, you
know, wait in a little bit andsee how things are progressing?
Any kind of advice or overallthinking in that regard?
Jacqui Fatka (09:05):
Yeah.
So I mean, one of the biggestthings that I kept hearing is
make sure you understand theprivacy of the different tools
you're using.
You know, we hear a lot aboutChatGPT and Grok and some of
those tools.
The problem is, if you put acustomer information in there
that then becomes public, and sowhat we, you know, knowing
(09:27):
those guardrails andestablishing those guardrails
within your company is reallyreally important to know that if
you are going to, you knowpractice different things or put
in input different information,know where it's going.
There's a there's a case,apparently, that a dentist put
in all the personal informationinto a letter so that he could
(09:49):
send out something to everybodyyou know, letting them know when
their next appointment was, andthen, all of a sudden, all of
that personal information wasthen public.
And so you know, that's one ofthe most important things is
establishing those guardrails,understanding privacy and then
also too, I think, findingpartners that really understand
(10:09):
agriculture.
The whole world is just kind ofexploding, with different
companies who are saying, youknow, promising huge ROIs and
offering the world.
But finding those ag partners,those companies within this AI
space that really understandagriculture, is really important
(10:31):
because you and I know howunique and as well as diverse
the ag industry is.
So knowing some of those uniquechallenges within this business
system with a partner is reallygood.
And also, too, if you're anorganization, having somebody
within the organization thatkind of is the AI point person,
(10:51):
being able to help everybody onstaff kind of navigate some of
the opportunities, is alsoreally beneficial to be able to
again help just ease into thisspace because the cost is
actually gone down quite a lot.
There's some tools that aren'tvery much.
You know there's some agspecific companies that are out
(11:13):
there that really do work withyou, regardless of your size.
So you know it doesn't have tobe a huge cost.
You know some of those biggercompanies are able to capture
larger ROIs because there's moreopportunity for them to capture
those savings if you're lookingat supply chain management.
(11:34):
But in some ways it really doesenhance it kind of levels the
field in some ways as well.
Sarah Muirhead (11:43):
You mentioned ROI.
Is there something a level ofROI in terms of what you can
expect to achieve, or how bestdo you go about measuring that?
It probably depends on how muchyour adoption is of AI and how
quickly you adopt it, but isthere a general rule that folks
should think about?
Jacqui Fatka (12:01):
You know when I was talking with people, it
really does, as I just mentioned.
Sometimes those largercompanies can see, you know,
even 70 times ROI if they reallybasically if it was able to
shine a light on an extremeinefficiency that they didn't
see before.
You know you think about supplychain and how it moves and
(12:22):
you're thinking about trains andunloading.
You know there's a lot ofopportunity.
So it kind of depends on whatlevel of adoption.
You are right.
So you know, incorporating aco-pilot, recording, a
transcription right and keytakeaways, you're not going to
have a huge ROI on that right,but you know some of those
supply chains.
You're able to see a lot morevalue.
(12:45):
But you know also to the,sometimes it's not necessarily
savings, it's just amplifyingwhat somebody can do.
Right, like you think about anagronomist and a lot of times
agronomists feel reallyoverwhelmed with all the data
that they're trying to capturewhen they go visit a farm or
(13:05):
they're entering things in andso being able to they don't have
to manually enter it and havingsome kind of system that would
record, condense and then youknow, create that, you know
generate that kind of profile ofthat customer.
(13:28):
That kind of profile of thatcustomer that saves a lot of
time.
That probably saves even maybehaving to hire an administrative
assistant for some of thoseagronomists, right?
So there's varying levels ofcost savings, time savings, and
sometimes it's not necessarily asavings, but again it goes back
to how do you better serve yourfarmer customers and how do you
do your job better.
And the other thing too and wehaven't talked about this yet is
(13:51):
we have a lot of turnover inthis space too, and so the
relationships and how you reallylean into knowing that producer
, that grower, having itcaptured in an AI system, allows
that easy transition.
If an agronomist decides toleave or retire or go to another
company, that profile is stillthere for that next person that
(14:12):
they come in.
So it helps just augment whatthey're doing on the ground too.
So you know you can't reallyput an ROI on that, but we know
how valuable it is because,again, it goes back to how you
best serve your farmer customers.
Sarah Muirhead (14:25):
Yeah, there's lots of information and
knowledge you can lose.
Valuable it is because, again,it goes back to how you best
serve your farmer customers.
Yeah, there's lots ofinformation and knowledge you
can lose when someone retires ormoves on, and if you can
preserve that, that certainlyputs the new person at a good
starting point.
So it sounds like there couldbe.
I know people always fear this,but in terms of a staffing
standpoint, there could be somestaffing advantages as well from
AI adoption.
Jacqui Fatka (14:46):
You know, it's kind of been a fear and an
opportunity, right.
And so, again, right now,especially AI is an augmented
intelligence, right, like, westill know that there's some
hallucinations, we still knowthat there's some issues.
I talked earlier about thatground truthing, being able to
have somebody who reallyunderstands what is going on and
(15:11):
verifying it.
We can't just flat out trustwhatever is being spit out by.
You know, these different AItools they're getting better and
they're improving.
Ai tools they're getting betterand they're improving.
So, you know, I think wedefinitely see that there's
opportunities for it to augmentour labor, maybe allow
(15:32):
agronomists to maybe cover moreacres, you know, serve more
farmers, be able to maybe havefewer people.
But you know, we're challengedright now with meeting all of
these labor, filling all theselabor spots on some of these,
you know, very rural, remotegrain locations, and so you know
(15:53):
, there's always a certain levelof jobs that may be replaced or
, you know, made differentbecause of this.
Right, like, even a droneoperator, they are going to need
different skills than maybe anoperator fly, you know.
Or a sprayer unit, right, butit's different, but you need
(16:16):
both of those skills.
Sarah Muirhead (16:19):
So some opportunities, probably a few
challenges just how you manageAI adoption and execution as you
move forward and bring it intoyour operation.
You mentioned grain operations,but I'm assuming, too, there'd
be a lot of application when itcomes to livestock operations,
and nutritionists andveterinarians could use a lot of
(16:39):
these tools, perhaps in thesame way that you're talking
about agronomists using thesetools.
Jacqui Fatka (16:44):
Absolutely, and it's funny because in the
livestock industry, ai has atotal different meaning, right,
artificial insemination orartificial intelligence.
But you know, we definitelythere's a lot of tools in the
livestock space that are alreadyhelping us know what the
animals are, you know if they'realmost sick or you know their
(17:05):
levels are changing, and that'sthe great thing is, there's so
many tools that we've started todabble in in the overall ag
space.
But just the bigger steppingback and seeing how technology
worldwide is advancing so fast,we want to make sure that ag is
(17:28):
able to participate in thisright.
We we don't want there to be adigital divide and you know, an
AI divide because the ag sectorisn't able or willing to to to
to dig into this.
And you know one of the, thefolks that I talked to, said it
so well to to to dig into this.
And you know one of the folksthat I talked to said it so well
he's like sometimes we'rewaiting for people, we're we're
not giving the next technology,but we should really encourage
(17:53):
our, our producers, the peoplethat we're, that we're servicing
, to go where we want them to go, not meet them where they are
right, like we.
Ag is very good about technologyadoption but looking back,
they've also been burned in someof these right Like bad
technologies or technologiesthat were really cost costly,
(18:14):
that maybe didn't have a goodreturn on investment.
But now we're starting to seethings decrease in cost.
The opportunities for everyonein the ag space is really
expanding and at the end of theday that's going to really be
important as we look at somewhattighter margins you know, just
(18:47):
a constrained overall agfinancial environment able to
continue to farm another day, tohave that money, to continue to
reinvest and be profitable, andthat just kind of snowballing
effect of technology adoptionand the ways that it can trickle
through the whole supply chain.
Sarah Muirhead (19:05):
Our thanks to Jacqui Fatka, farm supply and
biofuels economist with CoBank.
This episode has been sponsoredby United Animal Health, a
leader in animal health andnutrition.
You can learn more about UnitedAnimal Health and how they're
working to advance animalscience worldwide by visiting
their website at www.
unitedANH.
com.
(19:26):
I'm Sarah Muirhead and you havebeen listening to Feedstuffs in
Focus.
If you would like to hear moreconversations about some of the
big issues affecting thelivestock, poultry grain and
animal feed industries,subscribe to this podcast on
your favorite podcast channel.
Until next time, have a greatday and thank you for listening.
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