AI a ɛyɛ adwuma: Bayer nsua ma Ghana akuafoɔ

117 billion data points. Saa nɔma no te sɛ anansesɛm, nanso ɛno ne Bayer Crop Science de si wɔn AI so. Na ɛnyɛ sɛ wɔto AI ho nkɔmmɔ kɛkɛ; wɔde no reyɛ adwuma a ɛda adi: wɔn GenAI adwinnade E.L.Y. ama agronomists (afuw ho adwenemfo) atu mpɔn 60%, na ɛreboa 1,500+ adwumayɛfoɔ a wɔwɔ field so.

Most adwumakuo (kɛse anaa ketewa) gye di sɛ “AI pilot” a wɔbɛyɛ no ara ne nkonimdi. Nanso nea ɛtaa si ne sɛ pilot no bɛkɔ; afei w’ani bɛgye LinkedIn, na adwuma mu deɛ, adeɛ biara nsakra. Bayer asɛm no kyerɛ wɔn a wɔwɔ Ghana—akuafoɔ, agribusiness, adwumafie, ne mpo sukuu—sɛ AI success nyɛ software pɛ; ɛyɛ data culture, adwuma mu nsakrae, ne pain point baako a wodi mu denneennen.

Saa post yi yɛ “AI ne Adwumafie ne Nwomasua Wɔ Ghana” series no mu baako. Asɛmpa no? Sɛ wopɛ sɛ AI boa wo kurom anaa wo kuropɔn mu adwumadie, Bayer nsua no betumi ama wo roadmap a ɛyɛ den na ɛyɛ practical.

Nea Bayer yɛe a ɛma AI dii mu: data culture a wɔkyekyeɛ mfe 12

Bayer anaa Monsanto tete mu no, wogyee The Climate Corporation (FieldView) toom wɔ 2013 mu. Nnipa pii hwɛ saa deal no sɛ “platform purchase” pɛ. Nanso Bayer CIO Amanda McClerren kyerɛ sɛ ɛmaa wɔn data culture, talent, ne digital product thinking a ɛyɛ den.

Nea ɛkyerɛ ankasa ne sɛ: wosii data infrastructure ansa na GenAI hype no reba. Wɔboaboaa field-testing data ano, yɛɛ semantic tools ma data no yɛ “discoverable,” na wosii data warehouse a ɛate sɛ wopɛ sɛ wode si adwuma so.

Nsɛm a Ghana mpɛn pii yɛ no bɔne

Nea mihu no ne sɛ, Ghana mu adwumakuo bebree pɛ “AI app” ntɛm, nanso:

• data no gu folder ahorow mu, anaa notebook mu
• sɛ wopɛ record a ɛfa pesticide anaa fertilizer ho a, obi pɛ sɛ ɔfrɛ “ɔpanyin bi” na ɔka
• adwuma mu “knowledge” no wɔ nnipa mu, ɛnyɛ system mu

AI deɛ, ɛtumi boa. Nanso AI nni aduan a: data. Sɛ data no ntɔ mu, AI no bɛyɛ “nice demo,” na ɛrenyɛ adwuma a ɛwɔ ROI.

Data moat no: 117bn data points ne “failures” a wɔkorae

Bayer ka sɛ wɔwɔ 117 billion data points a ɛfa seed performance ho. Nea ɛma eyi ho hia ne sɛ, ɛnyɛ “success data” pɛ. Wɔwɔ data wɔ:

• nneɛma a eduu market
• nneɛma a ɛwɔ pipeline mu nanso enni mu
• genetic information a ɛfa hybrid/variety ahorow ho

Saa mix yi ma wotumi hwehwɛ nsɛm a ɛyɛ den: genetic combinations a ɛyɛ adwuma wɔ environment bɛn mu?

Ghana bridge: “data moat” betumi ayɛ local

Ghana akuafoɔ ne agribusiness mpɛn pii susu sɛ “data moat” yɛ kɛse a corporations nkutoo tumi yɛ. M’anim yɛ den wɔ saa asɛm no ho. Sɛ wopɛ data moat a ɛyɛ Ghana-appropriate a, ɛfiri:

• farm logs a ɛyɛ consistent (date, rain estimate, input, labour, yield)
• local soil tests (mpɔtam biara nni sɛnea ɛte)
• price & market movement data (especially December–January, bere a demand sɛe anaa kɔ soro)
• extension officers’ notes (knowledge a ɛwɔ field mu)

Nea ɛhia ne standard. Sɛ cooperative anaa nucleus farmer system bi yɛ template baako, data no bɛyɛ “trainable.”

AI a ɛma adwumayɛfoɔ nya mfaso: E.L.Y. ne “four hours a week”

Bayer E.L.Y. no yɛ system a ɛboaboa wɔn agronomic knowledge ne product recommendation sheets ano, na field agronomists de pɛ nsɛm ntɛm. Result: 60% productivity increase na ɛkora wɔn bɛyɛ 4 hours dapɛn biara a anka wɔde bɛhwehwɛ documents.

Saa nɔma yi kyerɛ point bi a Ghana mu yebu no mpɛn pii: AI value no fi frontline work mu—ɛnyɛ boardroom slide mu.

Nea ɛma “frontline AI” di mu

Sɛ wopɛ sɛ AI boa wo adwumafie anaa agribusiness wɔ Ghana mu a, fa saa checklist yi:

1. Pain point baako: “Knowledge search” anaa “customer response” anaa “inventory forecasting.” Mfa everything mmra.
2. Content library: PDF, WhatsApp notes, manuals—fa kɔ baabi a wotumi “clean” na wosiesie.
3. Access: mobile-first. Field staff no nni laptop da biara.
4. Feedback loop: “Was this answer useful?” na fa yɛ training data.

Me stance: Sɛ frontline staff no nnye mfaso ntɛm a, project no bɛwu, sɛ ɛyɛ “AI strategy” no kɛse dɛn ara.

Digital twin ne R&D speed: sɛ wobɛyɛ adwuma ntɛm a, wopɛ simulation

Bayer kyerɛ “digital twin” project bi: replica a ɛte sɛ “millions of potential farming acres.” Wɔde no yɛ simulation ma pipeline products. Ɛboa wɔ asɛm baako mu: weather. Wɔka sɛ field testing wɔ real life mu deɛ, w’ani da “did it rain in July?” so. Digital twin no ma wotumi hu product performance wɔ environments a wunnyae anaa wunnsɔ hwɛe.

Bayer ka sɛ AI aboa wɔn atwa product delivery time so mfe 2 wɔ breeding cycles mu. Wɔde ML/deep learning ayɛ R&D “for a long time,” ansa na GenAI bɛba.

Ghana bridge: “digital twin” kɛse no, ketewa version wɔ hɔ

Ɛnyɛ sɛ Ghana bɛyɛ digital twin a ɛte sɛ Bayer ntɛm. Nanso “twin thinking” betumi abɔ wo ho ban:

• Scenario planning: Sɛ rain delay ba, sɛ fertilizer price kɔ soro, sɛ pest outbreak ba—dɛn na wobɛyɛ?
• Farm budgeting models: simple spreadsheets + AI assistant a ɔyɛ analysis
• Yield prediction: even with small data, wubetumi de forecasting simple models (plus good recordkeeping) ayɛ adwuma

Nea ɛhia ne sɛ, wopɛ consistent inputs (data) na wopɛ decision points a ɛwɔ season mu.

“Test-and-learn” a ɛnyɛ asɛm kɛkɛ: pilot a ɛwɔ discipline

Bayer annkɔ “launch to everyone” ntɛm. Wɔde 1,500 agronomists sɔɔ E.L.Y. hwɛ bɛyɛ afe mu. Saa scale yi ma pilot no yɛ “real.” Na McClerren kyerɛ sɛ wɔfaa iterative methodology: pilot, gather insights, tweak, deploy.

AI pilots a ɛyɛ den wɔ Ghana: sɛnea wobɛyɛ no yiye

Sɛ woyɛ SME, cooperative, anaa institution bi a wopɛ AI pilot a, fa rules yi:

• Define success with numbers: “Reduce time to answer farmer questions from 20 minutes to 5 minutes,” anaa “cut stockouts by 30%.”
• Pick owners: business owner + tech person. Sɛ ɔbaako pɛ na ɔde bɛyɛ a, ɛtɔ da biara.
• Guardrails: knowledge base a ɛyɛ approved, na answer biara nhyɛ “dangerous agronomy advice” a wɔnnhwɛ.
• Train users: 45 minutes training + cheat sheet. Nnipa mpɛn pii bɔ AI ho dawuro sɛ “self-explanatory,” nanso adwuma mu deɛ, ɛnte saa.

Nea “data culture” kyerɛ wɔ adwumafie ne nwomasua mu

McClerren kae sɛ agentic AI bɛma yɛ “reimagine the work”—ɛnyɛ automation pɛ. Me gye di, na ɛwɔ asɛnnibea ma Ghana sukuu ne adwumafie.

Wɔ adwumafie mu (SMEs, agribusiness, NGOs)

Data culture kyerɛ sɛ:

• w’employee biara nim nea ɛsɛ sɛ wɔkora (date, customer, issue, action)
• wopɛ “single source of truth” (even if it’s Google Sheets ansa)
• wode decisions to data: pricing, inventory, outreach schedules

Wɔ nwomasua mu (TVET, universities, training centers)

Sɛ yɛpɛ AI a ɛboa Ghana workforce a, sukuu ne training centers bɛtumi ayɛ “data culture” no fapem:

• teach students sɛnea wɔkyekye data, cleaning, documentation
• practical projects: build a small knowledge base for an agriculture extension topic
• ethics: what data is private? who owns farm data?

Me stance: AI literacy a enni data discipline no yɛ half education.

Practical roadmap: “12-year head start” no, wubetumi asiesie wɔnkyɛre

Bayer nya mfe 12 a, ɛyɛ nokorɛ. Nanso Ghana mu deɛ, wubetumi ayɛ compressed version wɔ 12-18 months mu sɛ wodi steps no so.

90 days: si fapem

• Choose one use case: farmer support, sales enablement, inventory forecasting
• Create a shared dataset template
• Collect baseline metrics (time, cost, errors)

6 months: build trust and reuse

• Build a small internal knowledge base in Twi/English mix, sɛnea staff kasa
• Put review workflow in place (approved answers)
• Start weekly reporting: what questions come most? what answers fail?

12 months: scale and integrate

• Connect to field data (mobile forms, simple sensors where possible)
• Add role-based access (sales, agronomy, ops)
• Expand to second use case only after first is stable

Snippet-worthy truth: AI doesn’t fail because it’s too advanced. It fails because the work around it stayed the same.

Nea wobɛyɛ next (na sɛnea ɛbɛboa lead goal no)

Sɛ wokɔgye Bayer story no mu a, message no yɛ clear: AI a ɛyɛ adwuma fi data culture ne frontline value mu. Ghana akuafoɔ ne agribusiness betumi anya mfaso kɛse, especially bere yi a climate variability, input costs, ne market volatility reyɛ den wɔ West Africa.

Sɛ wo yɛ cooperative leader, agribusiness manager, anaa training coordinator a, fa adwene yi bɔ mu: pain point bɛn na wo staff spends hours on every week? Sɛ wutumi kyerɛ saa adeɛ no pɔtee a, wo AI project no bɛnya kwan.

Sɛ Ghana bɛyɛ “12-year AI strategy” a ɛsi akuafoɔ so a, ebia nsɛm bɛn na ɛsɛ sɛ yɛhyɛ aseɛ seesei—data standard, training, anaa field support tools?