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TRANSFORMING THE FUTURE: STRATEGIC AI ADOPTION FOR SMALL FOOD & BEVERAGE BUSINESSES

2025· other· en· W6987691984 sur OpenAlexaboutno aff

Notice bibliographique

RevueOCAD University Open Research Repository (OCAD University) · 2025
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGovernment (linguistics)ProductivitySmall businessProduction (economics)Transformative learningResilience (materials science)Service (business)Customer engagement
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Small businesses witness their evolution and resilience in 2025 through technology, which serves as a catalyst for both innovation and sustainable growth, as well as adaptability. Artificial Intelligence (AI) emerges as a standout transformative tool because of its capacity to revolutionize business operations while enhancing customer engagement and productivity levels. Canadian small businesses in the Food & Beverage sector have not widely adopted AI, even though its potential looks promising. Research shows that 30.1% of businesses see AI as a means to improve efficiency according to McKinsey’s 2025 (Economic Potential of Generative AI | McKinsey, 2025), report but a mere 7.5% of Canadian companies actually implement AI in their production processes according to the 2024 S. C. Government of Canada report (S. C. Government of Canada, 2024) only 7.5% of Canadian companies use AI for production processes. Information and cultural industries exhibit the highest AI adoption rate at 20.9% while professional services stand at 13.7% and finance at 10.9%, but accommodation and food service industries show only a 0.9% adoption rate because small businesses within this sector face implementation difficulties (S. C. Government of Canada, 2024). The 2024 survey and 2025 study by Edelman Mexico and Microsoft collected responses from Canadian small business leaders who have between one to 250 employees regarding their leading challenges and opportunities connected to AI adoption. The 2025 Edelman Mexico and Microsoft survey found that 78% of Canadian small business leaders with 1–250 employees are considering AI implementation while 65% are promoting AI tool adoption among their staff. Even as interest in AI grows among businesses, only 2% plan to expand their AI investment next year due to ethical concerns and cybersecurity risks along with difficulties in upskilling and unclear AI implementation processes. Yet, the potential gains are clear. Businesses testing AI solutions have reported increases in productivity and customer satisfaction as well as better work quality and employee engagement, achieving an average productivity improvement of 31% (New Study Reveals Canada’s SMBs Are Turning AI Curiosity into AI Action – Microsoft News Center Canada, 2024). To bridge this adoption gap, this Major Research Project (MRP) explores the systemic challenges small businesses face in the Food & Beverage industry. It offers an iterative, design-led roadmap for responsible and scalable AI adoption. Guided by the Double Diamond Framework, a structured design-thinking methodology that alternates between divergent exploration (expanding research and exploration) and convergent decision-making (narrowing down findings and solutions). This approach ensures a holistic and iterative process, allowing for the identification of real-world barriers and the development of scalable AI adoption ideas. By analyzing emerging AI industry-specific constraints and policy frameworks, this research offers practical, evidence-based insights to accelerate AI adoption in the chosen niche sector. To ground the research in lived experience, fieldwork was conducted using the Technology Acceptance Model (TAM) across nine small businesses, revealing significant barriers to AI adoption, including unclear value propositions, digital skill gaps, and cultural resistance to change. These findings highlight the urgent need to address foundational challenges before AI implementation efforts can succeed at scale. The outcome of this research is an AI adoption playbook known as “Biz Guide” which was developed using AI tools to function as a hands-on, strategic toolkit that supports small businesses throughout their AI transformation journey. The practical resource integrates case studies, sector-specific frameworks, curated tools, ethical checklists to support data-driven decisions and uphold human-centred values including artisanal quality and sustainability. Small business owners and industry stakeholders together with policymakers and technology providers will find this study full of vital insights. This study delivers actionable strategies which assist small businesses to bridge digital gaps and integrate AI inclusively for Canadian business success in an evolving digital marketplace.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Science ouverte, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,941
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,005
Études des sciences et des technologies0,0040,001
Communication savante0,0010,001
Science ouverte0,0060,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,073
Tête enseignante GPT0,296
Écart entre enseignants0,223 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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