TRANSFORMING THE FUTURE: STRATEGIC AI ADOPTION FOR SMALL FOOD & BEVERAGE BUSINESSES
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,003 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».