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“AI-driven personalisation is estimated to drive a 15% increase in commerce revenue by 2025. Yet, many businesses are just beginning to unlock its potential.”
The document “Come introdurre l’AI in azienda,” produced by Harvard Business Review Italy for Google Italy, underscores the transformative power of Artificial Intelligence (AI) in modern enterprises.
It provides a thorough overview of AI’s current adoption trends, its potential to reshape industries, and strategic guidance for implementation. At Alpenite, we resonate deeply with these insights, especially in the context of commerce, where AI offers unparalleled opportunities to innovate and scale.
AI’s pervasive influence in commerce
AI has become a universal enabler, transforming traditional business models into more agile, data-driven ecosystems. The surge in global adoption—from 50% to 72% within a year—demonstrates this technology’s undeniable momentum.
Generative AI, in particular, is no longer a fringe experiment but a strategic investment priority for 70% of CEOs. However, these advancements come with challenges, as approximately 70% of early AI projects failed to deliver significant ROI.
Alpenite’s take on commerce
In the commerce domain, success with AI is about leveraging the technology to address specific challenges such as personalised customer experiences, efficient inventory management, and seamless cross-border operations. Our expertise lies in integrating AI solutions tailored to these commercial needs, ensuring measurable business outcomes.
The 10 strategic opportunities for commerce
The report outlines 10 priorities for embedding AI into organisations. Here, we analyse them through the lens of commerce:
1. Define a visionary strategy: commerce leaders must align AI initiatives with broader business goals, such as increasing customer lifetime value or streamlining supply chains. AI models can go beyond basic trend analysis, using advanced predictive algorithms to foresee shifts in consumer demand and refine strategies dynamically, enhancing competitive edge. For example, AI-driven market segmentation can help target niche audiences with precision.
2. Use a gradual approach: commerce businesses should adopt modular AI solutions, such as demand forecasting models or visual search features for e-commerce. Iterative implementation allows teams to evaluate performance and scalability while minimizing disruption to existing workflows. Starting with smaller-scale pilots reduces risk and builds confidence.
3. Prioritise quality data: AI-driven commerce thrives on high-quality, integrated data pipelines. Advanced techniques like data lakes (centralized repositories for storing raw data), real-time data streaming (processing and analysing data as it’s generated), and machine learning (ML) preprocessing can refine inputs for precise customer profiling and hyper-personalised engagement strategies.
4. Integrate and strengthen existing technologies: commerce platforms can harness AI to unify and elevate existing systems. Advanced integrations, such as connecting AI-driven recommendation engines with Augmented Reality (AR) interfaces, enhance the customer experience by making it immersive and interactive.
5. Establish robust governance: AI introduces risks such as algorithmic bias and misuse of consumer data. Establishing ethical AI frameworks ensures transparency and accountability, critical in retaining consumer trust in industries that rely heavily on repeat purchases and brand loyalty. Ethical AI frameworks not only ensure compliance but also foster long-term consumer trust by transparently communicating how data is collected, stored, and used.
6. Foster collective intelligence: commerce thrives on cross-departmental synergies. AI tools can create seamless integrations—for instance, linking supply chain data with marketing campaigns to optimise stock promotions and minimise waste, fostering greater organisational cohesion.
7. Develop agile product focus: by using AI, commerce businesses can accelerate product innovation cycles. AI can analyse consumer sentiment, detect trends across global markets, and facilitate rapid prototyping of new offerings tailored to emerging needs. AI-driven automation can further streamline product development workflows.
8. Address challenges proactively: beyond integrating into legacy systems, businesses must prioritise resilience. AI-powered simulations can model scenarios for supply chain disruptions or shifts in consumer behavior, enabling organisations to preemptively adapt strategies and mitigate risks.
9. Strengthen digital culture and skills: upskilling remains pivotal. Commerce-specific AI applications such as automated loyalty programs, sales forecasting tools, and sentiment analysis require teams equipped to deploy and optimise these solutions. Comprehensive training ensures employees can collaborate effectively with AI systems.
10. Mitigate cybersecurity concerns: in commerce, trust is paramount. Advanced AI systems cannot only detect but also predict and neutralise threats like payment fraud, ensuring secure and frictionless customer experiences. Enhanced machine learning algorithms improve fraud detection accuracy, reducing false positives.
Challenges and opportunities ahead in commerce
Adopting AI in commerce presents unique challenges, including technical complexities and ethical concerns around data usage. However, AI’s transformative potential is unrivalled. Companies must address integration challenges through modular, scalable solutions while continuously aligning their AI efforts with emerging regulations and consumer expectations.
Additionally, AI can play a critical role in enhancing supply chain transparency by monitoring logistics in real-time, predicting potential delays, and ensuring inventory accuracy.
AI is also pivotal in driving sustainability initiatives within commerce.
By optimising energy usage in warehouses, reducing waste in inventory management, and enabling ethical sourcing through advanced data analytics, businesses can meet the growing consumer demand for environmentally responsible practices.
With AI, businesses can also create closed-loop supply chains, enabling them to reuse and recycle materials more efficiently, aligning with circular economy principles.
Another frontier is the increasing sophistication of customer behavior analytics. AI can process real-time behavior data across channels, offering actionable insights for a cohesive omnichannel strategy. These tools allow businesses to adapt dynamically, tailoring their approach to meet evolving consumer preferences.
The emergence of predictive and prescriptive analytics further empowers commerce leaders. While predictive models anticipate future trends, prescriptive analytics provides clear recommendations on optimal actions, transforming data into a proactive business strategy.
Conclusion: Alpenite’s vision for commerce
At Alpenite, we view AI as a game-changer in the commerce industry. Our approach combines technical expertise with strategic insights, enabling businesses to harness AI’s full potential. By fostering innovation in areas like predictive insights, seamless global operations, and personalised customer journeys, we transform commerce into a smarter, more adaptive ecosystem that anticipates and exceeds the evolving needs of consumers and businesses alike.
Through scalable strategies, ethical AI practices, and a commitment to fostering digital excellence, we empower organisations to lead in an AI-driven commerce landscape. For businesses ready to take the leap, Alpenite offers a proven roadmap for AI integration, tailored to the unique challenges and opportunities in commerce.
Let’s innovate together.
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