Jan 31, 2024
4 min to read
Discover the nuanced challenges in content generation and recommendation engines as we highlight the importance of quality product information inputs and diverse data to enhance user experiences. Plus, take a glimpse into the future of product experiences with Akeneo and Unifai, and how AI technology promises to shape and impact the way that we work!
Once confined to the speculative world of science fiction, Artificial Intelligence (AI) has officially permeated our everyday lives and entrenched itself into our reality with the rise in popularity of tools like OpenAI’s ChatGPT, Bing Copilot, and Google’s Bard.
Yet if you’ve interacted with these tools at all over the past year, you’ll have noticed a few things: one, while these solutions are an exciting step forward, they are not quite as autonomous or advanced as many people try to claim. Few people, even self-reported AI experts, seem to actually understand the capabilities of this technology today and how it will evolve to affect the customer experience in the coming years.
Two, the quality of the outputs that you receive from these technologies is heavily dependent on the quality of prompt and information you provide from the outset, particularly when it comes to content generation.
The recent strides in AI, particularly with content generation, have been remarkable, yet the crux of the matter is identifying the right attributes, data, and values essential for crafting meaningful inputs. Or, more colloquially speaking, it’s a “shit-in, shit-out” situation.
This paradox underscores the complex nature of AI and its ongoing journey from concept to seamless integration into our everyday lives, and highlights one very important caveat: as exciting as AI technology is, it will always fail if you don’t have the right attributes, the right data and the right values available.
AI algorithms, particularly those powering recommendation engines or content generation, analyze product data to understand customer preferences. Accurate information ensures that recommendations align with individual tastes and needs, leading to more personalized and relevant suggestions.
Take, for instance, the fashion industry. AI-driven tools can revolutionize the customer experience by providing personalized recommendations based on individual style preferences, such as suggesting a certain skirt that is frequently purchased with a top. However, the effectiveness of these recommendations hinges on the quality and specificity of the product information fed into the system.
For product experience, the future of AI is really going to focus on infusing intelligence everywhere in fields of work of product experience professionals. They have very tedious jobs, a lot of manual tasks that they have to do repeatedly. So we're going to automate those tasks for them and {bring value} across the chain.
Enhanced input diversity is a crucial aspect of advancing artificial intelligence capabilities, and it encompasses various dimensions for a comprehensive understanding of user preferences. One facet is textual data enrichment, where high-quality product information includes detailed descriptions, customer reviews, and specifications. An analysis of diverse textual data, such as customer reviews praising safety features or product compatibility, enables nuanced recommendations that reflect user sentiments.
Visual and auditory input integrations are equally essential, though is often considered as the more underdeveloped area of AI. Diverse data, including images, videos, and sounds, enhances AI models’ capabilities to understand and cater to customer preferences.
Moving towards comprehensive results, the amalgamation of traditional machine learning (ML) with deep learning models is a powerful strategy. Researchers are already combining these models to create hybrid systems that leverage the strengths of both. Traditional ML models excel in handling structured data, while deep learning models are adept at processing unstructured data like images and text. A hybrid model of the two can analyze both structured data like technical specifications and unstructured data like customer reviews and product images, resulting in a more nuanced understanding of a product’s appeal.
The seamless integration of AI technologies into everyday workflows is pivotal for optimizing operational efficiency and user experience. To achieve this, a user-centric design approach is crucial, ensuring that the integration minimizes disruptions for teams working on a daily basis. This involves creating intuitive user interfaces that allow team members to interact with AI tools effortlessly, promoting accessibility without the need for extensive training.
Invisible automation also plays a key role in enhancing productivity by automating repetitive tasks without constant manual intervention; these are applications of AI that your team doesn’t always even need to be aware of. Automated workflows, such as inventory management systems automatically restocking low inventory levels, contribute to operational efficiency in sectors like retail. Additionally, predictive maintenance powered by AI anticipates potential issues and addresses them proactively, reducing downtime and ensuring smooth manufacturing processes.
Transparency in AI decision-making is equally essential for fostering trust among team members. AI systems should be designed to be explainable, enabling users to understand the reasoning behind the decisions made. Clear communication about the role of AI in daily workflows is paramount, ensuring that team members are informed about how AI contributes to their tasks and the overall organizational goals. This transparency is particularly crucial in scenarios like customer service, where the use of AI to prioritize and route queries requires clear communication to build confidence in the decision-making processes.
Akeneo has always invested a lot into the AI and ML space very early on because we know that it's going to have an impact, but we also know that the impact will be solely tied to our ability to embed those processes into the everyday workforce.
At Akeneo, we’re not merely following the typical AI playbook; we’re rewriting it. Collaborating with our recently acquired partner, Unifai, we’re driven by a willingness to leverage AI and apply it to diverse needs. Our commitment to innovation goes beyond the ordinary – we strive to think outside the box and explore new applications.
As the world’s first intelligent Product Cloud, we can provide your team with access not only to a centralized source of truth for all your product information and an extensive network of expert partners, but also to product data that is cleansed, enhanced, and enriched with native AI capabilities.
Looking ahead, we envision expanding the applications of AI into areas such as retail activation and content generation. However, we are a customer-centric company! Our roadmap is shaped by the feedback and needs of our customers; our goal is to bring significant improvements to our customers through our technology, prioritizing solutions that truly matter to their businesses.
I think when you join forces with another company, you know that it's going to be for a long time, not just a couple of years. And if you want to make it really successful you need to make sure that it’s not only about a good technology fit; I really believe that Akeneo and Unifai are sharing some very essential values like a spirit of collaboration, innovation, humility, openness, all those values are critical for the success of partners for a long time.
In case you missed it, Akeneo announced a few months ago that Unifai has joined forces with us, the Product Experience company! If you want to know more about the partnership with Unifai, what’s to come next from the world’s first intelligent Product Cloud, or how to take the next steps in the development of your PX Strategy, you can register for the premiere PX event, Unlock 2024.
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