How AI can be used to streamline marketing workflows
It wasn’t too long ago that artificial intelligence (AI) technology was merely a concept from science fiction movies. But today, AI is changing the status quo of entire industries, including marketing technology (martech).
When it comes to leveraging AI capabilities in martech tools, it can range from chatbots to personalised content to social media management. So how can AI be used to streamline digital asset management (DAM) workflows?
Image recognition functionality
DAM systems help teams organise, find, distribute, and analyse digital content. In order to be effective, DAM software relies on metadata, or descriptive information about each piece of content, to provide structure and information to make digital assets findable. Metadata is crucial to a system’s success but can often be time consuming to add and is prone to error without a clear process.
Metadata fields are used to answer different questions about each asset including identifiers like: filename, photography type, description, and usage rights. A system of metadata fields is called a metadata schema. Creating a schema with fields relevant to a specific company improves the search experience and helps ensure that metadata is added accurately.
With the help of image recognition software, AI has a valuable and growing role in system management through its ability to automatically tag assets with relevant metadata during the upload process.
Employing AI to automate part of the image-tagging process can improve categorisation, power the accuracy of related assets, and offer advanced searching options for users. Not to mention, saving hours of manual tagging efforts.
The ability to automatically categorise and tag images extends to a range of scenarios. It can recognise faces or demographics — including the age, ethnicity, or gender of persons in the image. It can also apply keywords for specific industries, such as travel, food, and apparel.
Further, image recognition software includes security features that allow DAM administrators to:
- Moderate assets and automatically delete or hide uploads with inappropriate content
- Enable auto-tagging functionality for specific upload profiles or metadata fields
- Influence how the controls for strength, relevance, and variety are configured
Together, these flexible capabilities and features can accommodate unique content needs and business goals.
Benefits of using image recognition software
Adding the power of image recognition to a DAM system makes metadata creation simpler, faster, and most importantly, better. Image recognition software can reduce human errors and inconsistencies, and avoid assets being uploaded into the DAM system without any metadata.
Further, AI can make searching more effective as thorough metadata allows search tools to return accurate results, quickly.
Automating a manual process reduces the time needed to tag assets, meaning workflows can be better streamlined. All of these efficiencies translate to business cost savings. After all, less time spent tagging and searching means more time for creative and strategic work.
What does the future hold?
Although technology is helping all kinds of companies work with greater efficiency and speed, it is also clear that humans are still needed. Technology alone is not able to fix an organisation’s problems. A strategy must be in place to align the workforce, processes, and technology — such as AI — in order to achieve the desired marketing outcomes.
Tools like image recognition require a balance between automation and human touch. While it can free up time and resources for other value-driven projects, the workforce is still needed to inform and guide both the technology and its users.
In 2021, we will see the partnership between AI and the human workforce grow more important to marketing teams. Not only to streamline and scale DAM workflows but to successfully create cost savings, too.
By Jake Athey, VP Marketing and Customer Experience at Widen
Google launches Visual Inspection AI tool for manufacturers
Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped.
Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale.
How does it work?
The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.
Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI.
- Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor.
- Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps.
- Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers.
- Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
- Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.
Industry use cases
The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing.
Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”
Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture.