How can I guarantee that every electronic assembly or fabricated part produced is free of defects, blemishes, or lack of conformance right after it is produced and upstream in the manufacturing process?
What if I could forecast a prospect’s proclivity to buy my products or services based not just on their stated intent, but on their facial expressions in a video conference call?
What if I could consistently transform any paper document—regardless of format—to its digital twin?
What if I could quickly predict the construction, materials and services cost (or revenue) potential from numerous images or video of a natural disaster like a hurricane, earthquake, wildfire or flood?
The business impact of these types of questions is huge but can easily elude even the most talented analytics teams. What’s missing? Visual Intelligence, the ability to combine analytics with images and intent; historically, a time-consuming and costly endeavor.
Visual Intelligence requires the ability to extract, analyze, and derive critical details from time-sensitive unstructured data in visuals, images, facial recognition systems, multifaceted industry-specific contracts, trade documents, agreements and more. And you must be able to process the analytics at massive scale and in a timeframe that supports your business objective.
With recent advances in technologies such as deep learning, including Natural Language Processing (NLP), Conditional Random Fields (CRF), Bidirectional Encoder Representations from Transformers
(BERT) and others, you can now ingest and quickly process key data to make very accurate assumptions. As a matter of fact, the U.S. deep learning market has the potential to grow by US $7.2 billion through 2024. Industry analyst firm Gartner recently reported that the use of artificial intelligence (of which deep learning is a specific category) among enterprises tripled in the past year, with 37 percent of organizations reporting that they use it.
Roadblocks to Visual Intelligence
If you’re like most organizations, you have probably investigated and in some cases implemented variations of artificial intelligence or machine learning. But with the pace of business change and the issues we’re facing due to the COVID-19 pandemic, can you recognize issues that impede sales commits or supply chain movement? For instance, can you derive and present an offer to convert a lost sale? Or deliver an accurate and correct tracking response in real-time? How long is the time between receiving demand documents and billing those orders?
If you take a look at the most common document processes, bottlenecks occur because one of the following issues:
- Can’t capture and process unstructured data from non-uniform forms or formats
- Can’t easily capture data from all the systems normally engaged in the effort
- Current systems can’t correctly understand nor interpret complex data, images or text, particularly in specific industries or use cases
- Current systems can’t scale to process the massive amounts of data required
Beside functional constraints, process impediments can be cost- or risk-based: if you have an internal team and approach this as a custom project, what’s the cost in resources and time to build a solution from scratch? Would it be delivered in time to get the results you’re looking for? Would it scale? How well-supported would that work be if the team departed your organization? These considerations cause most of us to pause.
Where Visual Intelligence Can Impact Business Agility
The value of Visual Intelligence can greatly exceed the challenges you may face to implement it. Today with cloud platform provider engineered solutions, Visual Intelligence is far less expensive than historical norms while also extremely scalable, robust and extensible. This cost per value is key because deriving the most suitable level of data ensures a much higher degree of business success. For example, manufacturing processes that detect product defects (like a scratch or dent) in real time reduce material costs, cycle times and labor while increasing throughput. Likewise, merchandisers that can assess structured and unstructured data with social sentiment can model demand transference, including the effects of price and promotion for on-demand merchandise-to-store allocations and replenishments.
Visual Intelligence can help a number of industries:
- Retail: Upload an image and identify and classify products in real-time, integrate social sentiment and facial recognition to keep customers engaged at the point of sale. Derive availability, alternates and shipment lead-times based on intent and images.
- Manufacturing & Supply Chain: Process images in real time to identify and classify products and defects at point of cause. Identify counterfeit products, errors and spurious information in shipping documents, all in real-time.
- Logistics/Transportation: Reduce time between receipt of request for shipment and the order being billed through automated data extraction, classification and validation. Easily derive ETD and ETA from shipping instructions, waybills, receipts and orders through extraction and data enrichment.
- Financial Services: Accelerate document processing by extracting data from customer documents including handwritten documents much faster than human workers doing the same task.
Take an Innovative Approach
Visual Intelligence can transform your organization by accelerating your speed to insights. By combining intuitive visualization, AI, machine learning and conversational intent, Visual Intelligence can—with the help of knowledgeable subject matter experts— transform complex data to high-quality insights needed to make informed and critical business decisions.
Think about where you are in your digital transformation. Consider these scenarios:
- Do you have the skills inhouse to adopt AI and machine learning techniques and best practices?
- Are you capturing intent data?
- Do you have deep expertise in cloud analytics specific to your industry and business or do these analytics need to be created?
Answers to questions like these are critical to assess before embarking on a plan for Visual Intelligence. Ensuring these capabilities are in place will help you achieve Visual Intelligence in a timeframe that can deliver the results you need. However, if you don’t have these capabilities, or need to achieve Visual Intelligence quickly before adding them, there are options. Consider outside services that can help you plan and set up your team for success. These organizations can also design the analytics you need, or help you set up a “Test and Learn” environment to deliver insights on recommended actions.
Banking on Real Results
Visual Intelligence is in play today in a number of enterprises. These companies have adopted specific solutions for departmental or operational improvements. Typically, Visual Intelligence is implemented in areas where there is a large amount of images or unstructured data, or audio and video data. For instance,
- A global freight forwarder is able to eliminate the manual and time-consuming process of transcribing thousands of bills of lading each night with a Vision Machine Learning (ML) system that is accurate, highly-elastic, and fault-tolerant.
- A major door manufacturer enables cameras and Vision ML to detect any variety of defects (dents, scratches, glue) or mishaps (wrong color, window or trim) while in process. The toolset has direct impact on reducing warranty and returns cost and improving the quality of their manufacturing processes.
- A leading international internet and digital media company established a new data platform that has increased revenue through greater customer personalization enabled by 800+ machine learning models.
These are just a few examples. Want to learn more about Visual Intelligence for your business? Talk to our experts about what you’d like to do.