Be aware of the practical side of AI implementations

Overcoming the Hype

Accessibility of useful data, heavier computing power and advanced algorithms have in recent years resulted in companies looking at AI to help drive efficiency, cut costs, increase revenues, automate routine tasks, improve employee experiences and understand their customers better. More and more use cases were also being conceptualized for strategy and innovation by a large number of corporates globally.

Major investments were made in the past across multiple industries to try and utilize data and AI strategically. However, several companies who initially scrambled to implement AI in their organizations did not eventually get to enjoy the benefits they had envisaged at the start. This lead to fears, somewhat justifiably, that AI is over-hyped. Of course it was over-hyped then.

AI-Ups and Downs

AI went though many ups and downs. It took several case studies and analysis of companies, people and systems deployed in AI projects to understand that the problem in most implementations was a basic lack of understanding of what AI is capable of, how to practically leverage data, and what the dangers are that need to be side-stepped to ensure real value for the business.

The fact that AI was hyped and why it was so has now created an appreciation of the need to implement it properly, but also a fresh awareness of its huge potential and how it can be a real value driver if done properly. Businesses today widely accept the fact that they must adopt AI strategies in order to compete. But they also need to beware of the pitfalls in going ahead without a clear cut plan or process.

How you Approach AI is the key

AI has proven its value across various sectors in multiple industries. While more and more use cases are being addressed by AI, an Accenture survey in 2021 revealed that most organizations are barely scratching the surface.

Even before the pandemic hit it was evident that many businesses were achieving significant RoI by rolling out AI beyond the pilot stage. During the last couple of years AI transformation became the very means of survival for many, whereas for others it became a catalyst for growth.

But what was the secret to these superior performances that have set new standards in AI implementation?

What set them apart is how these companies approached AI. They did not just adopt the Cloud and start using AI and data solutions. It is more about how they put a lot of thought into it, analysed the organization and the business environment and the pitfalls, and strategized before they actually tapped into any of the widely available AI technologies. The successful AI achievers were those that put people and systems first(digital transformation strategy), and technology later, and who looked at AI as a differentiator to stay ahead of the game.

The Strategy

First, do a holistic research and determine what AI can and cannot do for you. What exactly do you want to achieve? Is it to solve a problem, or to tap an opportunity, or get new insights, or is it for a total reinvention of your business? How and where to start, and what are the difficulties? Pick the brain of a data scientist if you can associate with one.

Draw up a list of potential use cases and a roadmap including a long term vision. Priority could be the low hanging fruits, the easy wins. Most importantly, understand what type of data is available and where, and what are the additional types of information that needs to be tracked to implement the chosen use cases.

The Team

You may not have all the required skills in-house to implement AI. Building a team or finding an AI partner with the desired capabilities is another important step. In fact, building the right team with the same mindset and personal skills to work with the rest of the organization can be the most critical factor in the long run. Businesses need to understand that this is not an isolated project but an integration of a new and soon-to-be-scaled AI platform into an existing IT eco-system.

The quick MVP and Roll-out

Develop the solution to your first business problem quickly, ideally within 2 to 3 months. This MVP (Minimum Viable Product) should have enough functionality, value and benefit for the user to sustain its usage and also a feedback loop to enable further development. Engage an expert who is savvy both technically and in the business sense. Sit with him and define the key performance parameters over the initial roadmap period. Review these regularly and modify them if necessary down the line.

After the MVP has demonstrated its value and been evaluated, prepare to rollout on a broader scale. Utilize your AI partner’s expertise and experience to keep in perspective both the technical and business factors during the implementation stage.

Addressing some typical problems

Organizations heading for AI implementations must give due consideration to these important issues they will face for sure.

Reluctance to change – This is an age-old management problem, but for AI implementation it refers to changing your decision making process into a data-driven one and not just based on instincts. To overcome this, some visible and measurable benefits have to be revealed early. That is where a quick MVP in a particularly relevant use case will help.

Lack of involvement – The users need to feel they are part of the solution and not being forced to adopt something that is being imposed on them. The environment has to be such that they are well taught and feel themselves to be joint architects of the project. Set realistic expectation levels and a roadmap for achieving results. Support of key management personnel and stakeholders are also critical for creating the right environment.

Quality and availability of data –No doubt that clean data is a key factor. So, centralized monitoring and control to obtain incoming data in a standardized format is absolutely necessary. There is also the challenge to locate and keep track of the right data and how and where it is stored. Do not go for complex machine learning models and stick to simpler solutions, if you do not have enough good data or sufficient labeled data that can be used to train the system.

Skillsets and Infra for implementing AI – It may be less challenging and cheaper to outsource than form a huge in-house team. In any case you need to do an evaluation study of what’s available internally and also define the costs going forward, keeping scalability also in mind.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions.  Cogniphi’s Vision Intelligence platform AI Vision integrates Computer Vision, Machine Learning, and AI to extract precise and meaningful data from visual footage. These are further converted into actionable insights and notifications.


How AI Vision is Changing Public Safety for the Better

As we build a better society, one of the most important measures of progress is public safety. Companies around the world are designing innovative technologies to detect and prevent crimes. With AI, it has now become easier to leverage historical data to identify patterns and predict locations where crimes are most likely to happen in order to focus all energies into making the most vulnerable areas safe.

Moreover, with technologies like facial recognition and validation, it has become possible to solve crimes with reduced time and effort. More and more improvements in law enforcement can be seen with the implementation of data-based technologies, especially Artificial Intelligence.

But what’s in store for the future of law enforcement?

Vision Intelligence For Public Safety

Vision intelligence has highly practical applications in surveillance and public safety. With the ability to detect patterns in visual feed – images or videos, it becomes easier to track and identify items, patterns, people, and behaviours to trigger an alarm or warn the responsible security personnel in real-time of impending threats.

Some of the vision intelligence applications that are most useful in public safety are:

1. Facial Recognition and Validation

Facial recognition is currently a relatively commonplace technology where most smartphones are now locked/unlocked with it. However, it’s application in surveillance and public safety is more profound, and paramount. With AI-based visual intelligence, facial recognition can not only identify people behind a disguise but also read emotions and expressions to predict suspicious behaviour if and when a person is intending to commit a crime.
Moreover, with facial validation, it becomes possible to detect and prevent bypassing of facial recognition through identity theft or deep fakes.


AI Vision

 2. Behavior Detection

When trained to identify a set pattern of behaviour that people are required to follow, an AI-based camera can detect anomalies from the set pattern and provide alerts or notifications. This can be implemented in controlled public places such as prisons, mental asylums, etc. where a set pattern of behaviour is expected from individuals and any anomaly can be a sign of trouble.

AI Vision

3. Skeletal Construction

Certain movements can be identified in individuals that indicate criminal intent. In surveillance, with video cameras ingrained with vision intelligence, one can identify if there’s a fight that breaks out in an alleyway and whether it’s just a quarrel or it has serious repercussions. This insight could potentially enable the surveillance to make the right decision at the right time to prevent escalation of the situation and possibly prevent a crime from happening.

AI Vision

In addition to all this, the applications of vision intelligence are becoming more pronounced and imperative as we advance in technology and operations.

We, at Cogniphi, are building effective vision intelligence solutions that are outcome-driven and significantly improves the existing cameras in an area to drive insights and ensure improved surveillance and safety.

About Cogniphi

Cogniphi is a technology company that focuses on building next-generation vision intelligence solutions that are outcome-driven and seamlessly integrates into the existing infrastructure. Cogniphi’s AI Vision is a platform that’s built to improve operational efficiency at every level of an organization, across industries and sectors.

If you’re wondering how Cogniphi’s AI Vision can help you transform your business, get in touch with us for a free demo!

Why AI Vision is Crucial in Quality Inspection

The Fourth Industrial Revolution or Industry 4.0 is here – it’s blurring the lines between the physical, digital, and biological worlds. With a fusion of progressive technology like AI, robotics, the Internet of Things, 3D printing, and more, it’s changing the way the world functions.

We are witnessing the inception of full automation across sectors – manufacturing, retail, pharma, medicine, and more. Data has become instrumental to making this happen and Artificial Intelligence is what’s driving it. As we progress into a more capable, more exciting future, it’s imperative that the quality of production, in all sectors, becomes better and better – it’s called progress for a reason.

AI has been providing the inputs for digital transformation but without the capability to understand context. The AI cameras currently used by most businesses require a human to look at the footage to add context in the detection and have a very low accuracy which makes them ineffective. Without context, there’s little value that these cameras can provide.

The machines that businesses and organizations have setup into their premises to monitor data, especially cameras, only look at the 1s and 0s of the data that’s presented to them – they lack the clarity of context that denies them the opportunity to be precise, accurate, and error-free.

And that’s about to change.

In the last few years, we’ve taken huge leaps in vision intelligence. This prominent technology enables cameras to look at visual feed as a whole – complete in its context. This empowers them to identify, detect, or distinguish between objects with better clarity, precision, and accuracy.

Current advantages of vision intelligence in quality control

With the recent advancement in vision intelligence, we have already seen the benefits in quality control like:

  1. identifying damaged/faulty goods during production
  2. enforcing PPE for workers
  3. checking on vacant shelves in supermarkets
  4. Identifying and eliminating repetitive tasks

Why AI Vision will become crucial for quality inspection

Quality control is fast becoming automated in a lot of companies across sectors.

Such improvements in the process not only improves the productivity of a process but also leads to more efficient production leading to better returns for businesses with a higher production at reduced costs.

For instance, a camera overlooking a production line must have a person sitting behind it, ensuring that no product is damaged or defective. Now, this person could easily miss a few defective products owing to fatigue which could affect the later production. With an AI-enabled camera, the defective products can be instantly and consistently flagged and pulled out of the production line, saving wastage any further in the process.

These intelligent cameras can not only detect imperfections but also enable geometric inspection, packaging control, product classification, and more.

AI Vision

AI Vision

AI Vision

AI Vision

Some of the undeniable advantages of vision intelligence in quality inspection include:

  1. Being precise and accurate in reporting and flagging inefficiencies
  2. Accelerating the production speed through seamless flow of repetitive tasks
  3. Reducing the downtime of a process by eliminating breaks or rest
  4. Lowering the operational costs by cutting down on manual labor and saving on wastage

Cogniphi is accelerating the future of vision intelligence with Cogniphi’s AI Vision by enabling leaders in the manufacturing with specific cognitive intelligence to solve problems. We take a very objective approach to solving business problems – analyzing the challenge to find out the reasons behind any inefficiencies and reaching the root of the cause and solving it from within to ensure that the return on investment is profound.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. It believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.


What Is AI Vision and Why Is It Here to Stay?

Artificial Intelligence is one of the most misunderstood and overused buzzwords of our generation. We hear so many organizations bragging about their use of AI without really understanding what they are doing with it. In this blog post, our experts share how Artificial Intelligence is used in Cameras to improve business operations. Also, learn why it will become the future of how we do business.

Computer Vision is the application of Artificial Intelligence (“AI”) through a visual medium, like a live camera feed or an image. It essentially aims to replicate human perception and visual cognition. You must have already encountered computer vision without even realizing it, for example, the face-lock on your phone screen, to some level, is a simple application of computer vision or the Instagram or Snapchat filters that alter your hairstyle or turn your face into an animal on the screen, are all made possible with Computer Vision.

Any device that can comprehend a visual feed like a human being is a computer vision system, however, Computers have the ability to process more than humans can. Think about yourself reading these words. It’s likely that you’re focusing on one word at a time while you are reading, and recognizing the words around these words without fully processing them. Using Computer Vision, a computer would be able to see and read all the words on the page at the same time, drawing immediate meaning from the entire page

This principle also applies in a CCTV control room. Normally you would have people monitoring the live camera feeds on multiple screens, but how many live feeds can one person accurately monitor at a time? That’s where Computer Vision shines. It’s able to continuously and accurately monitor the feeds from all cameras simultaneously.

Automation integrated with AI Vision enables the workforce to overcome typical challenges of humans, like fatigue. It provides unseen insights that humans find difficult to access or comprehend in real-time. While human perception has its own share of limitations, a camera can gather every second of visual data from your premises that can be further analyzed to identify areas typically missed by humans.

Computer vision is the most natural next step in machine evolution. The purpose of AI is not to replace humans but to assist them. For that to happen, it needs to command similar cognitive abilities with enhanced capabilities. However, the underlying science behind AI Vision is not as complex or overwhelming as it might appear. Here’s a brief understanding of how AI Vision functions:

Capture Metadata from Camera Feeds

Content feed from a camera is analyzed by trained models to recognize objects, their actions, specific characteristics, and interactions in space and time. Typically, in a retail store, a camera equipped with Cogniphi’s AI Vision can detect shoppers, analyze footfall, recognize customers with face recognition, and evaluate customer expressions like anger with emotion recognition. Similarly in manufacturing, AI Vision can do myriad tasks such as identifying quality for inspection, counting warehouse inventory, etc.

Pattern Recognition and Anomaly Detection

Computer Vision derives relevant insights from unstructured data through contexts and occurrences of patterns along with their co-relations. In a retail store, AI Vision can analyze shopper dwell time, shopper interaction with products, alert anomalies such as misplaced objects in the wrong area, water spillage, and identifying reasons for inventory shrinkage, etc.

Recommendations and Predictive Analytics

Smart Vision equipped with actionable insights provides recommendations in the form of real-time alerts, analytics, and also integrates with existing business systems.

Cogniphi’s AI Vision is a pioneer in providing hyper-local computer vision solutions to retailers and manufacturers. Different components of the solution cater to different aspects of visual data and analytics to

– detect objects and their interaction in space and time.

– detect human body parts, their movement, interactions with real-world objects, their transformations, and other associated patterns.

– detect human faces and recognize people in 1:1 (Verification) and 1:N (Identification) mode.

– detect attention, emotions, and expressions on the faces

– identify and detect textures of objects.

– extract data from several documents and categorize data to train the model to interpret and analyze this data.

AI Vision is Here to Stay

Vision Intelligence technology is a cost-effective upgrade to existing data feeding cameras. On top of being a non-disruptive installation, Cogniphi’s AI Vision can help an enterprise reduce human error and experience overall increased productivity.

The Grand View Research states the growing valuation of the global computer vision market is expected to go up from $11 billion in 2020 to reach $19 billion by 2027. Manufacturing, Energy, Retail, Transportation, and Healthcare are the industries identified as best positioned to capitalize on this technology in the coming years.

The 2020 McKinsey Global Survey on AI has concluded that 50% of companies have adopted AI in one or other business functions and that the majority of use cases are aimed at optimizing operations or at product development or at customer service improvement. As companies increasingly look at AI to solve real-world challenges that depend a great deal on visual inputs, computer vision will have a huge role to play in achieving these objectives.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. It believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.

Reimagining Brick & Mortar Retail: How AI Vision can help

As the world recovers from the disastrous global pandemic, we are witnessing the revival and renewal of brick-and-mortar retail stores. eCommerce giants such as Amazon, are investing in innovative physical stores, which is reassuring for the traditional brick-and-mortar retail format.

For physical stores to remain competitive against these online behemoths, it is imperative that they stay up to date with the latest technologies. Modern technology can revolutionize the customer shopping experience and the efficiency of retailers’ operations. For example, Amazon stores are contactless, which means shoppers can walk in, select products, and walk out without stopping to pay. Customers are immediately charged for their goods as they leave the store. While this is convenient, an upgrade to this extent is not practical for most retailers, especially those outside the grocery style format, where customer service is key.

While innovation at Amazon’s scale is cost-prohibitive and inaccessible, there are other, more cost-effective, methods that most retailers can benefit immensely from currently.

Cogniphi’s AI Vision technology upgrades the retailer’s existing security cameras, enabling these cameras to understand what they see and send actionable insights to staff for action. These cameras become intelligent eyes that collect data 24/7 and improve store operations, revenue generation, efficiency, and safety.

The use cases of the data that could be collected using AI Vision are endless. Here are a few questions to demonstrate how beneficial this technology can be:

    • How does a retailer know when a product is out of stock on the shelf?
    • How long does it take a retailer to replenish that stock or place an order?
    • How does a retailer know what kind of customer is purchasing that product?
    • How does a retailer know how long it took a customer to decide on a product?
    • How does a retailer gather this information across all their stores?
    • How does the head office group know that the individual store is correctly displaying the products at all times?

These questions go deeper and deeper to demonstrate the depth there is for improvement in the retail space.

Enhancing existing security cameras with AI means that a camera can see that stock levels are low, can check the system to see if there is stock available in the stockroom, and can send a notification to staff to replenish immediately. This is a far greater outcome than the current model of waiting hours or until the next day for the product to be replenished, causing the store to miss out on sales.

Furthermore, the AI cameras can collect data on how many shoppers were interested in that product, provide information on how long it took the shopper to decide on that product or detect if a product was stolen and trigger a response for staff or security. This data is all anonymized so no shopper is identified.

These are only a few applications of many, that Cogniphi’s AI Vision can achieve. Below we explore applications that Cogniphi has executed at retailers around the world.

Heat Maps

Retail heat maps can help understand individual shop functionality and identify customer behavior at and around aisles. Retail heat map technology uses real-time imaging to detect movement and assign colors to each floor area based on traffic volume, frequency of visits, or dwell time in those areas to understand customer activity, test new merchandising strategies, and experiment with layouts.

The heat maps can be filtered by different metrics and by different customers, for example, by demographics, or by whether a shopper is alone, is a couple or a part of a group. This data is captured at a statistical population level so retailers can now make decisions off population-size data which includes all the available data sets as opposed to traditionally limited sample sizes.

The data can be used to re-design a store layout, product layout, and optimise category positioning by understanding which areas in a store have high traffic and by who, to achieve growth in basket size and value of purchase.

AI-based Loss Prevention

With AI Vision, theft can be prevented by identifying concealment of products, products that are not scanned at checkouts or products that are incorrectly scanned at checkouts. The cameras detect suspicious activity and behavior in real-time, giving retail stores enough time to respond proactively before the product leaves the store, as opposed to reactively spending time searching through footage to find evidence after the product is long gone.

Some retailers have opted to integrate notifications into point-of-sale devices for staff at checkouts, who can stop the offender before they leave or make sure products are scanned correctly. In other stores, a notification is immediately sent to security personnel with an image of the offender. An added advantage of AI Vision is that each incident is recorded and cataloged for later reference, flexible to your requirements.

AI Vision can also prevent staff from stealing by tracking each product across the store in real-time. Reducing such instances in the store can significantly reduce the losses faced by retailers on a regular basis.

Shopper Analytics

Conventional sensor-based, customer analytics apps can detect in-store traffic in limited detail and without contextual information, for example, they cannot tell the difference between a staff member and a shopper, so the data is always inaccurate. They also do not understand when a shopper is shopping in a group so when calculating store conversion rate, the data shows a lower conversion as not all members of a group, for example, a family will be expected to make a purchase. Cogniphi’s AI Vision can help retailers analyze and observe buyer behavior very closely by providing contextual data about shoppers.

Use cases include:

    • Notifying staff if a customer has not been approached for assistance within 30 seconds of entering the store.
    • Notifying staff if a customer is displaying expressions of confusion, frustration or is upset.
    • Collecting data on how many shoppers come into the store each day and ensuring accurate staff resource planning to meet demand.
    • Collecting data on localized shopper demographics for a more personalized product range and marketing messages.
    • Analyzing queue length and alerting staff to open more checkouts or close checkouts.
    • Providing accurate conversion rates and statistics.

Stock Management

AI Vision can monitor shelves and aisles to check if any of the shelves are out of stock or out of place.  This ensures timely adjustments so no customer misses out on purchasing products. It also ensures products are ordered on time so there are no out of stocks.

This data can be collected across all stores and shared with suppliers to hold them accountable for out of stocks or so that retailers can increase their negotiating power by proving they are fulfilling their trading terms and conditions.

Compliance Management

For stores that have multiple locations, there are many requirements imposed on them by their centralized head office. The challenge for head offices to manage is: how do they ensure all their stores are consistently meeting the many compliance conditions imposed on them?

Conditions include (among many):

    • Correct staff uniform
    • Opening and closing on time
    • Correct display of marketing materials and point of sale
    • Adherence to planogram requirements
    • Appropriate cleanliness and cleaning method
    • No out of stocks on the shelf

Using Cogniphi’s AI Vision, each store can receive an accurate rating out of 100%, calculated in real-time, on how they are performing against these KPIs. This will prioritize the efforts of Area Managers whose role is to ensure each store in their area is up to standard. When a store starts to drop its compliance, immediate notifications can be sent to managers to obtain remedies.

Occupational Health and Safety (OH&S)

Retail stores have many risk factors that can endanger the safety of staff and shoppers. In some stores, there are heavy machines that require trained operators. In other stores, a slippery surface could cause an individual to trip and hurt themselves.

Cogniphi’s AI Vision can monitor a store 24/7 for hazards that can impose an OH&S risk. If a spill or slippery surface is detected. Staff will be notified immediately to clean the area. If the process is for staff to wear protective clothing, like a mask, any breach of this will result in a notification to a supervisor and the overall OH&S rating of the store will decrease. 

Looking at a new dawn in retail

Cogniphi’s AI Vision is a plug-and-play solution that layers AI on top of retailers’ existing video/CCTV infrastructure.  It is pre-loaded with functionality features that enable deployment with faster returns.

The solution is highly flexible with retailers globally, progressively adding more features to their cameras every year. We find that typically retailers will tackle 1 to 3 problems in their first year, and after they see results and a positive return on investment, they add more features.

Cogniphi empowers companies with tools, people, and hyper-local solutions to rapidly innovate and adapt in a hyper-competitive business landscape. If your business is interested in finding out more, please see our website for more information [Click here] or reach out to our team, and one of our friendly consultants will be in touch with you [Reach us].

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. Cogniphi believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.