Securing the Hybrid or Remote Workforce With SASE
Since the transition to hybrid and remote work models began in earnest in 2020, cybercriminals have ramped up their efforts to exploit weaknesses and new vulnerabilities associated with these distributed environments. Surveys and studies have shown that remote workers are often taking shortcuts that circumvent security policies. More than ever, personal devices that may not be configured to meet security requirements are being connected to company resources. Home offices are essentially beyond the control of employers; thus, physical access controls are virtually non-existent. These are a few of the issues companies are struggling with as they strive to provide secure and dependable remote access to their staffers and monitor their work-related activities. Although it was developed before the 2020 workforce transition, the Secure Access Service Edge (SASE) concept seems tailor-made for today's iteration of the wide-area network.
What is SASE?
The cloud-based SASE service model combines wide area network (WAN) capabilities with security tools including Firewall as a Service (FwaaS), Cloud Access Security Broker (CASB), and zero trust access controls that will address and resolve many issues associated with hybrid and remote workforce environments. SASE facilitates secure connections to resources regardless of where they are in relation to those who need access to them. User access controls are based on identity, location, access timeframes, and user device risk assessments. By using what is known as worldwide points of presence, SASE reduces or eliminates latency across what can be a global network.
Zero trust is a critical component of SASE. Traditionally, everything and every user within a secured network is afforded at least some level of trust. For example, a user can move about a network accessing resources based on permissions assigned to their account once logged in. However, zero trust emphasizes on "never trust, always verify" principle. Rather than a user signing in once and having the ability to move laterally around the network during that session, both the user and device being used in a zero-trust environment would be required to authenticate each time they attempted to access designated "micro-perimeters" within the network. These micro-perimeters could be encasing applications or services, data, or other assets. Zero trust controls grant access to a micro-perimeter by verifying user identities, devices, request types, locations, activity history, and timestamps. Should a bad actor manage to gain access to a network protected by zero trust controls, they would likely find it impossible to move about and access critical resources.
SASE is highly scalable and flexible. Among others, available security features of SASE may also include data loss prevention, sandboxing, DNS security, and web filtering. Because it is cloud-based, SASE can reduce costs associated with procuring, managing, and maintaining technology resources.
Remote work with SASE
The SASE components discussed thus far serve as examples of how they can benefit organizations whether they are utilizing hybrid, remote, or more traditional work models. There are, however, some SASE advantages that relate more directly to securing and managing remote employees.
SASE facilitates better control over which remote staffers can access applications and websites. It provides more visibility into their access and usage of company resources, thus allowing management to better track those working without direct supervision and ensure that they adhere to policies. The access controls offered by SASE help to lock down home offices by blocking access via unauthorized devices. They prevent the exfiltration of sensitive data and ensure that the absence of organizational control over the physical security of the home office environment does not result in company assets falling into the hands of unauthorized individuals. Additionally, remote workers will connect to company resources via a zero-trust network, thus preventing those resources from being exposed to Internet-based threats.
Cybercriminals are increasingly targeting remote employees. New threat vectors seem to emerge daily. Remote location and hybrid work models have now become the standard. The recent Covid-19 pandemic is driving an entirely new model of working. SASE not only addresses the threats via its suite of security controls, but it also provides employers with greater insight into and control over the activities of their remote staffers. SASE dramatically reduces the vulnerabilities associated with maintaining a non-traditional WAN that includes numerous sites in the form of home offices where management lacks control over physical access.
While the transition to SASE takes time, especially for an organization currently maintaining its own IT infrastructure, the long-term benefits make it worth the effort, and they may include cost savings as well.
Cloud Enablement for Enterprise Applications
By Nikhil Shintre
Over the last decade or so, cloud has evolved as a preferred IT deployment model. Popularity of cloud can be gauged from the fact that majority of the enterprise IT providers are now having some kind of cloud offering, and most of the startup launched in this period are cloud native.
As cloud adoption becomes common practice and the benefits are well established, a larger number of enterprises move their current application stacks to cloud. As part of this shift, they also expect the Independent Software Vendors (ISVs) to enable and optimize their software for cloud. However, for the ISVs this require much deeper considerations like commercial models for cloud enablement, acceptance by the existing customers and the impact on acquisition / onboarding of new customers.
Models for Cloud Enablement -
From our experience, we see three possible models for cloud enablement – Cloud enablement in customer setup, Cloud enablement with the setup managed by ISV, and a full fledge multi-tenant SaaS setup managed by ISV.
- Hosted in Customer Cloud – This is for customers who prefer to deploy the software in their own cloud setup, by applying their own security controls for the software and data.From the ISV perspective, this is easiest to implement by adopting certain services like scaling, storage and monitoring in the software. However, since each customer could have their own cloud preference, it is advisable not to commit too much to a specific cloud service.
- Single Tenant SaaS – In this model, the ISVs deploy per customer isolated application stack either in a common cloud account or a dedicated account controlled by ISV. Both modes isolate customer specific stack, to address the concerns about security.In this model ISV handles complete deployment, monitoring and maintenance. This gives ISV flexibility to choose the cloud provider and plan the cloud enablement and optimization.
- Multi-Tenant SaaS – In this model, the ISVs deploys a single application stack with customers separated via multi-tenant implementation at the application business logic and the database level.This requires major restructuring of the application to ensure software level separation of tenant specific data and user access. Since this is software level separation, it needs to be carefully maintained during the development.
- This model aggregates the usage of resources to the best possible manner and gives the ISV the flexibility to choose the cloud provider and various services.
Comparing the Enablement Models -
Each of the above three models has its pros and cons in terms of effort for cloud enablement, maintenance of the setup, and licensing / pricing. These aspects can be compared as below.
|In Customer Cloud||Single Tenant SaaS||Multi-Tenant SaaS|
|Cost of Infrastructure||Directly paid by customer||Can be charged to customer at actuals||Must be bundled in subscription price|
|Licensing Model||Can continue with existing mechanism||Can continue with existing mechanism||Need to implement subscription model|
|License Upgrade / Downgrade||Very difficult to implement||Can enable upgrade / downgrade||Easy to upgrade / downgrade|
|Upgrade to New Versions||Customer decides on upgrade schedule||Flexibility to delay for specific cases||All customers get upgraded at once|
|Upgrade Frequency||Must be less||Can be moderate||Frequent upgrades possible|
|Onboarding Efforts||Same as existing||Reduced efforts||Minimal efforts|
|Availability monitoring||By customer||By ISV||By ISV|
How to Choose Between the Models –
Each of the three models serve a particular situation, but it is difficult to define specific rules around it. Instead, as a general guideline following aspects can be considered -
- Large customers would like to manage the application as per their security practices. Hence, if majority of customers are large sized, software hosted in customer cloud would be preferred.
- If the application is very critical in customer’s business process, the customer would prefer to control the data. For such applications, software hosted in customer cloud would be preferred.
- Application having tighter integrations with other enterprise systems, are difficult to move out from the customer. In such case, software hosted in customer cloud would be preferred.
- If the application has many small customers, or there is large no of users with small time of usage, multi-tenant SaaS is a win-win model for the customer as well as ISV.
- If the application requires continuous addition of features (e.g. New Product Development), multi-tenant SaaS makes it easier for faster deployment and fine-tuning.
- If the application involves data aggregation and data based inferences (viz AI/ML based), multi-tenant SaaS makes it easier to manage it at one place.
- When customer insists on a more stringent separation, but wants all other benefits of SaaS the single tenant SaaS can be chosen.
As a final word, the cloud enablement model must ensure smooth transition for existing customers, and ease of acquiring / onboarding new customers. So it is important to involve the product management, engineering and the customer support functions in any decision.
Points to keep in mind when Outsourcing Software Development
IT software outsourcing and CAD software outsourcing are some of the largest industries in the 21st century. Software development outsourcing takes place for various reasons, ranging from requiring specialized software/professionals to creating a digital product or addressing a given task.
A survey report indicates 57% of US start-ups have already outsourced their software development process.
Regardless of cases, outsourcing IT & CAD software projects is a positive decision that accelerates and accomplishes a software development need. But there is some caution to this.
Anything listed on the internet as software development service is not necessarily a perfect solution to your projects. There are various factors to look into, which makes finding a software development service an uphill task.
To make it easier for you, let us have a walk of some of the best and widely advised practices to adhere to when outsourcing software development projects.
Conduct background check
The first step is to evaluate your requirements and look for various software development service providers. Prepare a list of candidates. It helps in assessing market costs for such services and allows you to fix a reference point.
Once you have a fair idea, go ahead and shortlist the outsourcing firms that fit best to product requirements.
Carry out extensive research on the shortlisted companies and sort your preferred ones out. You should assess them based on their proficiencies, average turnaround time, and client reviews/testaments. Remember, you are entering into a relationship with a 3rd party vendor, and you cannot leap of faith. It is essential to be wary of anything that concerns your project and requirements. Starting a business partnership is easy but getting out of it can be messy if things go downhill.
Sort out your expenses in the correct order
There is a factor called Value for the product. The cheapest of things out there doesn’t necessarily mean they are your best bet. Even with a fixed budget, it is recommended to look for quality and not just the cheapest outsourcing vendor available. It would be best if you struck a balance between your expectations and your expenses.
Once this is figured out, the next step is to figure out the payment process and payments intervals. Businesses often come across situations that trigger payment hassles, so such possibilities must be considered and discussed beforehand. There is also a high chance of crossing the stipulated budget when outsourcing a project due to unforeseen necessities or unexpected circumstances. Therefore, it is best to leave some space in your budget for such circumstances. After all, better to be safe than sorry.
Choose the most suitable pricing model
The next step after arranging finances is looking into different types of pricing models. In this case, the pricing model is about the payment structure agreed with the vendor partner. Here are some commonly used pricing models:
• Hourly rates: In this case, a vendor is paid a fixed rate per hour. This model suits smaller software development projects since their lifecycle are short; hence, it makes sense to consider it hourly. This model also comes in handy when you have projects that only require minute modifications.
• Fixed rates: Fixed budgets involve an arrangement with clear and well-defined goals, scope, and timeline.
• Dedicatedy: Large projects or companies require a fully dedicated team, and such models need teams to work on-site.
To find the best model that works for your project, sit down, and chalk up a plan of action with the 3rd party vendor partner.
Ensure tight security of your project and product
It is an important side many up-and-coming project owners might overlook. There are a few crucial security steps you should take to safeguard your product while dealing with third-party vendors:
• Enact a secure means of transferring information on the project.
• Implement a degree of access control over sensitive data.
Please enquire about the security measures and protocols the vendor partner has set up and how they plan to work with your data. Generally, a good NDA (Non-Disclosure Agreement) should sort this out. An NDA will clearly state the clauses regarding what is allowed and not allowed with the information once the vendor partner has possession.
Although in the software development process, various working models have been put to the test. The agile methodology is popular among all. The reason for such a working principle is that a project without a definite aim and timeline can end up in a catastrophe. Some companies have witnessed such malfunctions since they put all their hopes upon the vendor team. It is vital to fix certain milestones and mini goals, to keep a step-by-step approach on the total lifecycle of the project. It also prevents the project from becoming a cluster, and you can monitor, and track completed and pending tasks.
Record keeping has been an essential aspect from biblical days. Proper documentation acts as a footprint of how your project has evolved through the whole process. The reasons why you should have adequate documentation is because:
• Documentation makes it easy to retrace procedures in case of any hassle pops up.
• Documentation maintains a written record of all transactions between you and the vendor partner.
• Documentation is one of the ways to ensure there is no room for mistakes and errors over scope, requirements, materials, content, or responsibilities.
• If you have a particular outsourcing or in-house team, good documentation is essential to understand the steps incorporated and helps following up on pending work. It makes upgrading and modifying a less cumbersome process.
Establish communication outlets and time schedules
One of the most significant issues while working with 3rd party vendor team is the lack of clarity regarding instructions and misunderstanding when developing custom applications. Such issues lead to delays and sometimes render incomplete or botched software. To address this matter, the first thing to do is set up a suitable communication medium right at the beginning of the project. The project scope and deliverables must be clearly explained and understood by both sides. During the operation, both teams should cooperate, and there must be periodic meetings on progress, issues, actionable, etc. A steady flow of information makes it easier for both parties to stay updated. If you are dealing with a foreign-based vendor partner from a different time zone, make sure the time constraints are considered, and there is no language barrier.
Set realistic goals
Finally, you must consider human factors. Dealing with humans requires flexibility. It is impractical to give a gigantic project to a vendor partner and set a short time frame expecting delivery by the deadline. You will end up with patched-up software that malfunctions. Remember to allocate time and resources for unexpected occurrences. Again, better to be safe than sorry.
An application may look impressive, but if it fails to perform as intended, your investment can be considered waste. When engaging with a 3rd party service, the emphasis should be on the desired functions, features, and smooth, easy user interface over aesthetics. Once your product performs as it should be, you can focus on its appearance and finishing look. This paragraph here no way rules out the importance of attractive looking, well-presented product, but more than what meets the eye.
Entering a partnership with a new 3rd party software development team is a meticulous and time-consuming process. However, keeping the points mentioned above should ensure seamless cooperation between you and the partner.
Common pain points with outsourcing software development
Software development outsourcing has been a common practice for quite some time. This business model has been adopted worldwide for many plus points, for example, tailored budgets, time savings, adding expertise, etc.
But anything with pros has its cons as well. You might have read about news headlines saying outsourcing is fading away; it is an old business model or how outsourcing can have negative consequences and outcomes.
Delegating IT services to 3rd party vendors is a universal cure for so many businesses. Things don't always go that as intended. The assurances of the desired result also include risks that can turn a seemingly decent idea into debris. Chief executives and Project Managers across companies have to brainstorm and develop technical challenges to stay on track with the ever-changing market ecosystem and consumer expectations. There are notable pain points concerning custom software development.
By now, you might be having second thoughts about outsourcing your IT project. But don't worry because although it is impossible to abolish all the negative factors associated with outsourcing, you can still put some anticipation and mitigation to work and bypass the issues.
A 2016 survey about outsourcing software development projects has marked out some specific pain points. Basing on the survey, the following are some common concerns regarding outsourcing software development and ways to address them:
Quality of Service
One of project managers' most frequent and biggest frustrations is the poor quality of service while dealing with software outsourcing services. Budget-centric outsourcing firms tend to supply inexperienced and cheap-to-afford software engineers. This strategy filters out the more talented cream of the crop who charge a premium for their skills. Sometimes, even teams tailored as per high skillset also fail to meet the expectations despite extensive recommendations. Now check out how to address this issue in the below-mentioned tips.
Tip 1: Too cheap rates
In a bid to save expenditure, don't sabotage your product. Usually, the cheapest ones are the worst. After all, it is the value in exchange for money. Selecting the most inexpensive services might compromise the quality of a product. Surf through various software development rates and calculate an average to regard it as a reference point.
Tip 2: Always ask for a free trial or opt for an MVP
Make sure you ask the 3rd party software service provider to demonstrate a free trial. It is done to judge code quality and their ability to meet the deadline.
There is another way, and it is known as a minimum viable product (MVP). MVP is invoked to test a business idea. Creating an MVP takes 3-4 weeks. MVP helps determine if the team meets your requirements — their update procedure, communication levels, time-zone constraints, and they have the necessary skills and expertise to get the job done.
Tip 3: Cite requirements in the contract agreement
Create an agreement document for the two parties. Define your quality requirements in the agreement. The agreement should mention coding standards, quality standards, criteria for the final product, the list of devices the product is supposed to work in, etc.
There are occasions where products work decently at first but start giving errors and malfunction in the next couple of weeks when the vendor has delivered and is not responsible anymore. Therefore, to avoid such headaches, fix a warranty period by negotiating, during which the vendor development team will correct all the bugs for no added cost.
Outsourcing often leads to uncalled expenditures you may have never expected. It is a common phenomenon. You might end up seeking advice and help from a contract lawyer or business analyst. Maybe some added business trips.
However, it has been observed, the significant causes of extra expenditures in outsourcing are the following aspects:
- The client didn't clarify their requirements.
- The client suddenly wishes to add new features not mentioned in the agreement or make changes beyond the agreement's scope.
Tip 1: Define your requirements and expectations clearly
When talking about large and complex and software projects, it is impossible to foresee every possible challenge and consider every detail. Throughout app development, requirements are often redefined, modified, and new features are added. If you clarify your needs at the starting phases, the cost estimate will be much more accurate.
Tip 2: Be prepared to pay extra if needed
Always be ready for minor changes that pop up during app development which can be implemented without using extra resources. However, if your project requires a previously unexpected new feature and you decide to enforce them, prepare a change request. These alterations influence schedule, scope, and budget will be revised and changed accordingly.
Tip 3: Create a clearcut legal document
Legal documents are pretty complicated to read since contracts or change requests must be as detailed as possible. However, the agreement has to be easy to read and understand. An agreement resembling a word salad with tricky legal jargon may not reveal the costs involved clearly. Carefully reading every clause and line before signing is a must.
Intellectual property issues
When you provide the outsourced team with confidential information, there's always a looming danger of information leakage. The outsourced partner might use your product or its elements as their own, or worse, give it to the next client. To overcome this, You should apply legal measures to protect your intellectual property.
Tip 1: Create a Non-disclosure agreement
An NDA is a legal method of protecting IP rights that specifies confidential information that requires serious privacy. NDA information encompasses business secrets, technical know-how, designs, ideas, customer lists, and other necessary information sent to the service provider. When the vendor signs the NDA, they agree not to exploit or reveal confidential information without prior client permission. In case of NDA violations, the agreement stipulates conditions of penalties and legal prosecution.
Tip 2: Include your final app in the agreement
The contract must specify the clauses mentioning the IP right regarding the final product, and all related aspects such as source code, algorithms, etc., must be transferred to the owner. To simply put, the product belongs to you after you've paid the bill.
Tip 3: Regard your service provider as your partner
One fact that could give you a sense of security is forging a long and trustworthy partnership with a well-grounded service provider. Once a business relationship matures and you start regarding each other as partners, the possibilities of IP rights infringement decrease. This mutual trust promises a sense of safety and utmost security.
The language barrier, Time zone differences & cultural fit
A thoroughly professional proficient English-speaking team is not easy to find. 3rd party services will give assurances about their team's proficiency in English, but it is not as accurate as they claim.
Time zone differences are a common and frequent facet of outsourcing software development, and occasionally, the outsourcing company sets the tone. But time zone differences also affect product delivery deadlines and seamless communication between teams.
Culturally fit is a significant pain point when hiring an offshore outsourcing vendor company. Too many cultural differences and diverse mindsets can lead to botched communication and drain your energy and resources.
Tip 1: Language focus as a criterion
Make sure your supposed service partner has a working environment that observes English as the working language, considering English is now the lingua franca. English language issues come up, especially when dealing with nations in South East Asia. In such cases, try conducting a one-on-one interview with prospects to know if you can understand each other.
Tip 2: Set a fixed time or Nearshoring
The best way to manage this is to know about the time zones of outsourced software developers and agree upon fixed online meeting hours. It has to be sorted out right when shortlisting possible vendor partners or even before signing the contract agreement. Nearshoring is another excellent choice since it presents lesser time zone differences.
Tip 3: Cultural compliance
A similar mindset is essential in business. Thus a more suitable outsourcing company would be the one that not only understands your work culture but culture as a whole.
Most of the risks mentioned above emerge when delegating a software project to a 3rd party developer team for the first time. A 3rd party service provider who can understand product requirements, client expectations, respect contracts, and IP rights, build a well-structured development team, and working model is a wish-cone-true. Being meticulous at the early stages will help you prevent mishaps and pain points. Also, it's about time you understand the importance of developing a partnership with your vendor.
EMOTION AI – A BOON FOR THE FUTURE!
By Pruthviraj Jadhav
Artificial Intelligence is the talk of the tech town. The capabilities that AI can exhibit are breaking all sorts of boundaries. There are intelligent AI projects that can create a realistic image, and then there are ones that bring images to life. Some can mimic voices. The surveillance-based AI can predict the possible turn of events at a working space and even analyze the employees based on their recorded footage. (To learn more about smart surveillance, visit www.inetra.ai)
This blog talks about a generation of AI that can identify human behavior and are special ones.
We are talking about the Expressions Social and Emotion AI, a recent inductee in the computing literature. The Emotion AI incorporates the AI domains adept in automatic analysis and synthesis of human behavior, primarily focused on human-human and human-machine interactions.
A report on “opportunities and implications of AI” by the UK Government Office for Science states, “tasks that are difficult to automate will require social intelligence.”
The Oxford Martin Program on the Impacts of Future Technology states, “the next wave of computerization will work on overcoming the engineering bottlenecks pertaining to creative and social intelligence”
What is Emotion AI?
Detection and evaluation of human emotions with the help of artificial intelligence from sources like video (facial movements, physiological signals), audio (voice emotion AI), text (natural language and sentiments) is Emotion AI.
While humans can understand and read emotions more readily than machines, machines can quickly analyze large amounts of data and recognize its relation to stress or anger from voice. Machines can learn from the finite details on human faces that occur too quickly to understand.
The Brunswik Lens Model
Let’s have a look at Fig. 1 shown below. The person on the left is characterized by an inner state µS that is externalized through observable distal cues. The person on the right perceives these as proximal cues; stimulate the attribution of an inner state µP (the perceptual judgment) to the person on the left.
From a technological perspective, the following actions are possible –
- The recognition of the inner state (mapping distal cues into the inner state).
- The perception (the actual decision made by the decision-maker).
- The synthesis (the optimal or correct decision which should have been made in that situation)
Fig 1. The Brunswik Lens Model
The Brunswik Lens model is used to compute the human-human and human-machine interactions and their emotional aspects. It is a conceptual model with two states − the inner and outer state. The outer state is easily visible for the observer but not much conclusive. The inner state is not easily understandable but leaves some physical traits (behavior, language, and physiological changes) used to perceive the inner state (not always the correct one).
For example, a happy person might shed tears of joy, but another person will consider the former in grief.?
These physical traits can be converted into data suitable for computer processing and thus, find their place in AI. In addition to the above, the Brunswik Lens covers another aspect of Emotion AI: the capability to synthesize observable traits that activate the same attribution processes that occur when a human’s traits are displayed when perceived by a human observer.
For example, suppose an artificial face displays a fake smile. In that case, humans tend to believe that the machine is happy, even though emotional expression is impossible with artificial entities since they cannot experience it.
However, people can understand the difference between humans and machines at a higher level but not at a deeper level where some processes occur outside their consciousness. In other words, a human’s reaction to machines is like how they react to other humans. Therefore, human-human interaction is a prime source of investigation for the development of human-computer interaction.
How does Emotion AI work?
Emotion AI isn’t limited to voice. It uses the following analysis –
- Sentiment analysis is used to measure and detect the emotional data of text samples (small fragments or large samples). It is a natural language processing method & can be used in marketing, product review analysis, recommendation, finance, etc.
- Video signals - It includes facial expression analysis.
- Gait analysis and gleaning - Certain physiological signals through video are analyzed to learn about heart rate and respiration without any contact using cameras under ideal conditions.
Social Media giant ‘Facebook’ introduced the reactions feature to gain insights and data regarding user’s responses to various images.
Fig 2. Reactions feature on ‘Facebook.’
Emotion AI needs user-generated data such as videos or phone calls to evaluate & compare reactions to certain stimuli. Later, such large quantities of data can be morphed into human Emotion and behavioral recognizing patterns using machine learning. It can leverage more in detail emotional reactions users have with the help of the high computational capability of machines.
The Oliver API Emotion AI can evaluate different modalities through which humans express emotions, such as voice tone, choice of words, engagement, accent. This data can be processed to produce responses and reactions to mimic empathy. The sole aim of Emotion AI is to provide users a human-like interaction.
Industry predictions -
- Global Emotion AI: According to ‘Tractica,’ the global Emotion AI market will grow from USD 123M in 2017 to USD 3,800M in 2025.
- Social Robotics: The revenues of the worldwide robotics industry were USD 28.3 billion in 2015 and are expected to reach USD 151.7 billion in 2022.
- Conversational Agents: The global market for Virtual Agents (including products like Amazon Alexa, Apple Siri, or Microsoft Cortana) will reach USD 3.6 billion by 2022.
- Global chatbot market: Valued at around USD 369.79 million in 2017 - is expected to reach approximately USD 2.16 billion in 2024.
Fig 3. Global Emotion Analytics Market (MRFR Analysis)
- Medical diagnosis – In certain diseases which need an understanding of emotions like depression and dementia, voice analysis software can be beneficial.
- Education - Emotion AI-adapted education software with capabilities to understand a kid’s emotions and frustration levels will help change the complexity of tasks accordingly.
- Employee safety - Since employee safety solutions and their demands are on the rise, Emotion AI can aid in analyzing stress and anxiety levels.
- Health care - Emotion AI-enabled bot will help remind older patients about their medications and monitor their everyday well-being.
- Car safety – With the help of computer vision, the driver’s emotional state can be analyzed to generate alerts for safety and protection.
- The autonomous car, fraud detection, retail marketing, and many more.
Emotions are a giveaway of who we are at any given moment. It impacts all facets of our intelligence and behavior at the individual and group levels. Emotion AI helps in understanding people and offers a new perspective to redefine traditional processes and products. In the coming future, it will boost up businesses and be a beneficial tool in medical, automobile, safety, and marketing domains. Thus, decoding emotions – the fundamental quality that makes us human and re-coding it to machines will be a boon to our future generation.
- Perepelkina O., Vinciarelli A. (2019), Social and Emotion AI: The Potential for Industry Impact, IEEE 8th International conference on ACIIW, Cambridge, United Kingdom.
- Brunswik E. (1956), Perception and the representative design of psychological experiments, University of California Press.
Automation vs. Future Job Market: How Will It Unfold
Automation is here to stay
An automated warehouse in Hong Kong that runs 24/7 uses a swarm of robots driven by AI to help deliver groceries. Known as Autonomous Mobile Robots, or AMR, they operate on a tailored track laden with QR codes to track their movements. The data they collect aids in improving their efficiency over time. The more the robots work, the smarter they become.
AI has helped meet modern consumers’ demands for fast delivery. The current Covid-19 pandemic has increased markets for automated logistics. Big players in e-commerce like Amazon and Alibaba already have a horde of AI-powered robots relentlessly doing their bidding. These Robots and computerized systems running them are subsets of a much bigger field of study: Artificial Intelligence.
Automation is here to stay and thrive. There is no going back from a technology that is on a mission to transform how we interact with our daily tasks. Automation is everywhere, From warehouses to factories, from mobile phones to customer support, from cab services to transportation. You name a field, and Automation is already prevailing in it.
Tesla and SpaceX CEO Elon Musk claimed that AI will be smarter than humans and will overtake by 2025. Although it sounds a bit exaggerated, the rate at which AI and Automation are galloping towards the future, such predictions are entirely dismissible. However, Elon Musk also described AI as an existential threat. There have been growing concerns about AI taking over human jobs.
Is it a grave threat, or is it fear-mongering?
As per a leading consulting firm, one in three US employees will hand over jobs to Artificial Intelligence by 2030.
How Automation is affecting various industries
Automation is a derivative of great industrial revolutions that changed the production and commodity landscape. There are four industrial revolutions, the current one being the fourth industrial revolution, also known as Industry 4.0 (To read more about Industry 4.0, refer to Introduction to Industry 4.0).
Coming back to Automation and its effect on industries, it is safe to say that some sectors will be receiving a more significant impact than others. Let us have a quick look at such industries ready to embrace the automation juggernaut.
Manufacturing: Probably the biggest receiver of change when it comes to Automation, the manufacturing industry is a fast-evolving domain that needs rapid advancements in Automation. Intelligent machines and robots have been in use in this industry for a decade already. The need for Automation in manufacturing is to enable error-proof operation, consistent production, negligible downtime, fewer human factors, and constant pace. In a world where consumer demand is growing, one must be super-efficient to meet those demands by supplying products to the market continuously.
Transportation: Transportation is one of the first industries to be affected by the automation wave. Airplanes have already been using autopilots for decades. Self-driving cars are being increasingly tested and deployed on the road. Couple that with the Internet-of-Things (IoT), and we have a robust system of intelligent vehicles.
Agriculture: With the world population touching 8 billion by the end of this decade, there is a dire necessity of producing the optimum amount of food to feed the people. As a result, the agricultural sector needs increased attention regarding automating food production, distribution, and supply.
Logistics: As mentioned earlier in this blog, top companies like Amazon and Alibaba have upgraded logistics at the consumer level by employing robots, placing AI technologies to manage warehouses and delivery departments.
Healthcare and Pharmaceuticals: With the advent of nanotechnology, robotics, and IoT, the healthcare and pharma sector has climbed the ladder and introduced some groundbreaking medical treatments. The field of gene research and genetic altering system employs nanobots to carry out tasks.
Customer Relations: Remember when you enter a website, and a pop-up generates, eager to lend you support? Or how about when you have a complaint, and you interact with a customer care executive? Well, they are most likely chatbots with curated responses to address your queries and grievances. Many retail outlets in advanced nations are adopting cashier-less automated transaction desks.
Automation will end with repetitive work, and it has started shaping future jobs. It is likely that soon a lot of the current jobs will no longer exist. It is even predicted that jobs like plumbers, car mechanics, barbers, and funeral directors are likely to be replaced by automated appliances, robots, and computers.
Will Automation Take Over Jobs, Or Will It Improve Them?
As seen from the thriving tech sector, there is no immediate threat to jobs with AI, but a more radical use of technology could destroy employment opportunities for millions. Automation has been around since the late 1800s, but with the rise of the digital revolution, we see it gain momentum and be applied to a wide range of sectors and services. We are already witnessing Automation and the use of robots taking over repetitive and mundane processes like manufacturing and sending information to factory floors. In the transport sector, most of the workforce is being replaced by technology. The financial industry has also begun losing jobs to computers as these can perform most of the jobs that have to be done.
Eventually, it may result in a world full of unemployed people and loads of robots and intelligent systems. Yes, all those possibilities could turn out to be true. There are big movie franchises that show why this is not a good idea.
According to a 2013 study on the probability of automated jobs predicted that bank workers, transportation and logistics workers, and clerical and administrative workers - many middle-class jobs - were at risk of being replaced by technology.
But is that fear-mongering genuine?
While Automation will indeed displace many jobs over the next 10 to 15 years, it won’t eliminate human employees at all but rather modify the job landscape by introducing new work opportunities. Rather than eliminating the drudgery of repetitive tasks, Automation will place people in control of an entirely different set of operations. As a result, future jobs will require a different set of skills and educational requirements. By eliminating machines, most experts believe that Automation will create an enormous number of jobs.
The World Economic Forum estimates that automation will lead to a net increase of 5.8 million jobs.
An Investment Management firm predicts that Automation will boost US GDP by five percent to $1.2 trillion over the next five years. Contrary to widespread fears of job losses, the World Economic Forum predicts a net gain of 5.8 million jobs through Automation. Two-thirds of the jobs that will be transformed by Automation will be more skilled, while the other third will be less skilled.
Fears that machines will put large parts of people out of work are exaggerated. Today’s European workers are facing a degree of change as their jobs evolve with technology. Market analysts suggest that more than 80 million European employees - about 50% of the total workforce - will have to learn significant new skills and upgrade themselves for their current jobs over the next decade. In megacities such as London and Paris, employment opportunities are concentrated where few residents are qualified to fill them. This, in turn, is a situation where labor-saving tools can lead to more work for people, sometimes without interference, sometimes due to new technological requirements.
Studies claim that most jobs will modify rather than disappear entirely. Many jobs will continue to exist, with a healthy number of automated tasks. While we have seen a decline in automatic manual work and routine tasks, other skills are generally considered safe: cognitive skills such as critical thinking and socio-behavioral skills such as recognizing and managing emotions and improving teamwork. Robots are not so easy to replicate today. Competitive advantages can be summarized as Automation in selected countries, where companies can protect and increase jobs.
World Bank's 2019 World Development Report dismisses the speculations about automation displacing jobs.
Data entry and office jobs are likely to decline, as computers can instantly load files and sort information. On the other hand, occupational therapists who treat, support, and evaluate people in the workplace are skills that robots are unlikely to replicate.
To be productive in the future, many experts suggest that humans and robots must work side by side. Robots will need jobs that can be automated, but humans will need jobs that require a personal and creative touch.
Companies can make a working synergy between employees and Automation. A good example is a renowned robotics company that uses Cobots - collaborative robots that companies can design to make Automation easier for human employees to use. The company has developed an online course to allow workers with no technical background to program a robot in 87 minutes. As a result, human workers can create automated robots for specific tasks, which will give back large amounts of valuable data.
The book “The Sentient Machine” is more optimistic about the impact of AI on society than books like “The Rise of the Robots,” which is more a cautionary tale, raising concerns about robot automation and AI taking over jobs only by workers but also by employees. Those who have a high probability of losing their jobs through artificial intelligence should not panic.
The Future of Automation
There is a tipping point that Automation is coming, and the impact on jobs will be determined by which country adapts most rapidly and effectively. Employers can expect to rely on computers for jobs that people would typically do. Computers are more error-free, and in some areas, more competent than human workers, so intelligent computers and their robot associates will be the future of work.
One thing is for sure. Countries highly dependent on industries like agriculture, textiles, food, and cars, are expected to be the worst hit. However, a study by The World Economic Forum predicts that even developed economies will see jobs lost to technology in the next five years. But what’s surprising is that the impact on employment is also likely to be far less intense in countries where we’d least expect it. Just before a global conference on the future of work, CNBC decided to explore how technology is transforming the world of work and the effect on both blue and white-collar workers.
The long-term impact of AI and other automation technologies on the labor market is uncertain as of now. It is recognized that many jobs will be affected, but it is difficult to predict precisely which positions in which sectors are at risk. No one knows how things will unfold in the future, so the best bet is to study the Automation & AI market, upgrade accordingly, and stay prepared.
AI-ML Intelligence and Learning
The emergence of Artificial Intelligence in recent years has shifted the dynamic of technology’s interaction and implementation in a way seldom seen. The fact that machines can think, analyze and operate like humans have been raising eyebrows since its inception. Artificial Intelligence, Machine Learning is deemed to be the most sought after field and career option in the coming decades.
But what is so captivating about AI ML? What is it that sparked fears of AI ML taking over human labor?
Inarguably, it is the essential attribute of AI – Intelligence and Learning.
Humans are the only species in the world whose intelligent quotient surpasses any other species by a wide margin. Whether due to evolution or otherworldly miracles, it is safe to say that humans stand at the top of the food chain and dominate the ecosystem like no other. This dominance can be credited to human intelligence.
Although there is no definite description for intelligence, one of the greatest scientists of the 21st century, Stephen Hawking famously quoted, “Intelligence is the ability to adapt to change.”
Intelligence can be thought of as an ability to acquire and apply knowledge or skills. The broad spectrum of intelligence covers abilities like understanding, logic, self-awareness, emotional experience, reasoning, planning, critical thinking and problem-solving. Although some of these abilities are found in every other animal species out there, humans surpass them by a long shot.
So what is it that humans do differently and better than others. Here are a few examples:
- Humans are capable of gathering information about a phenomenon from multiple sources. Such a varied perspective makes it easy for humans to consolidate data from different standpoints and form a solid knowledge foundation.
- Collecting and consolidating information lets humans correlate all the data and bind them together to form a patchwork thus giving shape to the knowledge base.
- A fundamental characteristic is the ability to make decisions with limited data or a partial understanding of the system. A human mind can process a piece of information from several standpoints and draw out the best conclusion, something never witnessed in any other species.
Learning is an essential factor in the field of evolution. Without learning capability, humans would not make it this far. Most of the animal species have a distinct learning curve, which helped them overcome adversities and evolve accordingly.
Learning is a process that causes “change” as a result of acquiring new or modifying existing knowledge, behaviors, skills, values, or preferences.
Learning is very much intertwined with intelligence. Learning is an application of intelligence itself. Putting it simply, intelligence is the stirs the pot while learning is the taste of it and understanding what needs to be done. Here is how intelligence and learning interact with each other:
Learning encompasses the following methodologies:
- Working with only data and no/partial knowledge about a system
- The next step leads to building a model with the limited amount of data
- The last step includes drawing out conclusions from the model, analyze and identify the shortcomings and refine the model
Learning facilitates the prediction of data based on the understanding of the model and gathering actual observations. These observations are compared with the predicted outcomes to differentiate between the two sets.
Approach to AI ML through Intelligence and Learning
The conventional method of problem-solving through intelligence and learning covered a one-time pre-defined rigid model which, when running through the application, yields a definite result that couldn’t be processed any further. Such a method consists of strict one-way inputs which are mostly theoretical. This approach negates the learning process and presents a limited scope for refinement. The conventional means of that takes a lot of time and once completed, it can be considered done and dusted.
The AI ML approach, however, takes a more flexible route as it enables the extraction of information from time to time, understanding the essence of information, and refine or adjust a model as per the findings. Such a method consists of various environmental inputs that might vary from time to time to draw several conclusions. The results are extracted to pull the absolute intrinsic or indispensable quality of something, which determines its character. AI ML approach to problem-solving facilitates adjustment of the model in accordance with its character to further run it through application again.
Although AI ML has imitated human intelligence and learning capability to a reasonable extent, the game is far from over. Humans being complex creatures has a wide range of intelligence, namely Logical-mathematical, existential, interpersonal, linguistic, bodily-kinesthetic etc. Humans can also derive the meaning of cosmic entities far from the reach of an AI machine for now. It remains to be seen how far AI ML will go considering this is just the start.
AI-ML Engineering Problems
Artificial Intelligence (AI), Machine Learning, and Deep Learning have been extensively used for more than a decade but largely remained confined to areas such as voice recognition, image reconstruction, image/signal processing, and output prediction.
Such algorithms have seen limited usage in engineering domains such as thermal management, electronics cooling industries, fluid dynamics prediction inside the engine or over a bonnet, aerodynamics, and fluid dynamics problems across an aero-foil or turbine engine.
The delicate relation between AI ML and engineering can be better explained with two specific terms – A priori knowledge and posteriori knowledge. Since the time of Emmanual Kent, western philosophy has defined A priori knowledge as something which is attained from reason and independent of particular experiences. On the contrary, posteriori knowledge is derived from real evidence that has to be considered authentic.
It means A priori knowledge is not circumstance-centric but instead follows a set of pretty universal rules. Fundamental concepts of thermodynamics, electromagnetism, mechanical, and material properties are highly quantitative. They stick to a predetermined route rather than a vast stock of different scenarios.
Engineering Problems Requirements
Every problem related to engineering emphasizes the below-mentioned parameters:
High Accuracy Levels – Every endeavor starts with a model in the early stages. The model undergoes various physical applications and virtual simulations. It is done to gather all sorts of data to determine the proposed workability of the model and improvise areas. The model goes through several stages of scrutiny until high accuracy levels are achieved. AI ML works more on input feeding, and the outputs fluctuate every given time.
Function Over Feel – Engineering problems ask for the accurately intended functionality of a model. Feel of the component is never the priority. Every process applied to a model at every stage makes sure the intended functioning is obtained. As mentioned before, it is more linear. On the other hand, AI ML targets more on the feel, which varies with different situations.
High Repeatability and Predictability – An engineering task involves a high repetition of activities and the desired outcome is already known. One cannot simply predict an AI ML output, and as a result, for a conventional model in engineering, AI ML is not suitable.
However, recent years have witnessed increased usage of AI ML in the engineering sector, which is attributed to the following change in trends:
- Keeping up with rapid advancements to address consumer needs, the speed of coming up with new ideas, design, versions have increased
- Extensive field testing is not viable anymore
- Over the years, lots of digital footprint data of earlier design and earlier products have been accumulated and is available to serve as feed for AI ML
- Feel attribute is getting a lot of importance which results in the implantation of AI ML
- The trend of customized design to suit specific requirements is getting more and more common
Application of AI ML in Engineering Problems
Although Artificial Intelligence has found its niche in the engineering sector, it is extensively found in four areas of operation, which have a massive importance in today’s market.
As the related data are available for every product released in the market, we have a readily available vast database to quickly conjure up past information and generate engineering data out of it. This makes the task more streamlined, so we can understand product requirements, highlight the recurrence of similar conditions in the past, and pull out past data that have previously catered to the same.
This minimizes the time required to draw out an elaborate plan from scratch. If a problem is repetitive, it can be solved with the help of past data. This helps to intend to multiple issues simultaneously.
Failure Analysis is the collection of data and analysis to obtain the cause of a failure.
Failure analysis is essential as it helps pinpoint the causes, reasons behind causes and pave a way to determine corrective actions or liabilities. A massive set of failure analysis records is fed to AI ML, which comes in handy during similar failures. AI ML can assess the loss and come back with valuable information, should the incident occurred in the past. Once again, it reduces detailed investigation and time.
A significant aspect of AI, while digital twin has been around circa 2002, credit goes to the Internet of Things (IoT) for making it cost-effective to implement. It was named one of the top 10 technology trends for 2017, considering it is imperative to business.
The digital twin is a virtual, digital replica of a real-world entity or process.
The intelligent components are integrated into a physical element to gather data such as working conditions, position, and process changes. The compiled data is collected, synthesized, and integrated into a virtual model and AI algorithms. Such data assets can be created even before the physical model is built. Applying analytics into these virtual models can give back relevant insights about the real-world asset. The best part of the digital twin is that once the physical and the virtual models are integrated, the virtual model can sync with the actual model.
The digital inspection involves collecting information and analysis of products on production to ensure quality control.
The operation of digital inspection has gained considerable momentum in engineering, especially in the manufacturing sector. Unlike paper inspections which could have been laced with occasional errors, digital inspection minimizes or completely obliterates the chances of mistakes. AI ML has made its way to production and manufacturing, consequently providing automation that is faster, cost-effective, and superior to human involvement. AI-infused digital inspections build intelligent systems that perform quality checks down to the finest of details, leaving no stones unturned.
The rise of artificial intelligence has allowed automated machines to develop complicated manufacturing and design operations. AI has found significant importance in:
- Areas where data is available (or could be generated) and forward model is hazy or too complex
- Areas concerning partial data and incrementally growing data problems
- Areas of operation with disparate sources of information and varied data
- Forward engineering problems where constraints and inputs are not well defined/quantified
The end goal is to introduce machines capable of learning, exploring, probing, and improving without human intervention. AI ML and Big Data are climbing the ladders of engineering with pace. An interesting point to bring up is that in our pursuit of creating supreme AIs, we are unwrapping information about how human brains perceive & operate and how we address the learning process, both consciously and unconsciously.
Brief history of Artificial Intelligence (AI)
In November 2014, E-commerce giant Amazon announced the launch of Alexa, a voice-controlled virtual assistant whose task is to transform words into action. It caught the attention of tech enthusiasts and the general populace alike. The inclusion of Samuel L. Jackson’s voice in Alexa was the talk of the tech town.
Recent years have witnessed a climactic change in the way technology interacts with humans. Alexa happens to be just that one card out of the deck. From Tesla’s cybertruck to internet giant Facebook’s EdgeRank and Google’s PageRank has called for both awe and a little bit of commotion within the tech community. The driving force behind such innovations can be put under a single umbrella term — Artificial Intelligence or AI.
Artificial intelligence (AI) can be defined as — the simulation of human intelligence in machines, especially computer systems and robotics. The machines are programmed to think and mimic human actions such as learning, identifying, and problem-solving.
Although AI has burst into the scene nowadays, the history of AI goes way before the term was first coined. It is safe to say that the principle is derived from the Automata theory and found references in many storybooks and novels. Early ideas about thinking machines emerged in the late 1940s to ’50s by the likes of Alan Turing or Von Neumann. Alan Turing famously created the imitation game,
now called the Turing Test.
After initial enthusiasm and funding on machine intelligence until the early 1960s,entered a decade of silence. It was the period of reduced interest and funding on research and development of AI. This period of decline is known as ‘AI Winter.’ Commercial ventures and financial assistance dried up and AI was put on hibernation for the said period.
The late 1970s witnessed a renewed interest in AI. American machine learning pioneer Paul Werbos devised the process of training artificial neural networks through backpropagation of errors. In simple terms — Back Propagation is a learning algorithm for training multi-layer perceptrons, also known as Artificial Neural Networks.
The neural networks consist of a set of algorithms that loosely mimics a human brain. It means much like a human brain; it is designed to interpret sensory data, cluster raw inputs, and classify them accordingly.
1986 saw the backpropagation gaining widespread recognition through the efforts of David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams. In 1993, Wan became the first person to win the international pattern recognition contest with the help of the backpropagation process.
Since the emergence of computers and artificial intelligence, computer scientists have drawn parallels between these intelligent machines and human minds. The comparison reached a pinnacle when, in 1997, an information technology company, IBM, created a computer known as Deep Blue to participate in a chess match with renowned chess master Gary Kasparov. The match went on for several days and received massive media coverage. After a six-game match, Gary Kasparov secured a win, Deep Blue secured two wins and rest three draws. The highlight of the spectacle, however, was the ability of machines to push forward the boundaries and lay down a new benchmark for computers.
Deep Blue made an impact on computing in many different industries. It enabled computer scientists to explore and develop ways to design a computer to tackle complex human problems with the help of deep knowledge to analyze a higher number of possible outcomes.
The rise in popularity of social media with Facebook saw the implementation of AI/ML in a wide array of applications. One prominent characteristic was the use of DeepFace. As the name suggests, DeepFace is a deep learning facial recognition system designed to identify human faces in digital images. DeepFace was trained on four million images uploaded by Facebook users and is said to reach an accuracy of 97%. Not so long after, NVIDIA launched Generative Adversarial Network (GAN), which is a class of machine learning designed to generate new data with the same inputs provided. The portraits created by GAN is so realistic that a human eye can be fooled into thinking it as a real snapshot of a person. GAN has seen widespread usage in the creation of celebrity faces. Google’s popular doodles are an outcome of the GAN system.
The advent and rise of AI, however, has generated quite of bit of negative speculations as well, owing to recent developments in the said field. Some key concerns are as follows:
- In 2016, Hong-Kong based Hanson Robotics introduced Sophia to the world. Sophia is a humanoid robot adept in social skills that can strike a conversation, answer questions and display more than 60 facial expressions. As much as it looked futuristic, the eeriness of the entire scenario did strike a discomfort among the masses. After all, machines being humans is something people are not accustomed to. The increasing use of robots and robotic science in the manufacturing industry is striking a rather uncomfortable nerve worldwide, as it comes with the replacement of the human workforce.
- It has been noticed that only a handful of industries gain immense help from AI. This has mostly been the IT sector and specific manufacturing industries. As a result, not every party is not willing to invest in AI technology and it remains to be seen how the situation unfolds in such a scenario.
- The last two decades witnessed a blossoming of interest and investments in AI. The emergence of AI algorithms, coupled with massive amounts of data and its ability to bend/manipulate them, is one of the most significant factors that artificial intelligence has reached where it is today. The development of deep learning is another for resurgence out of AI winter. However, with all the investments, interest, and funding, can AI live up to its hype, or is it heading towards another AI winter due to over-exaggeration, overpromising, and seemingly under-delivery of it said capabilities. It remains to be seen.
While there are certainly lots of speculations for AI, we expect that the next AI winter would not come. Another AI winter is possible if we repeat the past circumstances. As for now, AI is becoming a part of our daily lives. It is in our cars, phones, and other technologies we use on a day-to-day basis. It is common to interact with AI regularly, whether it is a helping chatbot, personalized ad or better movie show/TV suggestions. AI is too much integrated into our lives and only time will tell where it heads.
Insourcing - A Breakdown
Outsourcing has remained an integral aspect of striking deals between engineering and design firms. While it has been growing at a solid pace each year, several companies have taken the route to insource a part of their formerly outsourced services portfolio.
Insourcing is the practice of assigning a task to an individual or group inside a company. The work that would have been contracted out is performed in house.
Insourcing is entirely opposed to outsourcing where the work is contracted outside. Insourcing encircles any work assigned to an individual, team, department or other groups within an organization. It is a task or function that a firm could also outsource to a vendor, being directed in-roads. It often involves getting specialists with relevant expertise to fill temporary needs or train existing professionals to execute tasks without the need to outsource the same. The group of professionals could either be direct employees of the organization or hired expertise from outside third party vendors.
A perfect example can be put in this way – a company based in India opens a plant in the United States and employs American workers to work on Indian products. From the Indian perspective, this is outsourcing, but from the American perspective, it is insourcing.
Causes of Insourcing
The leading reasons for insourcing include:
- A management mandate to make changes in corporate sourcing strategy
- To provide a remedy for a turbulent outsourcing relationship
- To obtain the right mix of in-house and outsourced services based on current business goals
- Mergers and acquisitions can also influence insourcing decisions. A decent post-acquisition integration plan should include a common sourcing strategy between the two companies, which may ask for the outsourcing of functions that are in-house at one company and the insourcing of a task that was previously outsourced at the other
- Insourcing enables companies to have control over decision-making and the ability to move more quickly and precisely
Reasons to Insource
- Boosting business agility
- Transformation needs secure integration with the business
- Knowledge is now available and increasingly democratized
- Cybersecurity threats
- Providing a platform to nurture talent
While executing an insourcing project can be achieved, it is essential to know that insourcing a service can be more complicated than outsourcing the same. The transition may require rebuilding services and leveraging capabilities from ground level that were once wholly owned by the service provider, which can turn out to be more complicated than expected.
Insourcing vs Outsourcing
Both insourcing and outsourcing are feasible ways of bringing in labor or specialty skills for a business without hiring permanent employees. When it comes to selecting between outsourcing and insourcing, several entrepreneurs cannot decide what is best for them. Before jumping on to the differences between these two business practices, we need to check the definition of the terms.
Insourcing is the practice of assigning a task or function to an individual or group inside a company. The work that would have been contracted out is performed in house.
Outsourcing is the act of assigning a task or function to a third party vendor instead of having it performed in-house.
Differences between Insourcing and Outsourcing
- Insourcing helps to track the development process and puts control over the quality of the work, while in the case of outsourcing, it becomes difficult to trace the quality of work.
- There is very minimal risk in insourcing, as one can have complete supervision over intellectual property (IP). In outsourcing, the entire task is in the hands of an outside third party. In case IP is leaked, it proves awful as investments on research, people and development work go in the vain and outside party claiming the idea as theirs.
- Insourcing helps in evading intermediaries’ costs like fees and commissions. Insourcing also drives to point other cost exponentials such as incorporating and utilizing third-party vendors who offer value-based pricing.
- As insourcing helps in keeping track of task and workforce, it runs hand in hand with development squad which in turn helps in keeping an eye on every move in the business, finding problems and resolving them. While in outsourcing, one lacks to track when the problem arises and how it’s fixed.
- In outsourcing, there are possibilities of miscommunication as the outsourcer and outsource vendor are in different places. The information goes from head management, and then, they commune it to outsource provider’s managers, who will lastly convey it to employees. This arduous and lengthy process has a risk of miscommunications. However, insourcing cancels out such possibilities. The miscommunication aspect is reduced in insourcing, as there is direct communication with employees.
- Outsourcing a project overseas might face a few issues due to the different time zone and cultural factors. A vendor might have varying physical outsources, various techniques, design, and engineering. There is a big chance of communication problem due to different time zone. In insourcing, the assigned team will easily decipher the requirements, design, and engineering to produce a product as per nativity.
- Various projects require complete confidentiality of data, which cannot be outsourced to a third party vendor. In that case, it is feasible to bring in their resources over to the project location, keeping the confidentiality intact while introducing expertise.
Insourcing is more preferrable when the business requirement is for a limited time or temporary or involves little investment. Outsourcing weighs more when businesses need to cut costs while still in need of expert professionals.
Choosing an insourcing partner
Insourcing software development has turned out to be an effective way for tech firms to boost business (to learn more, refer to Insourcing – A Breakdown). It is the exact opposite of outsourcing with similar intents (to know more, refer to Insourcing vs. Outsourcing). But like any business strategy, preparation and execution is necessary and are crucial for a successful endeavor. Choosing an insourcing partner requires as much meticulous planning and careful observation as in the case of outsourcing. Following are the tips on choosing the right insourcing partner for your business.
Establish insourcing goals
This is the most critical step a company can take while choosing an insourcing partner. The scope of work, the billings, and the project requirements have to fall under the insourcing partner's capability. The responsibility of the partner is to maintain a high standard of quality.
The Right team size
Many companies overlook this consideration while looking into insourcing options, but it's one of the most crucial factors in completing an in-house project. Make sure the vendor partner has the right blend of expertise and number to cater to your requirements.
Find out if the vendor-supplied workforce has the right experience and expertise in delivering services similar to the one you plan for insourcing. This includes several projects executed, types of clients worked for, and function expertise for knowledge-intensive tasks. Assess the management team's experience and qualifications, project managers, and other team members of the vendor company. Before entering into a long-term or substantial contract, interacting with the proposed team members before the commitment ensures fitment between the requirement and the team chose to execute it.
This factor is also overlooked to a great extent. It is essential to make sure that the vendor partner has sufficient working capital and is financially secure. There have been cases where the insourced workforce is not paid correctly by their employers, which affects their productivity. It is a classic case of ergonomics.
Privacy and Confidentiality
Numerous projects emphasize the confidentiality factor. There might be instances where a task cannot be outsourced merely because of the sophisticated nature and business goals entangled with it. But then, lack of workforce and budget issues drive a company to go for the insourcing route. It helps in keeping the work in-house and private while supplying it with necessary measurements.
There might be a variety of other factors out there depending upon client preferences and conditions. Irrespective of the vendor one chooses, starting a pilot project with a small team is always feasible to assess the outcome's scope in the long run and scale-up with time seeing the vendor's fitment with the business objectives and culture.