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RPA Get Smarter – Ethics and Transparency Should be Top of Mind

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Stuart Battersby


The early incarnations of Robotic Process Automation (or RPA) technologies followed fundamental rules.  These systems were akin to user interface testing tools in which, instead of a human operator clicking on areas of the screen, software (or a ‘robot’ as it came to be known) would do this instead.  This freed up user time spent on exceedingly low-level tasks such as scraping content from the screen, copy and paste, etc.

Whilst basic in the functionality, these early implementations of RPA brought clear speed and efficiency advantages.  The tools evolved to encompass basic workflow automation in the following years, but the process was rigid with limited applicability across an enterprise.

Shortly after 2000, automation companies such as UiPath, Automation Anywhere, and Blue Prism were founded (albeit some with different names at their initial incarnation).  With a clear focus on the automation space, these companies started making significant inroads into the enterprise automation space.

RPA gets smarter

Over the years, the functionality of RPA systems has grown significantly.  No longer are they the rigid tools of their early incarnations, but instead, they offer much smarter process automation.  UiPath, for example, list 20 automation products on their website across groups such as Discover, Build, Manage, Run & Engage.  Their competitors also have comprehensive offerings.

Use cases for Robotic Process Automation are now wide and varied.  For example, with smart technology built-in, rather than just clicking on-screen regions, systems may now automatically extract content from invoices (or other customer-submitted data) and convert this into a structured database format.  These smart features may well be powered by forms of Artificial Intelligence, albeit hidden under the hood of the RPA application itself.  Automation Anywhere has a good example of this exact use case.

Given the breadth of use cases now addressed by RPA technologies across enterprise organizations, it is hard to see a development and product expansion route that does not add more AI functionality to the RPA tools themselves.  Whilst still being delivered in the package of Robotic.

Process Automation software, it is likely that this functionality will move from being hidden under the hood and used to power specific use cases in the RPA software (such as content extraction) to function in its own right that is accessible to the user.

The blurring of AI & RPA

The RPA vendors will compete with the AI vendors that sell automated machine learning software to the enterprise.  Termed AutoML, these tools enable users with little or no data science experience (often termed citizen data scientists) to build custom AI models with their data.  These models are not restricted to specifically defined use cases but can be anything the business users wish to (and have the supporting data to) build.

With our example above, once the data has been extracted from the invoices, why not let the customer build a custom AI model to classify these invoices by priority without bringing in or connecting to an additional 3rd party AI tool?  This is the logical next step in the RPA marketplace; some leaders in the space already have some of this functionality in place.

This blurring of the lines between Robotic Process Automation and Artificial Intelligence is particularly topical right now because, alongside the specialized RPA vendors, established technology companies such as Microsoft are releasing their own low-code RPA solutions to the market.  Taking Microsoft as an example, it has a long history with Artificial Intelligence.  Via Azure, its many different AI tools, including tools to build custom AI models and a dedicated AutoML solution.  Most relevant is the push to combine their products to make unique value propositions.  In our context here, that means it is likely that low-code RPA and Azure’s AI technologies will be closely aligned.

The evolving discussion of AI ethics

Evolving at the same time as RPA and AI technologies are the discussions, and in some jurisdictions regulations, on the ethics of AI systems.  Valid concerns are being raised about the ethics of AI and the diversity of organizations that build AI.

In general, these discussions and regulations aim to ensure that AI systems are built, deployed, and used in a fair, transparent and responsible manner.  There are critical organizational and ethical reasons to ensure your AI systems behave ethically.

When systems are built that operate on data that represents people (such as in HR, Finance, Healthcare, Insurance, etc.), the systems must be transparent and unbiased; even beyond use cases built with people’s data, organizations are now demanding transparency in their AI so that they can effectively assess the operational risks of deploying that AI in their business.

A typical approach is defining the business’s ethical principles, creating or adopting an ethical AI framework, and continually evaluating your AI systems against that framework and ethical principles.

As with RPA, the development of AI models may be outsourced to 3rd party companies. So evaluating the transparency and ethics of these systems becomes even more important given the lack of insight into the build process.

However, most public and organizational discussions of ethics are usually only in the context of Artificial Intelligence (where the headlines in the media are typically focused).  For this reason, developers and users of RPA systems could feel that these ethical concerns may not apply to them as they are ‘only’ working with process automation software.

Automation can impact people’s lives

If we go back to our example of invoice processing used before, we saw the potential for a custom AI model within the RPA software to automatically prioritize invoices for payment.  The technology shift would be minor to change this use case to one in healthcare that prioritized healthcare insurance claims instead of invoices.

The RPA technology could still extract data from claims documents automatically and translate this into a structured format.  The business could then train a custom classification model (using historical claims data) to prioritize payments, or conversely, flag payments to be put on hold pending review.

However, here the ethical concerns should now be very apparent.  The decision made by this model, held within the RPA software, will directly affect individuals’ health and finances.

As seen in this example, what may seem like relatively benign automation software is actually evolving to either reduce (or potentially completely remove) the human in the loop from critical decisions that impact people’s lives.  The technology may or may not be explicitly labeled and sold as Artificial Intelligence; however, the notions of ethics should still very much be top of mind.

We need a different lens

It may be better to see these ethical concerns, not through a lens of AI but instead, one focussed on automated algorithmic decisioning.

The reality is that it is not just the fact that AI technology may be making decisions that should be of concern, but in fact, any automated approach that does not have sufficient human oversight (whether this is powered by a rules-based system, Robotic Process Automation, shallow machine learning or complex deep learning for example).

Indeed if you look to the UK’s recently announced Ethics, Transparency and Accountability Framework, which is targeted at the public sector, you will see that it is focussed on ‘Automated Decision-Making.’  From the guidance document, “Automated decision-making refers to both solely automated decisions (no human judgment) and automated assisted decision-making (assisting human judgment).”

Similarly, the GDPR has been in force in the European Union for some time now, making clear provisions for individuals’ rights concerning automated individual decision-making.  The European Commission gives the following definition: “Decision-making based solely on automated means happens when decisions are taken about you by technological means and without any human involvement.

Finally, the state of California proposed in 2020 the Automated Decision Systems Accountability Act with similar goals and definitions.  Within this Act Artificial Intelligence (but not Robotic Process Automation explicitly) is called out: “‘Automated decision system’ or ‘ADS’ means a computational process, including one derived from machine learning, statistics, or other data processing or artificial intelligence techniques, that makes a decision or facilitates human decision making, that impacts persons” with assessment for accuracy, fairness, bias, discrimination, privacy, and security. Therefore, it is clear that the principle of the more general lens is recognized in public policymaking.

Enterprises should apply governance to RPA too

As organizations are putting in place teams, processes, and technologies to govern the development and use of AI within their organization, these must be extended to include all automated decisioning systems.  To reduce the burden and facilitate operation at scale within large organizations, there should not be one set of processes and tools for RPA and one for AI (or indeed, for each AI model).

This would result in a huge manual process to gather the relevant information, make this information comparable, and map it to the chosen process framework.  Instead, a unified approach should allow for a common set of controls that lead to informed decision-making and approvals.

This should not also be seen at odds with the adoption of RPA or AI; clear guidelines and approvals enable teams to go ahead with implementation, knowing the bounds in which they can operate. When using the more general lens, rather than one just targeted at AI, the implication becomes clear; ethics should be top of mind for developers and users of all automated decisioning systems, not just AI, which includes Robotic Process Automation.

Image Credit: pixabay; pexels; thank you!

Stuart Battersby

Chief Technology Officer @ Chatterbox Labs

Dr Stuart Battersby is a technology leader and CTO of Chatterbox Labs. With a PhD in Cognitive Science from Queen Mary, University of London Stuart now leads all research and technical development for Chatterbox’s ethical AI platform AIMI.

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How Augmented Reality Continues to Transform Customer Experience?

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Impact of AR


Augmented reality (AR) is continuously paving the way for more engaging interactions between businesses and their target customers. In more ways than one, AR-based customer experience can ensure increased customer satisfaction and business conversion.

According to a study, a whopping 71% of customers have very favorable opinions about AR in the shopping experience. They say it would urge them to buy from the store more often.

Augmented Reality (AR), which works as an overlaying aspect over the appearance of the physical objects and the world at large, can enhance the perception of the real world and thus can revolutionize the customer and shopping experience.

Source: retailperceptions

With the present mobile support for a three-dimensional view of the objects, delivering AR-powered shopping and customer experience across online stores and mobile stores has become more accessible.

According to a Tractica report, by 2022, the number of monthly active users for mobile augmented reality apps is likely to reach 1.9 billion.

Augmented reality-based customer experience promises a digital shopping and browsing experience transforming the entire customer journey with either immersive attributes or more meaningful interactions or more layers of information. Businesses that employ a developer to build an AR app equipped with AR experience should insist on making things easier for the customers.

Since AR, for improving the customer experience, offers so many promises, it is essential to look at the key ways AR can transform customer experience altogether.

Removes All Pre-purchase Uncertainty and Confusion

Pre-purchase

Despite the rapid growth story of e-commerce and mobile commerce stores across all niches and categories, the confusion and uncertainty about making purchases are still dominant for most customers buying online.

Since while buying online, people cannot try the products in person, the rates of product return and abandoned carts are considerably high compared to the brick and mortar stores.

This is exactly where AR can play an essential role by bridging the gap between the physical shopping experience and the online ones. Thanks to AR, online stores and mobile commerce stores can deliver a truly feel-real impression of the purchased products.

Already many stores have started to incorporate AR for the express purpose of offering an immersive shopping experience. The IKEA furniture store right on our smartphone allows you to see each furniture piece in your room.

Similarly, several garment and fashion accessory brands enable you to visualise the items on your body. For example, Gucci offers an AR trial feature so that customers can see their sneakers adorning their feet just by capturing the feet with the smartphone camera.

Delivering a Detailed Layer of Product Information

Augmented Reality (AR), besides offering an immersive and engaging way of trying the products before purchases, also ensures feting very detailed product information to the customers instantly. The best thing is this ability made AR-powered shopping popular for offline stores as well.

No wonder that in Japan, a whopping 66% of customers now expect regular brick and mortar stores to provide AR experiences. As far as understanding the production cost of the app with AR experience is concerned, this can ensure better business conversion and hence is worth the cost.

AR-based shopping and customer service can offer a lot of information instantly and guide customers navigating in a commercial space or a destination. No wonder travel companies are now utilizing AR to deliver guided tours to their customers.

While booking bus tickets or booking tickets for a sports arena or booking tickets of flights, now AR-based displays can show you the details of every seat along with the facilities to make highly informed purchases.

Already sprawling museum premises and large indoor sites in many parts of the world are helping the incoming visitors to navigate their ways with augmented reality-based interactive maps. Airliners can also guide their customers to navigate to the right gates in quick time.

Adding More Value Through Interactive Packaging

In a multitude of ways, augmented reality (AR) is making a positive impact on customer service, starting from pre-sale orders to the actual purchase experience. Interactive packaging is the latest example of this impact.

When scanning the product packages with their smartphone camera, customers can see very detailed and multilayered visuals with a lot of additional and helpful information. This results in more purchase decisions for the customers.

Heinz uses AR-based packaging to allow customers to get a lot of helpful information when purchasing tomato ketchup, an excellent example of this AR-powered interactive packaging.

There is no dearth of brands that allow customers to scan the QR code to get more detailed information about the products they are going to purchase. In business-to-business (B2B), customer communication can be highly useful. Instead of carrying brochures, visiting cards, or business presentations, you can allow your audience to scan a code to get the details.

Futuristic AR Based Dining Experience

For food and restaurant chains to receive a continuous flow of new customers and retain old customers, providing a smooth and frictionless experience became the established norm. Augmented reality (AR) in the food and beverage industry has already proved to boost customer engagement and improve brand loyalty.

There are already AR-based menus that are transforming food ordering for customers. Besides offering interactive 360-degree visuals of the food items, the latest AR-based food menu provides multiple layers of information and interactive overlays for the customers to customize before placing the orders.

Thanks to AR scanning, the food package labels have also become more accessible than ever before.

AR Powered Travel and Hospitality

If one industry we need to name has received the most significant impact of AR technology, none other than the travel and hospitality industry. AR-powered information overlays or navigational guidance appearing right on the smartphone screens completely revolutionized the traveler experience.

The Interactive virtual tours of travel sites, hotels, and restaurants offering an immersive, 360 degrees interactive visual of the sites and ambiance of premises added value to the travel experience in a never-before manner.

AR is also ensuring a highly reliable travel guide offering route and navigation guidance in real-time. Last but not least, modern translation apps providing a real-time translation of displayed signs is another excellent example of AR’s impact on the travel experience.

AR to Revolutionize Post Sales Support

Source: techseedotme

In today’s competitive business environment, every brand needs to establish its reliability through robust and uncompromising post-sales support. Such support refers to all the activities after a product is sold or a service is offered to the customers.

Some of the crucial post-sales support mechanisms that matter for branding and customer appreciation include installation, upgrades, warranties, repairs, troubleshooting, and answering customer queries.

In this respect, remote augmented reality-based customer support has already proved to be a significant way to push changes. It helped increase call resolution at the first instance by at least 20%, and the AR-based support also helped reduce the rate of dispatching technicians by at least 17%.

AR is transforming post-sales support for brands in two significant ways through AR-powered self-service and AR-powered technical support.

Business brands are increasingly using AR to help customers with self-service support. On their smartphones, customers can get guidance through highly interactive visuals and media, screen overlays on technical aspects and quick FAQ answers.

  • AR-based Technical Support

The AR-based visual support can also help customers understand the technically demanding aspects of various products, parts, the ways they function, and the particular measures of troubleshooting and problem-solving. An AR-based screen overlay can give details of all parts along with the model number, manufacturing details, versions and the problem resolution timing.

All those bulky user manuals written in multiple languages are already on their way to exit. They are being replaced by interactive AR-powered user manuals showing minute details of every component and how to operate a device with switches, buttons and other controls. For instance, a new car owner can take interactive visual guidance on different systems and car mechanisms after purchasing the vehicle.

Conclusion

Quite convincingly, AR has transformed the customer experience across e-commerce stores, brick and mortar stores and all other B2C and B2B interactions. AR has proved to be the most critical value addition impacting the customer experience.

Wasim Charoliya

Wasim Charoliya

Digital Marketing Strategist

Wasim Charoliya is a digital marketing consultant and strategist. He is passionate about helping startups, enterprises, B2B and SaaS businesses to establish thought leadership in their industry with actionable content strategy.

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The Hitchhiker’s Guide To Survival Analysis

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Benedict Timmerman


Survival analysis is the best thing in the world since sliced bread! However, in most machine learning circles, it’s pretty much synonymous with an “# it’scomplicated” relationship status.

Survival Analysis is an Extremely Valuable Branch of Statistics.

We want our guide to better serve you as a straightforward go-to/how-to, eliminating any confusion. The guide provides a valuable resource on how survival analysis, which can be applied to — well, almost anything.

However, survival analysis is wrought with misunderstanding and misuse.

What else should I know about survival analysis?

Also referred to as “time-to-event” analysis, simply put, it’s what we find when we analyze the time it takes for something like buying a new home (an event) to happen after getting a promotion -which we call “an exposure.

Basically, it’s modeling or a set of statistical stratagems which measure the time as mentioned above to an event. Literally how long it takes for something of interest to happen. Depending on what you are studying, observing, researching, or just finding interesting- you want to know, and we can now actionably determine how long it takes to happen.

To get started, first and foremost, you need to set and formulate your research question aptly to perform a survival analysis approach.

Often, researchers will simply use ‘when’ and/or ‘whether’ terminology. But, first, the information is given — as a prediction of when and/or whether something will happen.

Then the conclusion is in a yes, or no determination. Finally, the conclusion is an analysis about how long it takes before what we want to see (the subject of interest being examined) will happen, and whether what we’re looking for will happen or not.

When you’re analyzing how long it takes for an event to happen and whether it will happen at all, it is imperative that what you want to eventually see and find is the same (equal) for all the subjects you examine.

In other words, you don’t want a sample with elements that have no chance of experiencing the event. It just won’t work.

Exposure is the point when we’re off to the races and start the proverbial research clock in order to analyze any time-to-event.

The event itself, in this case buying a new home, means simply the time needed to process and develop from the exposure which is getting that promotionthe moment when we stop the “clock.”

The time elapsed between these two points is the focus of interest which we call “the survival time.”

Survival analysis is a game-changer for a diverse variety of disciplines and areas of research.

Most experts, however, mistakenly consider survival analysis a tool solely applied to study death and disease, an accurate method to measure relapse of a medical condition, the potential hospitalization of a patient, and the mortality rate in medicine and biomedical disciplines.

Survival analysis application has thankfully spread to serve a variety of fields and disciplines, including engineering, social and behavioral sciences, even professional sports.

In engineering, this process is known as “failure-time analysis” and is mostly applied to test the durability and quality of products.

Incorporating survival analysis in engineering is valuable. For example, we see a manufacturer wants to test how long it takes for light bulbs to burn out, how often the company’s computers crash, even predict when a mechanical part like an engine head gasket will crack.

In social sciences, survival analysis is known as “time-to-event” analysis. This is because there have been scientific studies to answer queries such as how long it takes for one to get married, get a first tattoo, buy a first home, or to graduate.

Medicine and biomedical research

In addition to medicine and biomedical research, JADBio can definitively perform survival analysis on several out-of-the-box and even what one may consider ‘weird’ cases, including:

Health – Obviously, when analyzing health disciplines, we can actionably determine values such as the time to: death, device failure such as a heart pump, or simply the readmission rate of a specific subset of patients.

Market – We use survival analysis more and more in marketplace areas of research such as manufacturing or sales when we want to determine the time to: a component failure in machines, whether a certain device becomes obsolete, and how long it will take to obtain a certain patent for example.

Finance – A valuable tool in the evermore elusive waters of finance, survival analysis can be applied to calculate the time to predict when a hospital may turn a profit or report loss, calculate costs, and how often staff present burnout or should be promoted.

Social Sciences – Especially helpful in social sciences, where experts now can analyze the time to: divorce, new couples having their first or second child, and how long it will take new families to buy their first home.

Government and Social Services – Helps determine the time to: child welfare and to match children with appropriate foster parents. Used to optimize the length of stay of children in the program, to estimate participation time in various social programs, and to estimate the time it takes for various policies to take effect.

Law Enforcement – Predicts the time to: estimate the likelihood of recidivism in criminal offenders.

Marketing Operations – Performed to assess the time to: the length of participation in loyalty programs.

Sports – Sports is a field where survival analysis can really be your golden goose. Sports — that’s right. In professional sports, survival analysis will change the game when it comes to delivering results like time to: mechanical failure of race car engines, or tires in F1, and the time it takes athletes to be substituted in team sports like football.

A coach can know the best time to switch out a soccer player. Team doctors and government health authorities can accurately evaluate and certainly limit the rate of Chronic Traumatic Encephalopathy (CTE) –a degenerative brain disease observed in professional athletes, military veterans, and anyone with a history of repetitive brain trauma.

In essence, there is no differentiation to survival analysis being used as a tool whether we consider health disciplines, the global market, social and behavioral issues, or professional sports.

When researching for survival analysis — survival time is the main driving interest.

We perform survival analysis on subjects that present a delayed onset of events where our goal is to observe that specific timeframe, how long it takes for the event to happen.

It is irrelevant whether there is a positive or negative correlation attributed to the event. The event may very well be death (negative), yet it can also be a new promotion (positive).

Although initially developed in the biomedical sciences to analyze time to death either of patients or of laboratory animals, survival analysis is now widely used in engineering, economics, finance, healthcare, marketing, and public policy. Survival analysis can be used to predict when a patient will expire; when cancer will metastasize, or on anything you are trying to predict time-wise.

Our Secret Special Sauce

At the core of this work is JADBio. JADBio systematically compares the performance and stability of a selection of machine learning algorithms and feature selection methods that are suitable for high-dimensional, heterogeneous, censored, clinical and other forms of data. The data set is used in the context of providing specific, accurate, and actionable predictions.

Leveraging the advances in modern data collection techniques will produce ever-larger clinical and other large data sets. It’s imperative to identify methods that can be used to analyze high-dimensional, heterogeneous, survival data.

JADBio has a world-class team and constructs a range of machine learning algorithms capable of analyzing vast types of data providing clients with the power to make decisions and steer their respective objectives in the direction of success.

Definitions of standard terms in survival analysis:

  • Event: Death, disease occurrence, disease recurrence, recovery, or other experience of interest.
  • Time: The time from the beginning of an observation period (such as surgery or beginning treatment) to (i) an event, or (ii) end of the study, or (iii) loss of contact or withdrawal from the study.

Image Credit: Provided by the author from The Hitchhikers Guide to Survival Analysis; thank you!

Benedict Timmerman

Benedict Timmerman is a Senior IT Experience Analyst supporting Digital Giraffe’s clients operating within the AI industry. Benedict covers data and machine learning solutions, providing quantitative and qualitative analysis on the available practices, people and markets. Benedict also spearheads the company’s lead generation process for its clients designing outreach campaigns.

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Expensive Coding Boot Camps are Limiting the Tech Talent Pool

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A Labor Shortage Could Be Our Economy's Biggest Downfall - ReadWrite


Coding boot camps have soared in popularity since their inception in the early 2010s. Course Report, an organization that conducts yearly market studies on boot camps, reports that nearly 25,000 coding boot camp graduates entered the job market in 2020 — up 39% from the previous year.

With the growth in popularity, however, has come an increase in price. Course Report also reports that the average coding boot camp costs more than $14,000. As costs increase, new opportunities for students to defer payments have surfaced, such as income share agreements, or ISAs, in which students don’t pay tuition until after they land jobs.

Rising Costs Mean Greater Risk

But many payback models come with risks. For example, a recent lawsuit filed against a coding boot camp is based on claims of false advertising around job placement rates that directly impacted students’ ISAs. And throughout history, there have been many predatory educational programs that have sold lies to bring in more revenue. Such instances resulted in rulings and regulations to protect students. However, it’s still true that tech training programs in the U.S. from for-profit enterprises face a complex balance of wanting to help students access better careers but needing to generate profits and returns for investors.

The bottom line is that the tech industry needs to look to new models for teaching coding skills that reduce students’ risk and financial burden.

Workforce initiatives and skilling pathways need to be more accessible to all Americans. While traditional college or university career pathways are an excellent option for some, there’s a large and growing pool of people in the U.S. for whom earning a four-year degree is unfeasible. The cost and risk of taking out student loans is a huge barrier, and many students can’t commit the necessary time while they work other jobs or care for families.

It’s Time to Create Realistic Opportunities

Workers today are interested in reskilling for new opportunities, and companies need more skilled tech workers.

But the time and money it takes to get a degree or go through a for-profit boot camp is often not an option for many.

For similar reasons, the same individuals who can’t take the traditional higher education pathways are still being left on the sidelines by boot camps.

It’s time to make opportunities more accessible and realistic for all. Exploring the following strategies can help the tech industry reduce the risk and financial burden of gaining new skills:

1. Create and support accessible, accelerated skilling pathways.

Coding boot camps do create a great talent pool. Still, to widen that pool for tech companies and create more accessible opportunities for job seekers, the tech industry needs to support other skilling pathways, such as free and accelerated digital job training courses, that open doors to individuals often shut out of other options.

Some options are not only free or affordable, but they also offer opportunities to learn skills part-time.

Because this format breaks down barriers presented by traditional education pathways and for-profit boot camps, it’s more accessible to those looking for career changes. It can produce a more diverse talent pool for tech companies.

Plus, getting more career changers into tech means bringing a wide and diverse set of transferrable soft skills into the industry.

2. Formalize apprenticeship programs.

Apprenticeship programs are great models for opening the door to more people. They allow entry-level workers to gain the specific skills they need to fill roles at a company while on the job and earning a wage. In this way, it minimizes risks for both employees and companies.

As employees learn precisely the skills they need while on the job, they can be sure they’re not risking time, money, and effort to learn potentially irrelevant skills or skills that could become irrelevant in the near future.

Companies benefit because they can mold apprentices to whatever skill sets they need. Instead of hoping candidates’ past experiences and education will serve them well in filling open roles, companies ensure candidates can do exactly what they need to fulfill current or future job responsibilities.

3. Upskill existing workers.

Even people who are already employed with a company might be interested in educational opportunities to learn new skills or sharpen their existing skill sets. Companies looking to fill tech roles can benefit from looking within their companies first to see whether anyone desires to learn new skills and move into a more technical career path.

When companies provide upskilling opportunities to current employees, they retain the talent they already have, provide accessible opportunities for employees to develop their careers, and contribute to a talent pool that will be able to fill future skills gaps.

Conclusion

Pursuing a career in technology has long been a hefty financial commitment for students — whether they’re following traditional university pathways or paying for coding boot camps.

Meanwhile, the tech industry has struggled for years to fill its skills gap and find adequate workers. Closing the gap will require the tech industry to support more accessible, financially viable opportunities for all.

Image Credit: pepi stojanovski; unsplash; thank you!

Jeff Mazur

Executive Director for LaunchCode

Jeff Mazur is the executive director for LaunchCode, a nonprofit aiming to fill the gap in tech talent by matching companies with trained individuals.

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