In the last 10 years, automation technologies have evolved dramatically to become what we know today as Intelligent Automation. Desktop Automation—software that supports human actions by automating repetitive tasks on a local machine— was the first iteration, later evolving to Robotic Process Automation (RPA)— software that mimics human actions by automating tasks performed by humans seamlessly across various applications and systems.
Today, Intelligent Automation, built on the foundation of its predecessors, leverages traditional RPA technology and combines it with digitization and artificial intelligence to augment human intelligence and expand the realm of possibility.
Intelligent Automation can be a difficult topic to wrap one’s head around, but like a lot of things, future success starts with solid fundamentals. And if you’re looking to learn, you’ve come to the right place.
Intelligent automation is the evolution of Robotic Process Automation. At its core, Intelligent Automation is the intersect of Digitization, RPA and AI. Where RPA addresses manual, structured and repetitive tasks that can be mimicked and automated by a Bot, Intelligent Automation introduces reason and cognition to “digitize and structure” inputs so that judgement-based decision making can be completed without human intervention.
Sounds great right? But before a company can truly capitalize on intelligent automation they must first digitize their paper-based data. Why? Because AI is highly reliant on large volumes of data in order to learn and be most effective. If a part of a process is paper-based rather than digital then the capacity for AI intervention in that process is limited.
In the example in the image below you can see how intelligent automation can be leveraged to automate an entire business area, where in the past only a single process would be within the scope of robotic process automation.
With increased scope comes the opportunity to realize new benefits from your automation, including:
1. Fraud Prevention: Intelligent Automation can be leveraged in Fraud Prevention due to the use of key AI tools, such as Machine Learning. Machine learning uses a large volume of data to autonomously learn patterns, predict outcomes, and act without being explicitly programmed with specific tasks. With large amounts of user data, intelligent automation can:
• efficiently perform analytics and calculate risk in real-time,
• monitor for suspicious payments,
• verify transactions with greater accuracy than a human would be able to, and
• notify account holders of suspicious activity in order to stop fraud in its tracks
Leveraging Intelligent Automation for fraud prevention will increase trust and overall reputation with customers, ensuring that everybody from the company to the user wins.
2. Improved Customer Service: Chatbots or conversational AI are a prime example of how Intelligent Automation can improve the customer experience. Even the most basic of chatbots can handle thousands of customer inquiries, reducing call-center wait times and increasing overall customer satisfaction.
• The AI being leveraged in this form of intelligent automation is Natural Language Processing (NLP). NLP uses statistics and learning algorithms to analyze textual information in order to understand the meaning, sentiment and intent. In the customer service context, a customer can raise a support ticket with a chatbot in the form of free text. This text is then processed with NLP to determine the level of urgency in the request, the sentiment (e.g. frustration) and then manage the interaction according to the level of severity/priority.
3. Improved Process Efficiency: Intelligent automation can dramatically reduce process handling times, significantly improving process speed and customer satisfaction. Intelligent Automation also provides enhanced process analytics and management information which can help organizations precisely pin-point process bottlenecks and areas for improvement.
• Machine learning algorithms that Intelligent Automation solutions incorporate can gather, organize, track, analyze, report on and store valuable data. This data can then be used to improve existing operations, address and correct issues in a timely manner, accurately forecast needs and develop best practices, all ensuring greater process efficiency.
4. Improved Quality: Intelligent automation greatly helps reduce the risk of transactional errors—including erroneous data inputs, mistakes in rule application and missed steps —to improve overall data accuracy and data-driven decision making.
5.Expanded Scope: The combination of digitization, RPA and AI significantly increases the number of processes that are in scope for automation. The combination of technologies allows an organization to automate much more, or even all, of an end-to-end process.
Savvy organizations are already leveraging intelligent automation in its varied forms to gain competitive advantage by enabling easier access to relevant data, more informed decision making, and the streamlining of processes. If you wish to learn more about how you can leverage intelligent automation, don’t hesitate to contact The Burnie Group today.
“The companies on the 2018 Growth 500 are truly remarkable. Demonstrating foresight, innovation and smart management, their stories serve as a primer for how to build a successful entrepreneurial business today,” says Deborah Aarts, Growth 500 program manager. “As we celebrate 30 years of the Canada’s Fastest-Growing Companies program, it’s encouraging to see that entrepreneurship is healthier than ever in this country.”
“Ranking in the Growth 500 two years in a row is a great honour, and we’re delighted to find ourselves amongst Canada’s best and brightest companies,” says David Burnie, Principal and Founder of The Burnie Group. “I believe this second nod confirms that we’re on the right track with our approach to client service and innovation. We want to thank our team and clients for making this possible once again.”
Ranking Canada’s Fastest-Growing Companies by five-year revenue growth, the Growth 500—formerly known as the PROFIT 500—profiles the country’s most successful entrepreneurial businesses. The Growth 500 is produced by Canadian Business. Winners are profiled in a special Growth 500 print issue of Canadian Business (packaged with the October issue of Maclean’s magazine) and online at Growth500.ca and CanadianBusiness.com.
For 30 years, the Growth 500 ranking of Canada’s Fastest-Growing Companies has been Canada’s most respected and influential ranking of entrepreneurial achievement. Developed by PROFIT and now published in a special Growth 500 print issue of Canadian Business (packaged with the October issue of Maclean’s magazine) and online at Growth500.ca and CanadianBusiness.com, the Growth 500 ranks Canadian companies on five-year revenue growth. For more information on the ranking, visit Growth500.ca.
About Canadian Business
Founded in 1928, Canadian Business is the longest-serving and most-trusted business publication in the country. It is the country’s premier media brand for executives and senior
business leaders. It fuels the success of Canada’s business elite with a focus on the things that matter most: leadership, innovation, business strategy and management tactics. Learn more at CanadianBusiness.com.
With a widely held misconception that technology is a threat to traditional workforces, employers have an imperative to consider how RPA and AI will affect people and organizational culture when determining where and how these technologies should be used in their organizations.
Regardless of how well designed an RPA or AI strategy is, if the human side of implementing change is not a focal point of that strategy, it stands to fail. This article explores some tangible ways companies can approach change management, ensuring employee buy-in.
Automation, after all, is less about replacing employees and more about streamlining work processes. It allows an employee’s role to be redefined from a focus on mundane and repetitive tasks to one that is more complex, more value-added, and ultimately more meaningful. Managed properly, automation can lead to a more engaged workforce.
Based on our experience designing and implementing broad-scale automation programs, we’ve identified three strategies that every organization should consider when adopting RPA and AI.
Prepare not just for automation, but for a cultural shift
When preparing for an automation project, managers are often tasked with developing a list of processes that AI and RPA can quickly improve. During this discovery phase managers also need to blueprint the broader impact of automation on people and culture. Such a blueprint can help to navigate the transition towards automation, identifying required changes to employee mindsets and behaviours and building an effective communication and change management plan.
By positioning automation and AI as employee allies—put in place to help alleviate staff from repetitive and mundane tasks—organizations can rally employees to become champions for technological advancement. For this to work, transparency is key. Conversations reminding employees that these technologies are tools that are supposed to work for them are fundamental to ensuring a smooth transition. Employees of all skill levels are better served when they understand how and why their work landscape is changing.
Encourage ongoing learning and development
In typical employee onboarding, time and resources are committed to ensuring staff are trained on the skills that are required to work effectively and efficiently. For many organizations, this is where learning and development starts and ends. However, organizations that thrive know that ongoing learning is essential to both employee and company growth.
Automation and AI provide an excellent opportunity for organizations to re-invest in the skills and capabilities of their employees. With the capacity that automation and AI unlock, time can be invested in training employees on more advanced skills. Staff can be redeployed to work on more value-added activities, including customer-facing interactions and revenue-generating initiatives. Automation and AI initiatives also require employee oversight and support, and current Subject Matter Experts are often well positioned to transition into an Automation or AI Centre of Excellence. With a thoughtful approach to training and upskilling employees and designing new value-added roles, a transition to automation and/or AI can lead to a more rewarding work environment that motivates staff and boosts morale and engagement in the workplace.
A Burnie Group client recently illustrated how to positively engage employees while embracing automation. In addition to clearly communicating their automation strategy, our client gave employees the opportunity to be trained in automation core skills and join the automation Centre of Excellence to participate in the automation implementation. Employees were also encouraged to identify automation opportunities in their work area, with the commitment that capacity released through RPA would be repurposed towards growth initiatives in the organization. This approach made employees feel that they were a part of the automation transformation and resulted in a very positive attitude towards the change.
As AI and RPA become more prevalent in the workplace, employees that are equipped with future-proof skills will be less fearful of automation and better prepared to work alongside this technology.
Position your company as an innovator
People want to work for organizations that support and empower their employees, and automation can be a tool that enables this. An organization’s investment in technology can help position it as an innovator in its field, helping it to attract and retain top talent.
When organizations embrace innovation and build it into their “DNA” to —continuously reinvent work, they reduce barriers to change and create an innovative culture where everyone wins.
One Burnie Group client implemented Robotic Process Automation as part of a broader strategic focus on innovation. With innovation being core to the values of the company, RPA was viewed as a natural fit. Rather than challenging the implementation and resisting change, employees sought ways to build RPA into their day-to-day work and leverage it to spend more of time with customers and on growth-focused initiatives.
Introducing AI and RPA into the workplace is no small undertaking. While most leaders address the effectiveness and efficiency gains that these technologies can deliver, truly successful leaders take a broader view to consider the best way to engage employees in the change.
Successful companies take the time to understand how automation can complement the work of employees and then invest in building a workplace where people and automation live in harmony.
Machine Learning is the science of finding patterns in data and using those patterns to make predictions. It is the process by which, over time, machines (computers) are enabled without explicit programming, to learn, grow, and change autonomously through real-world interactions.It is a subsect of Artificial Intelligence (AI).
AI refers to a computer system’s ability to perform tasks that normally require human intelligence, such as visual perception, speech recognition, communication, decision-making, planning, learning, and the ability to move and manipulate objects.
The infographic below explores the different applications of Machine Learning in a variety of industries, to learn more about AI and Machine Learning opportunities in your industry, please contact us for a free no-obligation discussion. We look forward to hearing from you.
TORONTO, ONTARIO– (June 1, 2018) – This latest achievement builds upon an already deep and long-standing relationship with Blue Prism. One of North America’s first Blue Prism partners, The Burnie Group helps clients transform their operations through the delivery of robotic process automation, augmented by thoughtful business process redesign and performance management. This approach provides substantial cost savings, streamlines and simplifies operations, and eliminates waste, errors and the risk of fraud.
“The Burnie Group is honoured to be recognized as a Blue Prism Silver Partner,” said David Burnie, Principal and Founder of The Burnie Group. “We constantly strive to be at the forefront of what’s new, and what stands to have incredible influence. We were the first consulting firm in Canada to adopt and embrace Robotic Process Automation (RPA), and we’ve been very successful in building this practice, this award is a testament to that.”
Recently, The Burnie Group supported ATB Financial in achieving Blue Prism’s 2018 ROM Excellence Award. An accolade presented to the company who has been judged to have achieved the best performance through implementing the Robotic Operating Model that leverages object reusability, appropriate controls and organizational design to maximize business benefits and scalability.
With the demand for RPA and The Burnie Group’s services continuing to increase, together The Burnie Group and Blue Prism are helping clients deploy digital workforces across North America.
About The Burnie Group
The Burnie Group is a Canadian-based management consulting firm that helps clients improve their businesses through the application of innovative strategy, rigorous analysis, world-class technology, and the continuous pursuit of operations excellence. The Burnie Group specializes in Strategy, Operations, Robotic Process Automation (RPA), Blockchain, and Workforce Management (WFM). Our programs deliver measurable, transparent, and guaranteed results.
Artificial Intelligence (AI) is the theory and development of computer systems that can perform tasks that normally require human intelligence. These tasks include visual perception, speech recognition, decision making, and language translation. Systems capable of performing such tasks are steadily transitioning from research laboratories into industry usage.
AI technology is unique in that it is flexible in application. It can be used to improve processes, enhance interactions, and solve problems that, until recently, could only be performed by humans. AI’s advanced abilities include natural language processing (NLP), machine learning, machine perception, and enhanced analytics.
The list below details 29 cutting-edge applications made possible by AI technology.
NATURAL LANGUAGE PROCESSING
Natural language processing (NLP) is a sub-category of AI that attempts to bridge the gap between human and computer communication. AI-enabled systems such as IBM’s Watson use NLP to understand and respond to the nuances of human language. This allows for more accurate analysis of data sets and communication of insights.
Customer interaction chatbots — Chatbots are computer programs that are commonly used to interact with customers by audio or text. Conversica, for example, is a virtual sales assistant that communicates persistently and politely with prospective sales leads. Conversica uses email to engage, qualify, and follow-up with leads, allowing the sales team to focus their efforts on closing deals.
Financial chatbots — In the financial industry, chatbots such as aLVin are used to interact with brokers. They can answer questions, understand intent, and direct brokers to their desired products and information.
Virtual assistants — Beyond chatbots, AI can power more complicated virtual assistants that can recognize client needs and complete various tasks. Expensify‘s virtual assistant, Concierge, assists in the automation of expense reports and travel arrangements for companies. It can inform clients of real-time price changes and can even file receipts on their behalf.
Communication systems – AI-powered communication systems can also be used to manage relations between peers and stakeholders. CrystalKnows, a personality detection software, uses NLP to evaluate LinkedIn accounts and develop a profile of how to most effectively speak to, work with, or sell to an individual. Crystal can even draft emails to anyone based on the preferred tone suggested by his or her online presence.
Legal assistants — The language processing capabilities of AI assistants can be tailored to a specific industry. LegalRobot’s AI assistants are designed to review legal documents and make suggestions for language clarity and strength.
Cognitive retail – Virtual assistant capabilities can be integrated with other customer relationship management products to provide in-person levels of service via online platforms. The North Face‘s personal web-shopper, XPS, uses NLP to understand customer need as would a human representative. It then uses machine learning to make informed product recommendations based on customer history, location, and other data.
Personal assistants – AI can also be used on a personal device to simplify daily tasks. Gluru technology, for example, is used to power a task management application that uses NLP to analyze a user’s conversational data, such as their email. This app can identify tasks, generate a personalized to-do list, and even provide actionable buttons to complete each task.
Web speech – New language-based technological advancements in AI can improve the web navigation and searching experience. These web speech APIs integrate voice recognition technology, syntactical analysis, and machine learning to seamlessly convert voice to text and vice versa. This click- and typing-free internet interaction can improve information accessibility for those with a disability or low technological acumen.
Machine learning is an application of AI that allows systems to process data and learn to improve the performance of a specific task without explicit programming. Deep learning is a form of machine learning that mimics human learning patterns to gain an understanding of unstructured data sets and generate intelligent decisions.
Medical decision making — Deep-learning programs such as Enlitic can optimize physician decision making by analyzing a patient’s past medical history, diagnostic information, and symptoms to provide actionable insights. These programs learn as they process data, improving their ability to identify diseases and provide treatment planning.
Healthcare analytics — Deep learning can also be used to compile, analyze, and interpret collaborative data. Flatiron, for instance, has developed a cloud-based platform for healthcare professionals that compiles insights, empirical data, and patient experiences to improve oncological care on a real-time basis.
Bioinformatics – In the field of bioinformatics, scientists use AI-software to identify patterns in large data sets, such as sequenced genomes and proteomes. This analysis can help in the development of new drugs to tackle diseases by determining which proteins are encoded by a certain gene. Atomnet is a deep-learning technology that analyzes the structure of proteins known to cause disease and designs drugs accordingly.
Emotional detection —Emotional detection systems powered by AI can detect human emotions without visual input. Researchers at MIT have developed EQ Radio, a system that learns to identify human emotions based on heartbeat data collected by wireless signals. This technology may one day be used by smart homes to detect if a resident is experiencing a heart attack.
Fraud detection — Fraud prevention has always been a major challenge for the financial industry. PayPal and other eCommerce companies have started to use deep-learning fraud-detection algorithms to monitor customers’ digital transactions and identify suspicious behaviours. A study by LexisNexis found that this deep-learning approach to security has reduced PayPal’s fraud rate to 0.32% of revenue, a 1% improvement over the industry average.
Cyber Security — AI-enabled cybersecurity programs analyze and organize internal network data to identify potential threats. The security program RecordedFuture uses machine learning and NLP to contextualize information and provide actionable analyses.
Procurement optimization – Companies can use AI internally to enhance business operations such as procurement. Tamr, a data unification software, uses machine learning to clean, analyze, and sort procurement data. It identifies savings opportunities, bundles spending across business units, and exposes supplier risk.
Customer interactions — While NLP allows virtual assistants to interact easily with customers, deep learning allows them to locate and provide the desired information. The AI model DigitalGenius analyzes historical customer service transcripts in order to recognize successful customer interactions. This allows the model to predict meta-data for new cases and provide suggestions for automated customer responses.
Optimized gaming — The gaming industry, previously focused on the level distinctions of “easy,” “medium,” or “hard,” is now using AI technology to develop self-optimizing and -evaluating games. These new AI-powered games keep players engaged by continuously adjusting difficulty and strategy to suit player ability.
Military planning — In a military setting, AI can be used to increase deployment efficiency. Autonomous machines (including drones and satellites) share data in real time to develop actionable strategies. The U.S. military AI system JADE evaluates historical data combines information with learned reasoning and presents suggestions to execute large-scale plans in minimal time.
Machine perception is the ability of a system to simulate the human perception of the world. AI uses machine perception to extract information from different data sources. Computer vision is a type of machine perception that allows AI to extract information from images.
Medical imaging — Computer vision represents a huge technological advancement for medical imaging and preventative care. The diagnostic program Zebra Medical Vision collects and analyzes medical scans for various clinical identifiers. It then accesses a database of millions of scans, enabling it to provide critical information such as the location of a tumour or a patient’s risk of cardiovascular disease.
Manufacturing — The manufacturing sector is increasingly turning to robotics to speed up repetitive tasks. AI-enabled robots use computer vision to complete tasks and to adapt to changing environmental conditions, broadening the types of tasks available to robots and preventing costly mistakes on the assembly line. Fanuc‘s Gakushu robots use computer vision and a machine learning software to collect and evaluate data for path, speed, and task optimization in aerospace manufacturing.
Service industry – Some AI-enabled robots can not only understand human language but can recognize human emotions. Using computer vision, Softbank‘s humanoid robot Pepper can interpret facial expressions as human emotions and generate responses accordingly. Pepper can also recognize and remember individual faces and preferences. It is primarily used as a greeter in Japanese office buildings, restaurants, banks, and stores.
Financial industry — In the financial industry, AI programs can learn to identify potential high-yield customers, to recognize fraud, and even to forecast changes in stock trends. To further reduce fraud, the financial application Face++ uses computer vision for facial recognition to secure users’ financial transactions.
Autonomous delivery – Companies are increasingly using AI for commercial navigation purposes. Autonomous delivery systems, such as Amazon‘s delivery drones and Domino‘s Robotic Unit, use computer vision to efficiently navigate obstacles and optimize routes. Beyond commercial delivery, Matternet’s autonomous drone network in Switzerland aims to reduce medical testing times by flying diagnostic materials between hospitals and labs.
Transit safety — AI technology is paving the way for autonomous cars and accident-free transit systems. The combination of deep learning and computer vision allows cars to observe and safely interact with the surrounding environment. Road safety can be further increased by AI-enabled navigation systems, which alert drivers to potential accidents and suggest alternative navigation routes.
Geospatial analytics — Geospatial analytics use computer vision to gather and compare satellite imagery with historical data in order to develop insights. Using these insights, AI-enabled satellites can track economic trends from space. Orbital Insight, for example, predicts retail sales based on satellite images of retail store parking lots.
Childcare — AI devices use computer vision to recognize faces and navigate around obstacles, but a new Google patent suggests they can even be used to provide childcare. Google’s AI babysitter system learns to recognize different features of a home and to differentiate between family members. The system can recognize when a child has been left alone for too long, or has wandered dangerously close to a socket, and alerts the parents accordingly.
Predictive analytics are used by programs to analyze historical data in order to predict future outcomes. When combined with AI platforms, analytic ability increases in speed, scale, and application.
Marketing — Some predictive models can be used to analyze consumer data and inform marketing decisions. Magnetic, for instance, can predict the most effective advertisement to present a particular consumer and can choose to present the selected advertisement to the consumer without human supervision.
Data extraction — Data-extraction programs use AI to analyze and extract specific information from documents. Xtracta, for example, allows various retail applications to extract data from receipts. This information, combined with predictive analytics, generates useful statistical reports and relevant buying suggestions for the application user.
Social Network Analytics — Social networks can provide valuable marketing data, but also produce linguistically complicated datasets. In order to produce usable information, user profiles must be semantically analyzed using NLP. Companies can then use predictive algorithms to identify a customer’s preferences and web navigation patterns in order to provide targeted web advertising.
Development of AI technologies is actively being encouraged through projects like Soar—a cognitive architecture project aimed at developing computational building blocks for intelligent agents—and OpenCog—an open-source software project intended to create a framework for artificial general intelligence. Through such collaboration, AI capabilities continue to advance, thus expanding application potential.
On January 15th, 2018, The Burnie Group hosted EDGEtalks: Executing on the Blockchain hype, the potential application of blockchain technology to business practices. The keynote was delivered by Don Tapscott (CEO, Tapscott Group; Executive Chairman and Co-founder, Blockchain Research Institute; Co-author, Blockchain Revolution). A panel discussion followed, moderated by Doug Heintzman (Head of the Blockchain practice, The Burnie Group). Panelists included Dr. David Jaffray (EVP Technology and Innovation, University Health Network), Dr. Marek Laskowski (co-founder, Blockchain Lab; Professor, Information and Computing Technologies, York University), Dr. Atefeh Mashatan (Professor, Ted Rogers School of Information Management, Ryerson University), and Chris Owen (VP, Enterprise Shared Platforms–Blockchain, TD Bank).
Blockchain: Solving Problems for the Digital Economy
In 2008, a person, or group, known as Satoshi Nakamoto, published a paper describing the first blockchain database. Three months later, it was used by Nakamoto to launch the bitcoin cryptocurrency network. Since then, interest in cryptocurrency investment has exploded, but industry experts insist that crypto’s true value lies in its underlying technology: blockchain.
A blockchain is a distributed ledger technology. Its core value lies in its ability to provide a reliable common version of the truth to members of a business network. Digital records (transactions) receive a unique signature (hash) of a fixed length. Transactions are then bundled and stored in time-stamped “blocks” that are “chained” to the blocks of previous transactions. This chain of blocks is synchronized across a network of distributed “nodes.” By comparing the last hash in a chain, nodes find consensus on the contents and integrity of the blockchain, making it virtually immutable.
This new technology was the first real solution to a problem that had plagued digital transactions since their inception: the double-spend problem The issue being that a digital payment was merely a copy of a token of value. Tokens could be copied multiple times and spent in more than one location. With blockchain’s continuous validation and replication of transaction history, single-use for digital tokens can be guaranteed. This, in turn, positively affects the trust deficit.
Another valuable characteristic of the technology is “smart contracts,” which can be stored and executed on a blockchain. This coded feature enjoys the same immutability, and therefore trustworthiness, as the data it manipulates. It allows for the use of business logic with automated transaction attributes, solving, amongst other things, the renege problem. In this situation, a customer might leave a queue (reneging) for various reasons before completing a transaction. When a smart contract is introduced, the customer is no longer disengaged by redirection to a banking intermediary to complete the transaction. The smart contract easily completes the transaction by triggering and validating payment securely and independently.
These problem-solving characteristics are representative of blockchain’s many potential advantages. The following will explain how blockchain can generate value, enhance business efficiencies, and bring about the new era of the Internet.
The True Value of Trust and Data
Business networks are built on top of available infrastructure, which inherently contains deficiencies that can introduce friction into business activities. Many business networks, therefore, engage third-party intermediaries to mitigate deficiencies, such as trust deficits between parties. These third parties can take many forms: banks, credit card companies, insurance companies, PayPal, Alibaba, auditors, and regulators. However, this centralized intermediary system is hackable, infamously slow, and costly.
Because a blockchain network addresses the trust deficit problem it allows for peer-to-peer transactions (P2P) completely independent of intermediaries. Individuals become owners of their own data, and that data an asset, protected on a blockchain from theft and fraud by immutable, time-stamped record keeping. In a blockchain-powered world, an individual’s medical records, financial records, academic degrees, voting history, professional history, and consumer history, can all be stored in encrypted transactions. A musician’s intellectual property might be stored on a blockchain, with smart contracts triggering automatic payments every time a song is featured in a film. A patient could allow a government temporary access to medical records to help identify and prevent possible outbreaks of disease. The sharing of this information with specific people for a fixed duration is the choice of the individual, un-beholden to the delays, rules, privacy exposure, or costs associated with an intermediary.
Rethinking the Firm
According to Ronald Coase’s theory of the firm, companies only exist due to the high cost of transactions within markets. Traditionally, performing activities in-house has kept these costs low and companies strong. The Internet, created as a network of information, began the process of unbundling the firm. With the creation of blockchain as an entirely new network of information, these transaction costs are so greatly affected that companies have no choice but to evolve as the underlying rationale for their existence is altered.
Blockchain features, such as smart contracts and autonomous agents, can cut costs by enhancing business efficiencies through automation. The scale of this automation can alter the very concept of what constitutes a company; a decentralized autonomous organization (DAO), for example, is a company run entirely through computer programs with next-to-no human participant. This model uses business logic and decision making embedded in smart contracts to automatically execute actions when triggered by pre-determined situational criteria. This new platform dramatically reduces business network friction, and eliminates nearly all traditional transaction costs, paving the way for distributed companies that resemble networks rather than centralized firms.
The Second Era of the Internet
Blockchain is a new paradigm that addresses the very purpose of computing: to automate business processes with the goal of reducing headcount and friction in business networks. It provides unprecedented peer-to-peer trust and can enhance business efficiencies in nearly any industry. When the existing Internet of Things—which is currently held back by the available software infrastructure and related trust deficits—is combined with blockchain’s new “ledger of things,” a partnership is formed. This partnership represents a second era of the Internet, in which discrete devices can confidently engage and contract each other to accomplish a goal. With widespread blockchain implementation, intermediary functions become redundant, companies more automated and decentralized, and the individual consumer more autonomous with improved privacy and increased control of their information assets.
Working with an experienced partner can help you harness the full potential of blockchain technology for your business. As a leading professional services enabler of blockchain strategy and implementation in North America, The Burnie Group will help you to identify areas where blockchain can increase your business’s security and efficiencies, set the right strategy and build the right foundation to substantively advance business goals.
Key insights from the #EDGEtalks panel of speakers:
“This is nothing less than the second era of the Internet”
–Don Tapscott, executive chairman and co-founder, Blockchain Research Institute
“This is not about bitcoin. It’s not even about crypto assets. It’s about something much bigger”
–Don Tapscott, executive chairman and co-founder, Blockchain Research Institute
“What if there were not just an Internet of information. What if there were an Internet of value. Some kind of vast, global, distributed ledger where anything of value, from money to stock to votes to music could be managed, transacted, stored in a secure and private way.”
–Don Tapscott, executive chairman and co-founder, Blockchain Research Institute
“I think this is a new paradigm, and a paradigm is a mental model. And they create boundaries around… assumptions we don’t even know that they’re there.”
-Don Tapscott, executive chairman and co-founder, Blockchain Research Institute
“the biggest problem for me…today, the net effect of all this digital on our economy is that we have growing wealth but declining prosperity. Our economies are growing but in most OECD countries the middle class is shrinking. We have a bipolarization [sic] of wealth”
-Don Tapscott, executive chairman and co-founder, Blockchain Research Institute
“For the first time in history, people have can now trust each other and transact peer to peer. And this is creating a great opportunity for… people in the middle to rethink their whole value proposition and the way that they operate, to deliver better value to customers at a lower rate.”
–Don Tapscott, executive chairman and co-founder, Blockchain Research Institute
“Blockchain is no different than any other technology that preceded it. What happens with new technology is that people think about that, they digest it in the context of what they know and understand today. How can I take that technology… apply it to what I do today, and make it faster and cheaper?”
–Don Tapscott, executive chairman and co-founder, Blockchain Research Institute
“It’s your data. It’s your history. You should own it, and you should grant permission and access to whoever you think of as an authorized user of that accumulated suite and wealth of data.” – Chris Owen, VP, Enterprise Shared Platforms–Blockchain, TD Bank
“People need to roll up their sleeves and invest the time to understand it… They need to sit down and understand what is this, how does it work, and then how does it disrupt your industry and the way we manage data.”
–Chris Owen, VP, Enterprise Shared Platforms–Blockchain, TD Bank
“Little by little, increment by increment, we’re going to migrate into a world where people are going to operate differently, and it’s going to be a weird and wonderful place.”
–Chris Owen, VP, Enterprise Shared Platforms–Blockchain, TD Bank
“The promise of blockchain is a disruptive technology and disintermediary technology…These monolithic business models based on exploiting the data of their customers are going to have to change.”
–Dr. Marek Laskowski, co-founder, Blockchain Lab
“I think that’s one of the biggest decisions that need to be made is which data can you share into this commons of data, and in what cases does it make economic sense to do so? And I think that where we discover those golden points where there’s economic sense and it’s feasible to share that data in a way that the entire ecosystem benefits, I think it’s a matter of finding those. It takes work.”
Many companies today are so focused on their product and customers that thoughtful management of employee culture falls by the wayside. Despite this lack of attention, those same companies will often assert that their culture is one based on high performance. How is that possible? Performance cultures don’t grow organically, they take thought, planning, and care. Does your company walk the walk or just talk the talk?
According to a BI Worldwide survey, 55% of employees who self-identified as unhappy in their jobs said that they would still be willing to work hard to please their company’s customers. Although this is not a drastically low number, the percentage grew to 87% amongst employees who were happiest in their jobs. The notion that engaged employees are more likely to improve customer relations is also supported by the findings of a 2017 Gallup report. The report went on to add that this behaviour could result in a 20% increase in sales. These findings are illustrative of the value of employee engagement within a well-managed company culture.
“Company culture is a collection of well-established values and habits in the organization that dictate how the company functions. It is strongly influenced and driven by senior leadership.”
David Burnie, Founder and Principal of the Burnie Group
High-performing employees are engaged in their work. They are aware of and encouraged by their performance record. They also have a strong working relationship with both their peers and managers, and a clear understanding of their department or team’s purpose. Realizing these workplace attitudes is just one of the ways in which your company culture can go from low-pace to high-performance.
Corporate culture: Low pace versus high performance.
Corporate cultures can vary greatly from business to business. Some emphasize a more traditional, low-pace culture, whereas some recognize the benefits of adopting a high-performance culture.
A high-performance organization is not afraid to try new things. These companies are often early adopters of innovative new technologies. However, they achieve their business goals not only by looking to the newest technologies but by looking at what they can do to improve employee performance through adjustments to company culture. A workplace that allows for seamless collaboration, access to necessary tools, and staff empowerment is a high-performance organization, with satisfied employees and customers alike.
Low-pace corporate culture
Lack of staff enablement
Lack of trust in staff decision making.
Staff have little access to necessary tools and skills.
Staff are truly enabled
Staff can make decisions within defined limits.
Staff can easily access necessary tools and skills.
Staff are motivated to make the right decisions.
Failure is unacceptable
Staff are afraid to make mistakes.
Staff are not encouraged or motivated to try new things.
Failures is seen as part of the learning process
Management sees mistakes as learning opportunities.
Staff are expected to learn from mistakes.
Staff are not reprimanded for mistakes made while trying new methods.
Tenure is used to support advancement
Only the highest-producing employees are promoted.
Limited leadership skills and formal management training.
Staff are advanced based on merit and ability
Staff are motivated to develop their abilities.
There is a sense of “fair” recognition.
Productivity is evaluated by a direct supervisor
Subjectivity is highly prevalent.
Limited or no objective data to evaluate performance.
Staff are assessed using accessible, and trackable performance data
Staff performance is evaluated without bias.
Leaders can act quickly when performance is subpar.
Leaders are equipped with data to effectively discuss group performance and provide due credit to individual employees.
Training and coaching are not top priorities
Little specialized coaching or training for staff members.
Training sacrificed in favour of other priorities.
Capable and skilled staff is an organizational priority
Comprehensive initial and regular training on relevant themes.
Personalized training and coaching where needed.
Encouragement of career and skills development.
Support of personal development through direct managers. This could include access to resources or actionable feedback.
Lack of transparency during execution
Managers only see the final output. There is no established method for them to influence the result during the process.
Limited opportunity to instruct staff on process topics due to lack of process visibility.
Full transparency into each process stage
Managers have a line of sight into staff capabilities as well as the status of each process.
Managers can identify areas of inefficiency and provide staff with the coaching necessary to make improvements.
The Burnie Group Case Study on Culture Change
Recently, The Burnie Group assisted one of the largest North American workforce management (WFM) software providers in implementing a plan to overhaul its organizational culture. The project involved the design, introduction, and institutionalization of a new, high-performance company culture. To achieve these steps, the following goals were identified:
Increase collaboration across all groups.
Build higher performing teams.
Make group performance more transparent.
Increase support for coaching.
Facilitate one-on-one conversations between managers and staff.
The results of the implementation were immediately evident. The sales teams, for instance, became more knowledgeable of the innovations created by the development team, thanks to increased best-practice sharing and knowledge transference. Facilitation of coaching and one-on-one conversations ensured that all team members were aware of group priorities, resulting in targets being met more often. Attentive change management resulted in the accomplishment of these project goals.
How did The Burnie Group help to accomplish these goals?
Collaboration and communication between groups were poor and highly isolated.
Expertise was concentrated in certain groups and was not shared with others. This reduced the efficiency and efficacy of staff.
Collaborative meetings, like “lunch and learns,” were firmly ingrained into the new culture.
Groups now routinely meet to share learnings across the organization so that no one group holds all the relevant information and updates.
Clear Targets and Accountability
It was difficult for employees to ascertain if a group was or was not meeting organizational This lack of understanding was rarely addressed.
Through daily discussions around the visual control boards, it was discovered that standard operating procedures (SOPs) were not in place in several areas, resulting in uncertainty, inefficiency, and lost time.
A well-defined set of key performance indicators (KPIs) was decided on and used to determine progress.
Progress was discussed daily at visual control boards and team huddles.
SOPs were designed and deployed to clarify expectations and increase consistency.
Coaching sessions and one-on-one conversations were often seen as negative events and were rarely scheduled.
An agenda was developed to help managers direct conversation. It included topics like performance metrics, escalations and issues, and career development.
Regular touch points are now scheduled between managers and staff. Such conversations are no longer seen as negative in nature.
How to begin your own company culture transformation
While large-scale cultural shift programs can yield impressive results, proper planning is paramount to successfully changing company culture. Below are some ways in which you prepare your organization for a successful transformation.
Take time to plan your program
– What level of rigour can your staff handle, given the current and projected workloads and service level agreements?
– How will you deliver learnings and materials? This can be challenging for companies with distributed workforces.
Communication matters: plan your messaging
– How will you frame the reasons for this change?
– Who will become the face of change?
– How frequently will communications be issued?
– How can you ensure communications are understood?
Form the ‘right’ team
– Form a team to conduct the day-to-day work that comes with a large-scale cultural shift. These responsibilities may range from managing communications to following up with complaints.
– Define team roles and responsibilities.
– Establish a shared understanding of the process and a clear vision of the end state.
– Facilitate clear lines of communication between team members and senior
Be prepared for pushback
– Have a management plan for potential dissent.
– Address concerns and incorporate concerns into solutions.
Involve senior leadership
– Ensure senior leadership is heavily and visibly involved in the process.
– Ensure all leaders have a common understanding of the opportunity, the desired outcome, and the rollout method.
– Have leaders clearly communicate project expectations with the staff.
– Encourage leaders set expectations of behaviour and lead by example.
– Have leaders be vocal proponents of the change, communicating the organization’s reasons for making the
– Establish effective methods of top-down communication to assist staff in the prioritization of tasks.
Identify and empower Change Agents
– Enlist people who are resourceful and eager for change.
– Ensure roles and responsibilities are clearly
– Empower agents to be positive ambassadors for the change.
– Ensure agents have frequent touch points with each other and with the project management team to keep messaging and priorities aligned.
Manage for desired results
– Adjust current SOPs, KIPs, or incentives as needed to fit the new company culture.
– Measure results of the project using data to ensure the identified opportunity has been correctly addressed.
– Make metrics visible to publicize positive effects of the project.
We hope that these ideas and insights will inform your future company culture transformation. Although there will always be challenges implementing a pervasive change, having a set plan for communications, timelines, and staff responsibilities, increases the level of transitional success. If you are interested in learning more, our project leaders are ready to share their extensive experience and insights with you.
By: Shane Nightingale, Associate
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