The Executive Guide to Artificial Intelligence

How to Identify and Implement Applications for AI in Your Organization Andrew Burgess Palgrave Macmillan © 2017

  By This Book:           
  

About the Author

Management consultant, author and speaker Andrew Burgess is an expert on disruptive technology. The Global Sourcing Association chose him as the 2017 Automation Champion of the World. A former CTO, he advises companies on AI and co-authored of The Rise of Legal Services Outsourcing

Summary

A Real Look at Artificial Intelligence

Artificial intelligence (AI) applies computer systems to tasks that once required human intelligence. A long-standing debate within the AI community asks if AI should augment the human mind or replace the work it does. Either way, AI and automation will fundamentally reshape the workforce.

AI can develop its abilities through supervised or unsupervised learning. In supervised learning, which is more common, people train AI systems using data and guide the system through making distinctions like between pictures that show dogs and pictures that don’t. In unsupervised learning, systems start with data that mean nothing to them and identify patterns on their own.

AI isn’t a hypothetical development that might appear sometime in the future. Businesses utilize AI today, and it transforms how they work. Many consumers experience AI today in the form of virtual helpers like Siri or Alexis.

"Artificial intelligence is being used in businesses today to augment, improve and change the way that they work."

An "AI Framework"

AI has eight core capabilities. In this framework, four capabilities focus on capturing information and four focus on figuring out "what is happening." The capabilities in the first set are: "speech and recognition, image recognition, search" and "clustering." Image recognition involves tagging images and making distinctions among them. When machines capture information, they convert unstructured data (big data) to structured data. This requires speedy processors and a lot of training. Certain capabilities make AI immediately useful. For example, speech recognition lets people give machines direct commands. 

The capabilities in the second set are: natural language understanding (NLU), optimization, prediction” and "understanding." The first three have applications in daily life. NLU goes beyond voice recognition. It includes a degree of understanding, AI’s ultimate capability, and it requires cognition. In optimization, an AI system transforms data from one form to another. Optimization requires the system to reach a goal, and it often applies algorithms and "cognitive reasoning" to solve problems. Prediction uses historical data to assess new data, for example, in making restaurant recommendations or in analyzing the risk factors in a loan application. Understanding, which isn’t yet commercially available, involves the machine’s ability to be consciously aware of what it does or thinks.

These eight capabilities work sequentially and synthetically. For example, speech

recognition might recognize someone’s words, a prediction function might complete the requested search and optimization might solve a problem.

"The biggest barrier to AI achieving escape velocity…is the overinflation of expectations."

"It’s impossible to get value out of something if it is not understood, unless it’s by some happy accident. In the world of AI there are no happy accidents; everything is designed with meticulous detail with specific goals in mind."

The Rise of AI

AI’s development stretches back to the mid-20th century. Early work focused on so-called expert systems. Programmers mapped knowledge of a topic in a set of branching choices. User choices would guide the system down one branch or another – an approach still used today in applications like chatbots.

AI passed through two "AI winters" – from 1974 to 1980 and from 1987 to 1993 during which progress stagnated. Both winters occurred thanks to too much hype and too little funding.

Several factors contribute to the contemporary rise of AI. The first is big data. Artificial intelligence needs "millions of examples" for training. Today’s continual use of social media and the Internet provides that data. Cheap storage, constantly increasing computing speed and ubiquitous connectivity drive AI and fuel the growth of cloud AI. Still, AI faces several barriers, including hype. People claim too much for AI. Excessive claims make people fear how AI might change business and the economy, or make their jobs obsolete. Most of AI’s tasks remain hidden from observers, and regulation could be a potent barrier to implementation.

"The first driver for the explosion of interest and activity in AI is the sheer volume of data that is now available."

Deep Neural Networks

AI depends on machine learning, that is, machines carry out difficult conceptual work, not people. Deep neural networks (DNNs) provide AI architecture. These networks have multiple layers – the more complex a problem, the more layers. DNNs have an input layer, an output layer and hidden layers in between where the difficult work gets done. Nodes in one layer connect to nodes in others. Each connection is weighted, which creates both weak and strong links. Weaker links produce undesired answers during training and don’t pass along as much information. As developers train networks, the weights adjust to reach an optimal level.

"Chatbots come in all shapes and sizes, which is a rather polite way of saying that there are really good chatbots but also very bad ones."

Associated Technologies

Practitioners can use AI alone or with other technologies. Cloud computing uses

multiple remote servers linked via network. These servers store data. Cloud computing gives AI access to large, public data sets. Analysts then use cloud computing to process the data. Technicians use AI with robotic process automation (RPA), which employs technology to replace a series of human actions. RPA performs transactional work much more cheaply than people can, especially repetitive processes – like reading similar documents – and rules based processes – like answering IT service requests.

Robotics uses AI. Autonomous vehicles depend on AI to sort the information their sensors gather. Some firms use service robots to greet people. AI also comes into play in the Internet of Things (IoT) when devices transmit data directly to each other. When billions of devices transmit data, this generates massive big data – a natural place to implement AI. When AI can’t complete a task, humans intervene, such as in crowdsourcing or in cases when a task exceeds a system’s capability – say, reading handwritten text.

"The reason machine learning is called machine learning is, rather obviously, that it is the machine, or computer, that does the learning."

AI in the Real World

Some organizations use AI to improve customer service, for example, via chatbots. Simple chatbots can answer only yes/no or multiple-choice questions. But chatbots that receive extensive training through thousands of human-to-human chat conversations can answer questions and help customers make orders.

Recommendation engine AI – such as Amazon’s and Netflix’s – applies data from

customer purchases to suggest future purchases. AI processes claims quickly and improves functions customers will never see. British retailer Tesco sends robots through its stores filming the shelves. The system uses image recognition to identify product gaps and lets staff know where to restock. The Israeli tech company Nexar uses information from a dash cam app to help people become better drivers. Business leaders who want to work with AI should identify the challenges their company faces and ask how AI can help. Leaders should consider AI and automation together and decide what they want such systems to accomplish. They can try a solution or application on a small scale, test it and

then apply it more broadly. Businesses should align their AI strategies with their overall strategies.

"Capturing information is something that our brain does very well but machines have historically struggled with."

"Robotic process automation…describes a relatively new type of software that replicates the transactional, rulesbased work that a human being might do."

The "AI Maturity Matrix"

Companies can adapt a Maturity Matrix – as originally developed by Carnegie Mellon University for use in IT – to evaluate their current level of AI integration. Traditional maturity matrices have five levels, but an AI matrix should have six, with “Level 0” referring to firms that still do everything manually. Companies, or individual departments or divisions may operate at five levels:

1. The firm applies traditional IT applications to specific tasks – like processing invoices but hasn’t assessed AI’s impact or applied automation more broadly.

2. Most people still do most things manually, but at least one team has automated a task using scripting or macros.

3. A firm starts applying automation tools tactically to meet distinct goals.

4. Firms use a range of automation tools to apply AI to multiple processes.

5. A Level 5 firm applies AI and automation throughout its operations.

"One aspect where AI projects are generally trickier than ‘normal’ IT projects is with the dependency on data, and this challenge is particularly acute during the prototyping stage."

"AI Heat Map"

Organizations can create an AI heat map to identify the areas of their operations where applying AI is “desirable, economically viable and/or technically feasible.” Firms should start with their strategic objectives, and identify pressing challenges and places where sufficient data is available to enable AI-based solutions.

For your firm, list the possibilities and rate them by desirability and how feasible or viable they are. Rate all possibilities using the same scale, say 1–10, so your firm can compare rankings from different areas. As a firm chooses AI projects, it can develop a business case for each one. Calculate a project’s “hard benefits” – like reducing costs, mitigating risk, increasing compliance and customer satisfaction, reducing losses, and generating revenue. Also assess its “soft benefits,” such as its impact on the firm’s culture and its marketing. Consider your options before implementing AI. Buying off-the-shelf AI software is simplest. Firms with special needs may build their own platforms and applications for greater control and flexibility. Only build a customized corporate system when your firm has large-scale, pressing needs. AI platforms fall somewhere between those two options. Huge companies such as Google and Amazon use platforms because they can train customized algorithms to handle specific tasks.

"If there is trust and transparency around the data that consumers find useful, then they are more likely to allow businesses open access to that, therefore

increasing the utility even further."

Implementing AI

As many firms implement AI, some are ready for the next level – “industrializing” AI. A successful firm will develop an “ecosystem” to support its AI and automation projects. Within that system, all vendors and technology should align with corporate strategy. Vendors should demonstrate technological expertise, experience and a cultural fit. The firm should form architecture teams to guide AI-related options through development and implementation to operations. AI-driven firms may add new leadership positions, such as a “chief data officer” and “chief automation officer.”

AI’s primary challenge is dealing with poor data. With AI, accuracy isn’t as important as in traditional computing. “Data fidelity” matters more. Biased or inappropriate data can disrupt AI performance. Users can improve data by crowdsourcing or “cleansing” it to remove inaccuracies. AI also must cope with its own “bias and naïveté.” AI systems don’t understand social norms and may learn incorrect or inappropriate behavior. They need training via human intervention so they don’t find correlations that lack meaning.

Choosing the “wrong technology” is also a risk. However, AI uses specialized applications that do just one thing and do it well. If a business assembles an AI system out of multiple components, it should be able to replace any single component to improve overall system function. As businesses adopt AI, they could become overly dependent on it. This overdependence can be practical (can users tell if answers are correct?) or philosophical (will humans forget how to think if the machines think for them?). There’s also a risk of “malicious acts.” For example, if a bank implements voice recognition as part of its security system for account holders, an AI system could mimic those voices to gain access

to the accounts. Some users have directed AI to “socially engineer people’s behaviors.” For example, bots can post messages on social media to redirect political conversations.

"Prediction employs one of the core ideas of AI in that it uses lots of historical data in order to match a new piece of data to an identified group."

“Creating your first AI build, however small, is a key milestone for any AI program."

AI’s Future

Image recognition will continue to improve – with better image tagging and facial recognition. The use of voice recognition use will expand more into business-to-business interactions. Improvements in microphone technology and algorithms for speech recognition will make real-time voice transcription more accurate and efficient. Search software will improve. NLU will gain capability, especially in real time. The lack of “properly labeled, high-quality data sets” will continue to be a constraint. As proper data become more available and AI improves – through reinforcement learning – at using unlabeled data, optimization will continue to develop.

"Sometimes AI simply isn’t up to the job. Sometimes you will need to pull humans into the loop to help complete the process."

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How to Win Friends and Influence People

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Cryptoassets

The Innovative Investor’s Guide to Bitcoin and Beyond
Chris Burniske and Jack Tatar McGraw-Hill © 2017

                        By This Book: 
                        

About the Author

Chris Burniske is a co-founder of Placeholder Ventures, a firm that focuses on cryptoassets. Jack Tatar is an investor in and adviser to cryptoasset start-ups.

Summary

The Origins of Bitcoin

Bitcoin shot onto the world’s radar just as the United States’ financial system was breaking down in 2008 amid the fallout from collateralized mortgage obligations (CMOs). In October of that year, an individual using the pen name Satoshi Nakamoto released a paper detailing a currency called bitcoin and explaining the blockchain software that underpins it.

Nakamoto had created an alternative to the centralized arrangements used for electronic financial transactions. He designed the system to have “decentralized autonomy”; that is, the masses rather than the government would oversee it. Ostensibly, Nakamoto’s blockchain, had it been available earlier, could have tracked the mortgages in CMOs and surfaced the linkages among them, thereby increasing the securities transparency and perhaps mitigating the widespread financial crisis that erupted.

Nakamoto’s real identity and his whereabouts remain unknown. But bitcoin and blockchain have caught on and given rise to a flourishing number of cryptoassets.

“Bitcoin lets anyone be their own bank, putting control in the hands of a grassroots movement and empowering the globally unbanked.”

The ABCs of Bitcoin and Blockchain

In general, the term “blockchain technology” refers to its components, “hardware, software, applications and users, in relation to a personal computer.” Bitcoin currency is the product of the particular software coding that constitutes the bitcoin blockchain technology. Bitcoin software is open source, meaning that anyone can access the technology to generate bitcoins or to alter the algorithms and create other cryptoassets. The software is a distributed, digital record keeping ledger. No one can alter or erase the records in a chain. Data updates occur in subsequent entries. The technology uses encryption to ascertain the bonafides of those making transactions. Blockchain is transparent; anyone can view the details.

“Miners” are individual computer users who compete to record transactions on Bitcoin’s blockchain. Their “proof-of-work” is how the different computers group the transactions into a block and the blocks into a chain. The miners must solve a “cryptographic puzzle” to gain access to append to the blockchain. For their efforts, they receive pay in bitcoins.

The process boosts the supply of bitcoins, and the awarded coins motivate users to mine.

Blockchains can be either public or private: Public blockchains are open to anyone on the Internet, while private blockchains are akin to intranets, available only to authorized users.

The Evolution of the Currency and the Technology

In its short existence, bitcoin’s price increases have been impressive. An initial purchase of $100 in bitcoins on July 19, 2010, at the start of bitcoin trading, would have soared to a mind-boggling $1.3 million by January 2017. In comparison, over this period, $100 invested in the S&P 500 would have grown to $242. Similarly, bitcoin delivered returns well in excess of those provided by the technology stalwart FANG stocks – Facebook, Amazon, Netflix and Google.

“As a small portion of the innovative investor’s overall portfolio, alternatives are an effective way to balance risk.”

Bitcoin also did better on a risk-adjusted return basis than the three major indices the S&P 500, DJIA and NASDAQ 100. However, not everybody bought into bitcoin at its inception:

Those who purchased $100 worth at a peak price of $1,242 per bitcoin in November 2013 would have seen that $100 investment whittled down to $83 by January 2017.

“Bitcoin’s blockchain is a database that records the flow of its native currency, bitcoin.”

Early in 2011, bitcoin captured the attention of a wider audience and of criminal

elements with the debut of the dark web’s Silk Road online marketplace, which chose the cryptocurrency as its coin of the realm. Drug dealers quickly overtook the digital Silk Road. Another spike in bitcoin’s profile, along with an almost 700% increase in its value, happened in April 2013. Some observers speculate that this leap in demand for bitcoins was the consequence of a financial crisis in Cyprus, which led to Cypriots losing money in their bank accounts. Bitcoin holders would not have been susceptible to such sovereign losses, because bitcoin is outside a government’s control.

Then, in November 2013, bitcoin burst onto the global stage due to rising demand in China.

The People’s Bank of China noticed this activity and promptly clamped down on the use of bitcoin. At the same time, authorities in the United States caught up with the founder of Silk Road and closed down the website. A long, steep and rocky decline in bitcoin’s value followed.

Subsequently, blockchain became more prominent than bitcoin as a "general purpose technology" that, like the steam engine, has the potential to change the world. As 2017 began, the inventory of cryptoassets grew to more than 800 offerings, including Litecoin, Ripple, Zcash and Ethereum. However, bitcoins worth $17 billion at the time made up the lion’s share, roughly 70%, of the total value of cryptocurrencies.

“Each time miners add a block, they get paid in bitcoin for doing so, which is why they choose to compete in the first place.”

What Cryptoassets Can Do for Investment Portfolios

Cryptoassets are alternative investments, as are investments in real estate and commodities.

Alternatives help diversify portfolio risk, since they have low correlations with traditional capital assets. 

The three main categories of cryptoassets are:

•  Cryptocurrencies – These function, like money, as a "means of exchange, store of value and unit of account."

• Cryptocommodities – These encompass the parts of the digital infrastructure, such as bandwidth and storage.

•  Cryptotokens – These vouchers enable user access to "digital goods and services" such as social networks and games.

An investment in these assets can help bring down the overall risk of a portfolio, since their correlation with mainstream capital assets is near zero. This unusual circumstance is likely because cryptoassets are so new that their pool of investors does not overlap much with those who invest in conventional securities.

"A private blockchain is typically used to expedite and make existing processes

more efficient, thereby rewarding the entities that have crafted the software and maintain the computers."

What Differentiates Cryptoassets from Other Investments

In 2017, the US Securities and Exchange Commission issued guidelines to classify cryptoassets as securities. However, as of early 2018, the SEC has not approved any cryptoasset’s applications for exchange trading, nor is it required to do so. Aside from the status of SEC approvals, a good way to understand cryptoassets is to consider the investment class in which they’re categorized, but the multiplicity of their functions as currencies, commodities and tokens makes cryptoassets complicated to classify. The US Commodities Futures Trading Commission sees them as commodities, and the US Internal Revenue Service has referred to them as property. Traditional assets fall into the broad groups of "capital assets, consumable/transformable assets or store of value assets." Cryptoassets are somewhere between a commodity and a store of value, unlike metals such as gold, which are both. Cryptoassets are a distinct asset class for the following important reasons:

• They have a unique form of governance. Developers, owners of mining computers, the companies that intermediate cryptoassets, the asset holders and the end users all participate in oversight and accountability.

• A computer code issues the assets in a way that intentionally maintains a scarce supply. For example, releases of new bitcoins occur on a progressively decreasing schedule. The maximum number of coins, 21 million, will be in circulation by 2140. After that date, holders will receive transaction fees, similar to those credit card companies pay.

• Cryptoassets offer uses that other asset classes do not, and these will evolve as technology changes. Most notably, bitcoin serves as a “decentralized global currency.” Some people invest in these new assets for their intrinsic utility, such as bitcoin’s ability to settle financial transactions. This purpose might be difficult to distinguish from an investor’s “expectation of profit,” a condition met by SEC regulated securities. Speculation is another motive for holding the assets, just as occurs with some traditional instruments.

"If [the SEC] feels there are still not enough consumer protections in place for bitcoin and other cryptoassets, then it has no obligations to approve any exchangetraded products."

"While exchanges, by default, will store the assets they trade, that is not always the safest place."

"Over time, next to zero bitcoin will be issued, but the aim is for the network to be so big by then that all contributors get paid a sufficient amount via transaction fees, just like Visa or MasterCard."

Trading Cryptoassets

Because they are new investment vehicles, cryptoassets trade on exchanges separate from those of conventional securities. As an alternative to mining, an investor can acquire holdings of existing assets or make ground-floor purchases of newly created cryptoassets through an initial coin offering. These issues might be announced in a private network, on social media or on listing sites such as CoinFund. Exchanges have proliferated, from one in mid-2010 to more than 40 in early 2017. Trading volume has taken off as a consequence; the worth of bitcoin trades reached more than $11 billion in January 2017. With greater capacity, liquidity has increased and the value of trades has jumped. In January 2017, the price of a single bitcoin shot past $1,000. Similarly, other cryptoassets such as Ethereum have seen increases in their trading volume over time. Of course, external factors, including regulation, affect the market. As of 2016, better than 90% of the global trading in bitcoin took place in China. In 2017, the People’s Bank of China’s restrictions caused bitcoin’s price to plummet from its $1,000 peak, just as happened in 2013, the first time the currency hit the $1,000 mark. However, bitcoin’s value recovered more quickly in 2017 due to the greater number of exchanges.

The availability of exchanges for trading cryptoassets is a sign of "market maturity," as is a decline in volatility. And as specific new assets develop their features, their performance will better correlate both with other cryptoassets and with conventional securities.

The market for cryptoassets is making progress in these areas, but it has a way to go to reach maturity. Importantly, the assets have attracted masses of speculators and experienced wild market swings. Some new asset offerings have turned out to be fraud schemes presented to investors with false information. As with most new assets, investing in cryptomarkets calls for a great deal of caution.

"When a cryptoasset is skyrocketing, it can be hard to resist the urge to jump in

and ride the rocket. However, the timing can be precarious, and spotting the end of a bubble is not easy."

"Any cryptoasset worth its mustard has an origination white paper."

Evaluating Cryptoassets

Cryptoassets appeal to innovators, and they offer investors many opportunities for early participation in start-up companies. Blockchain technology applications can substantially change or disrupt the way business works in many sectors of the economy. In the financial services industry, speedy and low-cost bitcoin payments could replace companies like Western Union for sending remittances across borders. "Business-to-business payments" likewise could flow more readily with cryptocurrencies. Distributed ledger technology can smooth the insurance industry claims process. Supply chains, health care and real estate information access, and delivery tracking are all targets for more effective redesign using blockchain.

As with traditional offerings, fundamental analysis is a good place to start an investment evaluation of a cryptoasset. The creators of a cryptoasset provide a "white paper" with the specifics of the asset, the problems it solves, its competition, its technical information and the plan the developers have for its issuance. The asset should have a "decentralization edge," which is a legitimate reason for this standalone product. A white paper should contain detailed descriptions of the asset’s utility and its competitive advantage. Value also derives from the supply of the asset relative to the demand for it. Speculative activity affects the asset’s potential value. An asset’s turnover, or its velocity, is another indicator of value. Additionally, it’s preferable to have the computers mining the cryptoasset spread far apart geographically rather than clustered together, so that they are not subject to any single jurisdiction.

Technical analysis, based on price and volume, can supplement fundamental metrics.

Cryptoassets require storage after their acquisition. Options include deposit accounts at a trading exchange; a "hot wallet," giving an investor access to the asset through the Internet; and "cold storage" of the asset in an investor’s offline device.

"If the miners for a cryptoasset are all in a single country, then that cryptoasset could be at the mercy of that nation’s government."

"Even though the rules regarding taxation of these assets may change, one thing is clear: as with any other asset, the IRS is watching.”

"Goldilocks Years of Cryptoassets"

Though Wall Street still remains largely on the cryptoassets sidelines, some large corporations and financial institutions are beginning to test the feasibility of using blockchains. Millennials, however, could make this the moment for investing in cryptoassets, given their greater comfort with the concept, its practices and its promise.

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Open to Think

Slow Down, Think Creatively, and Make Better Decisions
Dan Pontefract

By This Book:    

Summary                           

Pause to Reflect

Today, people are busier than ever. They have little time to do anything well including the vital process of thinking things through. For many people, clear thinking is an increasingly rare commodity. People don’t always get around to the necessary steps of thoughtfully weighing their options, building expertise and reaching their own reasoned conclusions to solve problems and make decisions. Some outsource their creative and critical thinking to Alexa, Siri or Wikipedia. Closed thinkers are unwilling to open their minds to new ideas. They seem to think it’s more convenient to go through life shut off from new information and ideas. Too many organizations also function in a close-minded way.

Many people and companies need to adopt a new thought process to build their decision making skills and agility. This improved system of thought – open thinking – is “a holistic approach of reflection, decision making and action to secure an ethical outcome.” Open thinking calls for careful consideration, for taking action through a process of “dreaming, deciding and doing.” With open thinking, you work through an issue, weigh the evidence, decide how to resolve it and then take the necessary action. Open thinking is iterative, inclusive, contemplative and interrogative. It’s deliberative, not automatic or reflexive.

“When we think, we are using our mind to actively form or connect an idea… Thinking is also an

approach, a possibility, a deliberation, an opinion, or an attitude.It can even be a belief or a conclusion.”

“Reflection and Action”

Open thinking balances reflection and action. If these elements don’t align, three bad habits can take hold:

1. “Indifferent thinking” – Habit traps people into staying with their current methods and thought processes no matter how self-defeating.

2. “Indecisive thinking” – Those who have a hard time making decisions constantly muddle over what to do or not do. They fall prey to “endless dreaming,” a state of mind that strategic management expert H. Igor Ansoff calls “paralysis by analysis.”

3. “Inflexible thinking” – Many people are uncomfortable consciously thinking about what they believe or the actions they’re going to take. They avoid analyzing their own processes and just plunge ahead. They “choose activity over a weighted blend of ideation, pause, consideration and response…The act of doing becomes the most important thing.”

“Thinking – like eating is something we all do. In fact, we are all constantly thinking. But as with eating, there are both healthy and unhealthy habits.”

“Better thinking is hard, not easy. Better thinking takes time, not haste. There is no shortcut.”

Welcome New Ideas

Open thinkers remain receptive to new ideas and information from a wide variety of sources. They welcome innovative concepts and view accepted dogma with skepticism. Open thinkers move beyond what they know. They acknowledge what they don’t know, and they’re willing to learn. They pursue discovery even if the new knowledge might upset them or blast apart their current world view. To determine if you should incorporate open thinking into your way of handling information, conducting analysis and making decisions, ask yourself three questions about the way you think now: Do you devote sufficient time to “reflecting and dreaming?” Do you rely on verifiable data to make decisions? And, do you take the time required to do things properly? Open thinking has three core elements:

1. “Creative Thinking”

Creative thinking covers “ideation and reflection,” which lead to better ideas. Mythologist Joseph Campbell became famous for his detailed, insightful discussion of cultural archetypes. His most notable example is the “hero” – the inspiration for the Luke Skywalker character in the Star Wars film franchise. Campbell gave himself time to reflect deeply, dropping out of grad school to travel and think before producing his classic, The Hero with a Thousand Faces.

Open thinkers are willing to “wander” and take the time they need to think things through. Taking time to think means making time for daydreaming, an essential component of creativity. When you dream, you stop and observe. You spend time thinking. Daydreaming helps you figure out new solutions and processes. For an aspiring or practicing open thinker who wants to pause and reflect, time becomes the most valuable commodity. Don’t exploit your time – as most organizations want you to do. Explore it. Set up a quality time-management system to protect your time. Don’t overly commit yourself.

Free up your day as much as possible. When you can, farm out nuts-and-bolts tasks to people on your team. Move your focus from the minutiae to the big picture.

2. “Critical Thinking”

Critical thinking centers around analysis and judgment; it generates better decision making. Everyone has cognitive biases that get in the way of clear and logical thinking. Learn your biases, and compensate for them. Challenge your thinking and the conclusions you reach. Seek new ideas and information. Welcome opposing or nonconforming opinions. Collaborating with your colleagues helps promote open thinking. Ask people you trust and respect for their suggestions and advice. The more people you involve in your decision making, the better your decisions will be – within limits. As you think through new approaches and concepts, realize that failure isn’t a negative for people or organizations if you and your company evaluate your mistakes. Failure can have value as a learning

experience. It can help you diagnose where your personal or corporate critical thinking may have gone off the rails. To make the most of daily processes and activities, as well as successes or failures, organizational leaders must be open thinkers who understand the crucial leadership qualities that go into critical thinking.

3. “Applied Thinking”

Applied thinking means acting on your decisions. Applied thinking actualizes your “commitment to execute a decision.” It doesn’t focus on “what” to do, but on “how” to do it. To get the results you want, the “how” always counts. Set clear goals everyone on your team can understand. To inspire your colleagues to develop their own solutions, support the solutions they suggest and implement them in a process of planned action. Demonstrate your empathy and understanding about their struggle to find those solutions. Because external conditions will change constantly, remain flexible and supportive. Don’t assume your applied-thinking solutions will always work well or smoothly. They won’t. Expect snags along the way. In any thoughtful endeavor, “hiccups and curve balls” come with the territory. Avoid letting your organization become a “factory of actions.” Keeping everyone looking super busy all the time may generate a veneer of efficiency, but busywork isn’t efficient. You want your employees to be thoughtful in their actions. That can’t happen when everybody is moving 100 miles an hour. Help your employees and colleagues focus on the long term, not the short term.

Applied Thinking at Your Organization 

As you exercise applied thinking, “be ruthless about the long term.” Yes, you must accomplish things today, but don’t get seduced by the apparent success of constant action. You may, by reflex, want to prioritize immediate actions, and you must stay vigilant about the short term, but always ask how what you do today will affect your core purpose and the

shape of your future. To keep a close watch on the short term while staying aware of the long term, refine your in-house organizational practices, such as “calendar etiquette” and managing time. Recognize that information and processes will become obsolete, sometimes more rapidly than you can imagine. So while you must accept mistakes, the way you respond to these inevitable aspects of doing business will highlight the efficacy – or lack of efficacy – in your applied-thinking. Build an agile, resilient, tolerant culture; avoid rigid thinking.

Open Thinker: Chef Peter Gilmore

Australian executive chef Peter Gilmore plans and supervises meal preparation at two award-winning restaurants in Sidney, Australia: the Quay and Bennelong. The way he works exemplifies open thinking. He applies “creative visualization” when he plans a new dish, meal or menu. During this stage, he eschews practicality because it would limit his thinking, yet he never forgets that the dishes he dreams up must become workable menu items. He balances reflection and action. His dishes embody open thinking’s dreaming, deciding and doing aspects.

To retain his emerging ideas, Gilmore writes everything down. He tests new recipes, moving among creative, critical and applied thinking. Gilmore depends on collaboration with his kitchen team to develop, formalize and finalize all aspects of a dish. He documents the preparation process and the cost of new dishes. He works out how to explain them to the front-of-house team and helps the waiters describe the “emotion and intent” of a new dish to clients. Gilmore’s kitchens function as testing and proving grounds for open thinking.

“10 Essential Guidelines for Open Thinking”

Follow these 10 principles to exercise open thinking:

1. Allow yourself plenty of time to think clearly and comprehensively. Never rush your thinking. Open thinking is a “slow thinking movement.”

2. Too much thinking, rethinking and re-rethinking can become self-defeating. So can too much collaboration. After careful thought and conversation, move ahead decisively.

3. Never take action just to take action. Be thoughtful about what you do and decide what not to do.

4. Be flexible in your thinking. Let knowledge shape your ideas and opinions.

5. Write down your best thoughts. Idea flow is a continuing resource.

6. Be systematic and organized in everything you say and do. Scatterbrains are ineffective thinkers and planners.

7. You can’t be creative if you’re always busy. Take breaks to refresh yourself and create time for new ideas to emerge.

8. Dig for information to enable proper analysis and informed decision making. Never be hasty. Don’t settle for insufficient data. Lisa Helps, mayor of Victoria, British Columbia, thinks people often leap to premature conclusions after reading “three things on Facebook.” She faces each situation ready to take in meaningful information.

9. Maintain your focus. Don’t succumb to time-wasting distractions.

10.As an open thinker, include time to dream, decide and do every day.

Agility and Flexibility

Open thinkers stay flexible and adaptable. Dion Hinchcliffe, chief strategy officer at 7Summits – an online solutions provider – says to try new ideas if old ones don’t work.

He developed a five-step system for flexible problem solving:

1. Develop an idea or solution.

2. Try your idea. Experiment. If you fail, fail quickly.

3. Make the most refined decision possible. Continue to problem solve.

4. If you can’t find a solution, put the current problem on the shelf. Focus on a new problem and try to solve it instead.

5. Maintaining a cycle fuels flexibility. Be ready to return to the original problem. This process educates you continually.

John Dalla Costa, founder of Toronto’s Centre for Ethical Orientation, says open thinkers have three traits:

1. “Courage” – Open thinkers connect with new ideas, even if those ideas run counter to their current worldview.

2. “Responsibility” – Open thinkers accept new, verifiable information and welcome new data that move them closer to an ultimate truth.

3. “Fairness” – Open thinkers know investigation and experimentation often involve mistakes, which can be the greatest teachers of all.

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