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Alan Patterson is a consultant at Camden Consulting Group and president of Mentoré, with extensive experience providing change management, leadership development, and executive coaching services to senior executives in a wide range of industries. Having guided senior executives at more than a dozen Fortune 500 companies in rethinking how best to elevate their talent, he is an expert at driving effective organizational change. Alan also co-designed and co-facilitates Camden’s Organizational Leaders Program, which provides technical experts with the support to make the shift to broader business leaders. He brings 30 years of international consulting experience to his executive coaching engagements. Alan’s expertise has been tapped by many global and national organizations, including Anheuser-Busch, Biogen Idec, CVS, Federal Reserve Bank, Johnson & Johnson, Hewlett-Packard, Major League Baseball, Merck, Sea World, and Williams-Sonoma.A published author, Alan has been a featured speaker and workshop leader for several national associations, including serving as a national expert resource for the American Institute for CPAs and many state CPA associations.

Alan Patterson

April 2016 - TD Magazine

3D Leadership

Though challenging, it is important to learn how to delegate. Bob Hewes and Alan Patterson outline six steps for delegating successfully.

Delegate or Burn Out: Knowing When It’s Time to Let Go!

Managers need to focus on the organizational climate to better understand which employees are engaged—and what they do.

Employee Engagement’s Secrets Revealed

When No Feedback is Bad News

When No Feedback is Good News

September 2012 - TD Magazine

A Three-Pronged Approach to Leadership Development

A New Pattern in Employee Development

Implementing a successful coaching program within an organization requires a thorough understanding of the current culture and identifying areas where coaching can be most beneficial. Organizational culture shapes how work gets done and influences everything from decision making to interpersonal interactions. Here’s a concise guide to assessing organizational readiness, analyzing culture, and identifying where coaching could be integrated.Understanding Organizational CultureOrganizational culture is the set of shared values, beliefs, and behaviors that shape how work gets done within a company. To effectively understand an organization’s culture, it’s crucial to consider several key aspects:Core values and beliefs: Examine the organization’s core values and beliefs. How are these values communicated and reinforced? Do employees understand and align with these values? Understanding the foundational principles that guide behavior is essential for identifying how coaching can align with and support these values.Behavioral norms: Consider the unwritten rules that govern behavior. How do employees interact with each other and with management? Are there specific behaviors that are encouraged or discouraged? Understanding these norms can reveal areas where coaching can help modify or reinforce behaviors.Leadership styles: Leadership styles play a significant role in shaping organizational culture. What leadership styles are predominant? How do leaders communicate and make decisions? Identifying these styles can highlight areas where coaching can develop more effective leadership practices.Employee engagement: Employee engagement is another critical aspect. How engaged are employees in their work? What motivates them, and how is their performance recognized? High levels of engagement often indicate a readiness for coaching initiatives aimed at enhancing job satisfaction and performance.“Leadership has a profound impact on an organization’s culture, shaping the environment and influencing employee behavior. Leaders set the tone through their actions, attitudes, and communication styles, which employees often emulate. When leaders model coaching behaviors such as active listening, these values become ingrained in the organization’s culture.”  —Alan Fine, Performance Coach and Bestselling AuthorAnalyzing Organizational CultureTo gain a comprehensive understanding of the organizational culture, a combination of methods should be employed:Surveys and questionnaires: Conduct surveys and questionnaires to gain insights into employees' perceptions of the organizational culture. Questions should cover job satisfaction, leadership effectiveness, communication quality, and overall workplace environment. This data can reveal patterns and areas needing attention.Focus groups and interviews: Engage in focus groups and one-on-one interviews to explore cultural dynamics more deeply. These conversations can uncover underlying issues and provide a more nuanced understanding of the culture. Direct feedback from employees can highlight specific areas where coaching could make a significant impact.Observations: Observe day-to-day interactions and behaviors within the organization offers a firsthand view of the cultural landscape. Pay attention to how meetings are conducted, how decisions are made, and how employees collaborate. These observations can reveal discrepancies between stated values and actual practices.Document analysis: Review internal documents, such as company policies, training materials, and communication memos, can reveal the formal and informal aspects of the culture. This analysis helps identify the alignment between stated values and actual practices, providing a clearer picture of where coaching can be integrated.Identifying Areas for Coaching IntegrationOnce the organizational culture has been analyzed, the next step is to identify areas where coaching can drive improvement and support cultural alignment:Leadership development: Coaching can be a powerful tool for developing leadership skills. Focus on enhancing emotional intelligence, promoting effective communication, encouraging adaptive leadership styles, and fostering collaboration. Developing leaders who can inspire and engage their teams is crucial for a healthy organizational culture.Employee engagement and motivation: Coaching can help boost employee engagement by providing personalized development plans, offering support for career growth, and addressing individual motivational drivers. Engaged employees are more likely to contribute positively to the organizational culture and achieve higher performance levels.Team dynamics and collaboration: Improving team dynamics through coaching can facilitate better collaboration and teamwork. Focus on enhancing problem-solving skills, building trust within teams, and fostering a culture of open communication. Effective teams are the backbone of a successful organization.Change management: Coaching can support employees and leaders through organizational changes by helping them adapt to new processes, manage resistance, and build resilience. Ensuring alignment with the organization’s strategic goals during times of change is critical for maintaining a positive culture.Performance improvement: Addressing performance issues through coaching involves identifying and overcoming barriers, setting clear goals, and providing ongoing feedback. Coaching can help employees achieve their full potential, leading to improved overall performance.Assessing organizational readiness and analyzing the current culture is crucial for successfully integrating coaching. Organizations can create tailored coaching programs that drive meaningful change and enhance overall performance by understanding cultural dynamics and identifying areas where coaching can have the most impact. With a well-planned implementation strategy, coaching can become vital to the organization’s growth and success.Following these steps, along with the right tools and resources, organizations can create a coaching culture that fosters continuous improvement and sustained success.

Assessing Organizational Readiness: Analyzing Culture and Identifying Coaching Opportunities

 _“Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower.”—_Alan Kay Raymond Kurzweil, the director of engineering at Google and a renowned futurist predicted in The Singularity Is Near: When Humans Transcend Biology that by 2029 computers will be cleverer than humans. While such a mindboggling prediction can be a matter of debate, there is little doubt that in the future machines will be much more sophisticated than what they are today. Indeed, these smart machines will be able to learn on their own—without human intervention—using technology!   Erik Brynjolfsson and Andrew McAfee, co-authors of The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies say, “Technologies that used to seem like science fiction are becoming everyday reality.” Machines that can use such technologies to learn and autonomously adapt to the changes will cause serious disruption to well-established business models. Likewise, Gartner predicts that “by 2017, a significant and disruptive digital business will be launched that was conceived by a computer algorithm.” All of these predictions are based on one common observation: Machines can learn exceptionally fast, and it is possible to build machines that can learn without any active human supervision. In fact, several of the top 10 emerging technology trends of 2015, as predicted in a recent report by World Economic Forum (WEF), are technologies for learnable machines. Neuromorphic Technology With advances in machine learning and rapid processing of high volumes of data, the next generation of powerful computing will be built on chips that will process information by mirroring the brain's architecture. As explained in the WEF report, such computers will be energy efficient and will have much more computing power than a conventional CPU (central processing unit). Moreover, these computers will be able to “anticipate and learn”—instead of just responding in a pre-programmed fashion. IBM has already demonstrated this technology in a prototype form through their million-neuron TrueNorth chip in August 2014. Gartner has predicted that by 2017, 10 percent of computers will be learning rather than processing. The report also mentions, that, “The Defense Advanced Research Project Agency (DARPA) and Ecole Polytechnique Federal de Lausanne are funding the SyNAPSE and The Human Brain projects, respectively, fostering neuromorphic computing techniques intended for pattern recognition applications, including facial recognition, object recognition, drug discovery and medical diagnostics.” Emergent Artificial Intelligence We are already seeing significant advancements in artificial intelligence through self-driving cars and automated flying drones. Emergent artificial intelligence takes this step further with machines that can learn automatically by using large volumes of data. Carnegie Mellon University's NELL (the Never-Ending Language Learning) computer system "not only reads facts by crawling through hundreds of millions of web pages, but attempts to improve its reading and understanding competence in the process in order to perform better in the future." Machines with emergent artificial intelligence are not only capable of processing huge amount of information in a short time frame and arrive at an optimal solution, they also can avoid human errors and emotional biases. However, many experts are raising early warnings for potential pitfalls associated with such super-intelligent machines, which will not only replace the jobs of many human workers, but also may eventually “overcome and enslave humans.”    Programmable Material In case of 3D printing, computer readable designs are fed to a machine that can process the design and produce a finished object using additive manufacturing technology. Medical applications of 3D printing have reached such an advanced stage that even a living tissue or a cell can be printed in this process. Other fields of application of 3D printing include automotive, aerospace, electronics as well as housing and construction. Researchers at MIT have pioneered a technology in which the finished objects will be made from “smart materials” that can be pre-programmed. This new technology is called 4D printing, because it incorporates the additional dimension of “time.”   “While 3D printing is purposed around building static items, 4D printing employs dynamic materials that evolve or adapt to their external environment in real-time and in direct response to changing conditions. This ability to transform is embedded within the material itself. Objects created through 4D printing are assemble and alter their structure when the material of which they are composed comes in contact with different conditions, such as moisture or humidity. The ultimate goal is to create stimuli-responsive components, materials that modify their own form or self-assemble new patterns automatically and in predictive ways,” explains a September 2014 Georgetown Journal of International Affairs article. Advanced Robotics Next-generation robots will learn to collaborate with humans to become their co-workers. They will no longer be used only in factory assembly lines and separated from humans by safety cages. The WEF report talks about an example: “In Japan, robots are being trialed in nursing roles: they help patients out of bed and support stroke victims in regaining control of their limbs.” Boston Dynamics, a wholly owned subsidiary of Google, builds “advanced robots with remarkable behavior: mobility, agility, dexterity and speed.” These robots can walk, run, and climb in a variety of terrains and surfaces by learning to deal with environmental variations. An interesting Tech  Times article discusses how researchers at University of Maryland are looking at whether robots can learn a skill, like cooking, by watching YouTube videos.  Apart from the five technologies mentioned above, technologies that are going to impact our lives in a profound manner are Big Data, Analytics and Internet of Things (IoT). Among these, analytics will be a de-facto technology for a learning machines. Embedded Analytics According to Gartner’s “Top 10 Strategic Technology Trends for 2015,” analytics will be embedded in every app so that it will continue to learn and develop insights on a continuous basis. Such technology-based learning will lead to “advanced, pervasive, and invisible analytics,” through which the apps will be able to intelligently monitor events, gather data, and learn about the context to take better decisions. By continuously analyzing data and by developing insights, these apps will create deep understanding of the business domains. Chris Argyris, in his seminal HBR article on organizational learning, “Teaching Smart People How to Learn,” coined the terms single loop and double loop learning. Argyris explained single loop learning with an analogy of a thermostat that automatically turns on the moment the temperature of the room drops below 68 degrees. “A thermostat that could ask, ‘Why am I set at 68 degrees?’ and then explore whether or not some other temperature might more economically achieve the goal of heating the room would be engaging in double-loop learning.” The article raised the issue of how highly skilled professionals can be very good at single loop learning but miserably fail at double loop learning. With the advancement of technology, we can now have thermostats that are intelligent enough to use double loop learning. Nest Labs, a Google company, specializes in making sensor driven self-learning thermostats and smoke detectors.   Bottom line: Machines will no longer remain dependent on their masters for learning. More importantly, like humans, learning machines will be able to use technology to enhance their skills. B. F. Skinner once said, “The real problem is not whether machines think but whether men do.” The time has come to seriously ponder over the implications of this remark.

Learning Technologies for Machines

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