From Projects to Purpose: Transforming Portfolio Management with Outcome-Based Thinking

 

In a previous post, we escaped the "Tyranny of the Timesheet" and explored the exciting potential of Outcome-Based Management (OBM). We saw how this revolutionary approach shifts the focus from clocking hours to achieving meaningful results, empowering individuals and fostering a culture of trust and autonomy.

Now, let's dive deeper into the core principles of OBM and discover how it can unleash the true potential within your organization.

Outputs vs. Outcomes vs. Impact: What's the Difference?

Imagine this: your team delivers a shiny new feature for your product (output). Users love it, and customer satisfaction scores soar (outcome). This, in turn, leads to increased sales and a boost in revenue (impact).

See the difference? It's not enough to simply do things; we need to understand the why behind our actions and the ripple effects they create.

• Outputs are the tangible deliverables – the reports, the features, the completed tasks. They are the "what" we produce.
• Outcomes are the changes that occur as a result of those outputs. They are the "why" behind our work – increased customer satisfaction, improved efficiency, reduced risk.
• Impact is the ultimate effect on the organization or society – increased revenue, improved brand reputation, positive social change.

OBM is about aligning our efforts with desired outcomes and ultimately, creating a positive impact. It's about moving beyond checking boxes and focusing on creating real value.

The Motivation Equation: Why OBM Fuels High Performance

Think back to the last time you felt truly motivated at work. Were you driven by the fear of a looming deadline or the promise of a bonus? Or was it something deeper – a sense of purpose, the thrill of mastering a new skill, the freedom to explore your own ideas?

Daniel Pink, in his groundbreaking book "Drive," argues that true motivation comes from three key psychological needs:

• Autonomy: The desire to direct our own lives.
• Mastery: The urge to get better and better at something that matters.
• Purpose: The yearning to do what we do in the service of something¹ larger than ourselves.

OBM taps directly into these intrinsic motivators. By focusing on outcomes, it gives individuals the autonomy to choose their own path and approach. It encourages continuous learning and development, fostering a sense of mastery. And by connecting individual efforts to organizational goals, it provides a clear sense of purpose.

In a world of knowledge work, where creativity and innovation are paramount, OBM is the key to unlocking the full potential of your team.

OBM and Portfolio Management: A Powerful Partnership

Now, you might be thinking, "This all sounds great, but how does OBM fit within the structured world of Portfolio Management?"

The answer lies in aligning your portfolio with strategic outcomes. Instead of just managing projects, a strategic PMO can use OBM to:

• Prioritize initiatives: Focus on projects that directly contribute to key organizational outcomes.
• Allocate resources effectively: Invest in initiatives that deliver the greatest impact.
• Monitor performance: Track progress towards outcomes, not just adherence to timelines and budgets.
• Foster a culture of continuous improvement: Regularly review outcomes and adjust strategies accordingly.

By embedding OBM principles into your portfolio management processes, you can ensure that every project, every initiative, is driving your organization towards its strategic goals.

Implementing OBM: A Roadmap to Success

Ready to embrace the power of outcomes? Here's a practical roadmap to guide your organization's transition to OBM:

1. Define clear, measurable outcomes: Start by identifying the specific changes you want to see. What are your organization's strategic goals? How can you translate these goals into tangible outcomes?
2. Communicate the "why": Help everyone understand the purpose behind the shift to OBM. Why is it important? How will it benefit the organization and its employees?
3. Empower your teams: Give individuals the autonomy to choose their own approach to achieving outcomes. Encourage experimentation and innovation.
4. Foster a culture of trust and accountability: Create an environment where people feel safe to take risks and learn from their mistakes.
5. Provide the right tools and support: Ensure your teams have the resources and training they need to succeed.
6. Measure and track progress: Regularly monitor outcomes and celebrate successes. Use data to inform decision-making and drive continuous improvement.

Real-world example: Imagine a software development team transitioning to OBM. Instead of focusing solely on delivering features on time and within budget, they start by defining desired outcomes, such as increasing user engagement or improving customer satisfaction. They then have the autonomy to choose the best approach to achieve these outcomes, whether it's through new features, improved user experience, or enhanced customer support.

Embracing the Future of Work

Outcome-Based Management is more than just a management trend; it's a fundamental shift in how we think about work. It's about empowering individuals, fostering collaboration, and focusing on creating real value.

By embracing OBM, you can escape the tyranny of the timesheet, unlock the true potential of your team, and build a more purposeful and fulfilling future of work.

Beyond the Weeds: Cultivating AI Adoption with "Wild Gardens"

In a world grappling with the rise of AI, one thing is clear: simply deploying the technology isn't enough. We need to cultivate its adoption. At a recent Conference Board session, Christopher Hernandez, Corporate VP at Microsoft, offered a compelling vision for how to do just that. He introduced the concept of "wild gardens" and "curated gardens" to illustrate the power of balancing organic exploration with structured guidance in AI adoption. This approach not only unlocks innovation but also fosters a more inclusive and fulfilling work environment, transforming the fear surrounding AI into a blueprint for collaborative success.

The Garden Metaphor Explained

Hernandez's analogy is simple yet powerful:

• Curated Gardens: These represent the traditional, structured approach to implementing AI. Think formal training programs, standardized frameworks, and top-down directives. While essential for consistency and control, they can sometimes stifle creativity and individual initiative.
• Wild Gardens: Imagine employees given the freedom to explore AI tools, experiment with applications, and self-organize around their own interests. These "wild gardens" are fertile ground for organic learning, innovation, and a sense of ownership.

But "Wild" Doesn't Mean "Untamed"

It's crucial to understand that "wild gardens" aren't about leaving employees to fend for themselves in an AI jungle. As Hernandez emphasizes, these organic initiatives still require nurturing. This means providing:

• Frameworks: Clear guidelines and ethical considerations to ensure responsible AI development and use.
• Formal Education: Basic training and resources to equip employees with foundational AI knowledge.
• Communities of Practice: Platforms for collaboration, knowledge sharing, and peer support.

The Roots of Successful Change

This balanced approach aligns beautifully with established change management principles. Consider Prosci's ADKAR model, which emphasizes Awareness, Desire, Knowledge, Ability, and Reinforcement. "Wild gardens" excel at fostering Desire by allowing employees to experiment and discover the value of AI firsthand. They build Knowledge and Abilityorganically through hands-on exploration and peer learning. Meanwhile, "curated gardens," with their formal training and guidelines, provide the necessary Awareness and Reinforcement to ensure alignment and scale.

Similarly, Kotter's 8-Step Change Model highlights the importance of creating a sense of urgency, forming a powerful guiding coalition, and generating short-term wins. "Wild gardens" can be hotbeds for generating those short-term wins, showcasing the immediate benefits of AI in a way that excites and motivates the broader organization.

Cross-Pollination: Where Magic Happens

Imagine a traditional corporate training program on a new AI tool (your "curated garden"). It's structured, informative, maybe even a little dry. Now picture a group of employees, intrigued by the potential of this tool, coming together to explore its capabilities in their own way (your "wild garden"). They're sharing ideas, experimenting with applications, and even stumbling upon innovative solutions that the formal training never anticipated. That's the magic of "wild gardens" – they unleash the collective ingenuity of your workforce.

Cultivating a Thriving AI Ecosystem

By nurturing both "wild" and "curated" approaches, we can create a dynamic ecosystem for AI adoption. Leaders must provide the framework, education, and community support to empower these "wild gardens" to flourish. In turn, these organic initiatives will infuse the formal structures with new energy, insights, and ultimately, greater success. It's time to move beyond the fear of AI and embrace a future where technology and human ingenuity work hand in hand.

Beyond the Hype: A Look at the Rich History and Diverse Capabilities of AI

Artificial intelligence (AI) has rapidly become one of the most talked-about and hyped technologies of our time. From self-driving cars to AI-powered chatbots, AI is everywhere. But amidst all the excitement, it's easy to forget that AI is not a new phenomenon. In fact, it has a rich history that spans several decades.

This blog post aims to cut through the hype and provide a deeper understanding of AI, its diverse capabilities, and its potential impact on society. We'll explore the evolution of AI, highlighting key milestones and showcasing how companies have been leveraging AI long before the advent of generative AI (GenAI). We'll also delve into the limitations and ethical considerations surrounding GenAI, providing a balanced perspective on this powerful technology.

What is AI?

At its core, AI involves creating intelligent agents – systems that can reason, learn, and act autonomously. It's about imbuing machines with cognitive abilities, enabling them to perform tasks that typically require human intelligence.

What is GenAI?

GenAI is a subset of AI that focuses on generating new content, such as text, images, music, and code. It has gained significant attention recently due to its ability to create human-quality outputs, but it's important to remember that it's just one piece of the broader AI landscape.

Cautions and Limitations of GenAI

Despite its impressive capabilities, GenAI has significant limitations that must be understood:

Technical Limitations

1. Hallucination
1. Generation of convincing but false information
2. Inability to verify factual accuracy
3. Confusion with similar but distinct concept
2. Context Understanding
1. Difficulty with nuanced context
2. Limited understanding of cause and effect
3. Struggles with abstract reasoning
3. Resource Intensity
1. Massive computational requirements
2. High energy consumption
3. Substantial training costs

AI vs. GenAI

Understanding the distinction between AI and GenAI is crucial for several reasons. First, it helps us appreciate the full spectrum of AI capabilities beyond just content generation. Second, it allows us to make informed decisions about when and where to apply different AI techniques. Finally, it enables us to have a more nuanced discussion about the potential benefits and risks of AI.

A Brief History of AI

The roots of AI can be traced back to the 1950s, when researchers first began exploring the possibility of creating machines that could think. Here's a brief timeline highlighting some of the key milestones in the evolution of AI:

Year	Event	Impact
1950	Alan Turing proposes the Turing Test, a benchmark for machine intelligence.	Laid the foundation for AI by defining a standard for machine intelligence.
1956	The term "artificial intelligence" is coined at the Dartmouth Summer Research Project on Artificial Intelligence.	Marked the official birth of AI as a field of study.
1980	Rise of machine learning and neural networks.	Enabled AI systems to learn from data and improve their performance over time.
1986	First successful backpropagation implementation	Revolutionary advancement in training neural networks
2010	Deep learning breakthroughs in image recognition and natural language processing.	Led to significant advancements in areas like computer vision and machine translation.
2012	AlexNet breakthrough	Revolutionized computer vision and deep learning
2017	Transformer architecture introduction	Revolutionized natural language processing
2020	Rise of large language models (LLMs) like GPT-3 and LaMDA.	Enabled AI to generate human-quality text, translate languages, and answer questions in a comprehensive manner.

NOTE: There are many more milestones in the evolution of AI.  For a fuller list, see here. 

Examples of AI in Action (Before the GenAI Hype)

Long before GenAI took center stage, companies were already leveraging various AI techniques to enhance their products and services. Here's an expanded timeline with more diverse examples, including some that utilize alternative AI techniques:

Year	Company	AI Application	AI Type
1990	Amazon	Product recommendations	Machine Learning (Collaborative Filtering)
1998	Google	PageRank algorithm for search engine ranking	Machine Learning, Network Analysis
2000	Yahoo!	Spam filtering in Yahoo! Mail	Machine Learning (Bayesian filtering)
2002	iRobot	Roomba robotic vacuum cleaner	Robotics, Machine Learning (Navigation and Obstacle Avoidance)
2006	Netflix	Personalized movie recommendations	Machine Learning (Collaborative Filtering, Content-Based Filtering)
2011	Apple	Siri voice assistant	Natural Language Processing
2013	Spotify	Personalized music recommendations	Machine Learning (Collaborative Filtering, Natural Language Processing)
2015	Google	Open sources TensorFlow, a machine learning library	Machine Learning (Deep Learning)
2017	Waymo (Google)	Testing self-driving cars	Computer Vision, Machine Learning, Robotics, Optimization, Reinforcement Learning
2018	UPS	ORION route optimization system for delivery drivers	Optimization (specifically, constraint programming)

Composite AI: Combining Strengths for Enhanced Capabilities

Imagine an AI system that can not only understand your spoken requests but also reason about them, access relevant information from a vast knowledge base, and then generate a creative solution. That's the power of Composite AI – the art of blending different AI techniques to achieve more sophisticated and robust outcomes.

Composite AI is like assembling a team of experts with diverse skills to tackle a complex challenge. It's about leveraging the strengths of different AI approaches, such as machine learning, natural language processing, knowledge graphs, and even rule-based systems, to create a synergistic whole that is greater than the sum of its parts.

Current and Future Applications of Composite AI

• Enhanced Chatbots: Imagine chatbots that can not only understand your questions but also reason about them, access relevant information, and provide personalized responses based on your preferences and past interactions.
• Personalized Learning: Composite AI can create adaptive learning systems that cater to individual student needs by analyzing their learning styles, identifying knowledge gaps, and providing tailored content and feedback.
• Advanced Robotics: Robots that can navigate complex environments, interact with humans naturally, and perform intricate tasks with dexterity and precision.
• Drug Discovery and Development: Composite AI can accelerate the process of drug discovery by analyzing vast amounts of biomedical data, identifying potential drug candidates, and predicting their efficacy and safety.

Navigating the AI Landscape for Business Success

As we stand at the intersection of AI's rich history and its promising future, one thing becomes clear: the true power of AI lies not in replacing human intelligence, but in augmenting it. By understanding both the capabilities and limitations of different AI approaches, we can harness this technology to enhance human potential while navigating its challenges responsibly.

The future of AI will likely be shaped by:

1. Integration of multiple AI techniques
2. Development of more efficient and sustainable models
3. Enhanced focus on ethical AI development
4. Greater emphasis on human-AI collaboration
5. Continued innovation in specialized applications

The key to successful AI implementation lies in maintaining a balanced perspective: embracing the technology's potential while remaining mindful of its limitations and ethical implications. As we move forward, the focus should be on developing AI systems that are not just powerful, but also responsible, sustainable, and aligned with human values.

Disclaimer: This blog post was created with the assistance of generative AI. Although reviewed and edited, I am human and may still occasionally make mistakes. Please use this information responsibly and verify any critical details independently.