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To Solve a Tough Problem, Reframe It

• Julia Binder
• Michael D. Watkins

Research shows that companies devote too little effort to examining problems before trying to solve them. By jumping immediately into problem-solving, teams limit their ability to design innovative solutions.

The authors recommend that companies spend more time up front on problem-framing, a process for understanding and defining a problem. Exploring different frames is like looking at a scene through various camera lenses while adjusting your angle, aperture, and focus. A wide-angle lens gives you a very different photo from that taken with a telephoto lens, and shifting your angle and depth of focus yields distinct images. Effective problem-framing is similar: Looking at a problem from a variety of perspectives helps you uncover new insights and generate fresh ideas.

This article introduces a five-phase approach to problem-framing: In the expand phase, the team identifies all aspects of a problem; in examine, it dives into root causes; in empathize, it considers key stakeholders’ perspectives; in elevate, it puts the problem into a broader context; and in envision, it creates a road map toward the desired outcome.

Five steps to ensure that you don’t jump to solutions

Idea in Brief

The problem.

Research shows that most companies devote too little effort to examining problems from all angles before trying to solve them. That limits their ability to come up with innovative ways to address them.

The Solution

Companies need a structured approach for understanding and defining complex problems to uncover new insights and generate fresh ideas.

The Approach

This article introduces a five-phase approach to problem-framing: In the expand phase, the team identifies all aspects of a problem; in examine, it dives into root causes; in empathize, it considers key stakeholders’ perspectives; in elevate, it puts the problem into a broader context; and in envision, it creates a road map toward the desired outcome.

When business leaders confront complex problems, there’s a powerful impulse to dive right into “solving” mode: You gather a team and then identify potential solutions. That’s fine for challenges you’ve faced before or when proven methods yield good results. But what happens when a new type of problem arises or aspects of a familiar one shift substantially? Or if you’re not exactly sure what the problem is?

Research conducted by us and others shows that leaders and their teams devote too little effort to examining and defining problems before trying to solve them. A study by Paul Nutt of Ohio State University, for example, looked at 350 decision-making processes at medium to large companies and found that more than half failed to achieve desired results, often because perceived time pressure caused people to pay insufficient attention to examining problems from all angles and exploring their complexities. By jumping immediately into problem-solving, teams limit their ability to design innovative and durable solutions.

When we work with organizations and teams, we encourage them to spend more time up front on problem-framing, a process for understanding and defining a problem. Exploring frames is like looking at a scene through various camera lenses while adjusting your angle, aperture, and focus. A wide-angle lens will give you a very different photo from that taken with a telephoto lens, and shifting your angle and depth of focus yields distinct images. Effective problem-framing is similar: Looking at a problem from a variety of perspectives lets you uncover new insights and generate fresh ideas.

As with all essential processes, it helps to have a methodology and a road map. This article introduces the E5 approach to problem-framing—expand, examine, empathize, elevate, and envision—and offers tools that enable leaders to fully explore the problem space.

Phase 1: Expand

In the first phase, set aside preconceptions and open your mind. We recommend using a tool called frame-storming, which encourages a comprehensive exploration of an issue and its nuances. It is a neglected precursor to brainstorming, which typically focuses on generating many different answers for an already framed challenge. Frame-storming helps teams identify assumptions and blind spots, mitigating the risk of pursuing inadequate or biased solutions. The goal is to spark innovation and creativity as people dig into—or as Tina Seelig from Stanford puts it, “fall in love with”—the problem.

Begin by assembling a diverse team, encompassing a variety of types of expertise and perspectives. Involving outsiders can be helpful, since they’re often coming to the issue cold. A good way to prompt the team to consider alternative scenarios is by asking “What if…?” and “How might we…?” questions. For example, ask your team, “What if we had access to unlimited resources to tackle this issue?” or “How might better collaboration between departments or teams help us tackle this issue?” The primary objective is to generate many alternative problem frames, allowing for a more holistic understanding of the issue. Within an open, nonjudgmental atmosphere, you deliberately challenge established thinking—what we call “breaking” the frame.

It may be easy to eliminate some possibilities, and that’s exactly what you should do. Rather than make assumptions, generate alternative hypotheses and then test them.

Consider the problem-framing process at a company we’ll call Omega Soundscapes, a midsize producer of high-end headphones. (Omega is a composite of several firms we’ve worked with.) Omega’s sales had declined substantially over the past two quarters, and the leadership team’s initial diagnosis, or reference frame, was that recent price hikes to its flagship product made it too expensive for its target market. Before acting on this assumption, the team convened knowledgeable representatives from sales, marketing, R&D, customer service, and external consultants to do some frame-storming. Team members were asked:

• What if we lowered the price of our flagship product? How would that impact sales and profitability?
• How might we identify customers in new target markets who could afford our headphones at the current price?
• What if we offered financing or a subscription-based model for our headphones? How would that change perceptions of affordability?
• How might we optimize our supply chain and production processes to reduce manufacturing costs without compromising quality?

In playing out each of those scenarios, the Omega team generated several problem frames:

• The target market’s preferences have evolved.
• New competitors have entered the market.
• Product quality has decreased.
• Something has damaged perceptions of the brand.
• Something has changed in the priorities of our key distributors.

Each of the frames presented a unique angle from which to approach the problem of declining sales, setting the stage for the development of diverse potential solutions. At this stage, it may be relatively easy to eliminate some possibilities, and that’s exactly what you should do. Rather than make assumptions, generate alternative hypotheses and then test them.

See more HBR charts in Data & Visuals

Phase 2: Examine

If the expand phase is about identifying all the facets of a problem, this one is about diving deep to identify root causes. The team investigates the issue thoroughly, peeling back the layers to understand underlying drivers and systemic contributors.

A useful tool for doing this is the iceberg model, which guides the team through layers of causation: surface-level events, the behavioral patterns that drive them, underlying systematic structures, and established mental models. As you probe ever deeper and document your findings, you begin to home in on the problem’s root causes. As is the case in the expand phase, open discussions and collaborative research are crucial for achieving a comprehensive analysis.

Let’s return to our Omega Soundscapes example and use the iceberg model to delve into the issues surrounding the two quarters of declining sales. Starting with the first layer beneath the surface, the behavioral pattern, the team diligently analyzed customer feedback. It discovered a significant drop in brand loyalty. This finding validated the problem frame of a “shifting brand perception,” prompting further investigation into what might have been causing it.

Phase 3: Empathize

In this phase, the focus is on the stakeholders—employees, customers, clients, investors, supply chain partners, and other parties—who are most central to and affected by the problem under investigation. The core objective is to understand how they perceive the issue: what they think and feel, how they’re acting, and what they want.

First list all the people who are directly or indirectly relevant to the problem. It may be helpful to create a visual representation of the network of relationships in the ecosystem. Prioritize the stakeholders according to their level of influence on and interest in the problem, and focus on understanding the roles, demographics, behavior patterns, motivations, and goals of the most important ones.

Now create empathy maps for those critical stakeholders. Make a template divided into four sections: Say, Think, Feel, and Do. Conduct interviews or surveys to gather authentic data. How do various users explain the problem? How do they think about the issue, and how do their beliefs inform that thinking? What emotions are they feeling and expressing? How are they behaving? Populate each section of the map with notes based on your observations and interactions. Finally, analyze the completed empathy maps. Look for pain points, inconsistencies, and patterns in stakeholder perspectives.

Returning to the Omega case study, the team identified its ecosystem of stakeholders: customers (both current and potential); retail partners and distributors; the R&D, marketing, and sales teams; suppliers of headphone components; investors and shareholders; and new and existing competitors. They narrowed the list to a few key stakeholders related to the declining-sales problem: customers, retail partners, and investors/shareholders; Omega created empathy maps for representatives from each.

Here’s what the empathy maps showed about what the stakeholders were saying, thinking, feeling, and doing:

Sarah, the customer, complained on social media about the high price of her favorite headphones. Dave, the retailer, expressed concerns about unsold inventory and the challenge of convincing customers to buy the expensive headphones. Alex, the shareholder, brought up Omega’s declining financial performance during its annual investor day.

Sarah thought that Omega was losing touch with its loyal customer base. Dave was considering whether to continue carrying Omega’s products in his store or explore other brands. Alex was contemplating diversifying his portfolio into other consumer-tech companies.

As a longtime supporter of the brand, Sarah felt frustrated and slightly betrayed. Dave was feeling anxious about the drop in sales and the impact on his store’s profitability. Alex was unhappy with the declining stock value.

Sarah was looking for alternatives to the headphones, even though she loves the product’s quality. Dave was scheduling a call with Omega to negotiate pricing and terms. Alex was planning to attend Omega’s next shareholder meeting to find out more information from the leadership team.

When Omega leaders analyzed the data in the maps, they realized that pricing wasn’t the only reason for declining sales. A more profound issue was customers’ dissatisfaction with the perceived price-to-quality ratio, especially when compared with competitors’ offerings. That insight prompted the team to consider enhancing the headphones with additional features, offering more-affordable alternatives, and possibly switching to a service model.

Phase 4: Elevate

This phase involves exploring how the problem connects to broader organizational issues. It’s like zooming out on a map to understand where a city lies in relation to the whole country or continent. This bird’s-eye view reveals interconnected issues and their implications.

For this analysis, we recommend the four-frame model developed by Lee Bolman and Terrence Deal, which offers distinct lenses through which to view the problem at a higher level. The structural frame helps you explore formal structures (such as hierarchy and reporting relationships); processes (such as workflow); and systems, rules, and policies. This frame examines efficiency, coordination, and alignment of activities.

The human resources frame focuses on people, relationships, and social dynamics. This includes teamwork, leadership, employee motivation, engagement, professional development, and personal growth. In this frame, the organization is seen as a community or a family that recognizes that talent is its most valuable asset. The political frame delves into power dynamics, competing interests, conflicts, coalitions, and negotiations. From this perspective, organizations are arenas where various stakeholders vie for resources and engage in political struggles to influence decisions. It helps you see how power is distributed, used, and contested.

The symbolic frame highlights the importance of symbols, rituals, stories, and shared values in shaping group identity and culture. In it, organizations are depicted as theaters through which its members make meaning.

Using this model, the Omega team generated the following insights in the four frames:

Structural.

A deeper look into the company’s structure revealed siloing and a lack of coordination between the R&D and marketing departments, which had led to misaligned messaging to customers. It also highlighted a lack of collaboration between the two functions and pointed to the need to communicate with the target market about the product’s features and benefits in a coherent and compelling way.

Human resources.

This frame revealed that the declining sales and price hikes had ramped up pressure on the sales team, damaging morale. The demotivated team was struggling to effectively promote the product, making it harder to recover from declining sales. Omega realized it was lacking adequate support, training, and incentives for the team.

The key insight from this frame was that the finance team’s reluctance to approve promotions in the sales group to maintain margins was exacerbating the morale problem. Omega understood that investing in sales leadership development while still generating profits was crucial for long-term success and that frank discussions about the issue were needed.

This frame highlighted an important misalignment in perception: The company believed that its headphones were of “top quality,” while customers reported in surveys that they were “overpriced.” This divergence raised alarm that branding, marketing, and pricing strategies, which were all predicated on the central corporate value of superior quality, were no longer resonating with customers. Omega realized that it had been paying too little attention to quality assurance and functionality.

Phase 5: Envision

In this phase, you transition from framing the problem to actively imagining and designing solutions. This involves synthesizing the insights gained from earlier phases and crafting a shared vision of the desired future state.

Here we recommend using a technique known as backcasting. First, clearly define your desired goal. For example, a team struggling with missed deadlines and declining productivity might aim to achieve on-time completion rates of 98% for its projects and increase its volume of projects by 5% over the next year. Next, reverse engineer the path to achieving your goal. Outline key milestones required over both the short term and the long term. For each one, pinpoint specific interventions, strategies, and initiatives that will propel you closer to your goal. These may encompass changes in processes, policies, technologies, and behaviors. Synthesize the activities into a sequenced, chronological, prioritized road map or action plan, and allocate the resources, including time, budget, and personnel, necessary to implement your plan. Finally, monitor progress toward your goal and be prepared to adjust the plan in response to outcomes, feedback, or changing circumstances. This approach ensures that the team’s efforts in implementing the insights from the previous phases are strategically and purposefully directed toward a concrete destination.

Applying the Approach

Albert Einstein once said, “If I had one hour to solve a problem, I would spend 55 minutes thinking about the problem and five minutes thinking about the solution.” That philosophy underpins our E5 framework, which provides a structured approach for conscientiously engaging with complex problems before leaping to solutions.

As teams use the methodology, they must understand that problem-framing in today’s intricate business landscape is rarely a linear process. While we’re attempting to provide a structured path, we also recognize the dynamic nature of problems and the need for adaptability. Invariably, as teams begin to implement solutions, new facets of a problem may come to light, unforeseen challenges may arise, or external circumstances may evolve. Your team should be ready to loop back to previous phases—for instance, revisiting the expand phase to reassess the problem’s frame, delving deeper into an overlooked root cause in another examine phase, or gathering fresh insights from stakeholders in a new empathize phase. Ultimately, the E5 framework is intended to foster a culture of continuous improvement and innovation.

• JB Julia Binder is the director of the Center for Sustainable and Inclusive Business and a professor of sustainable innovation at IMD.
• Michael D. Watkins is a professor of leadership and organizational change at IMD , a cofounder of Genesis Advisers , and the author of The Six Disciplines of Strategic Thinking .

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Introducing Gemini: our largest and most capable AI model

Dec 06, 2023

[[read-time]] min read

Making AI more helpful for everyone

A note from Google and Alphabet CEO Sundar Pichai:

Every technology shift is an opportunity to advance scientific discovery, accelerate human progress, and improve lives. I believe the transition we are seeing right now with AI will be the most profound in our lifetimes, far bigger than the shift to mobile or to the web before it. AI has the potential to create opportunities — from the everyday to the extraordinary — for people everywhere. It will bring new waves of innovation and economic progress and drive knowledge, learning, creativity and productivity on a scale we haven’t seen before.

That’s what excites me: the chance to make AI helpful for everyone, everywhere in the world.

Nearly eight years into our journey as an AI-first company, the pace of progress is only accelerating: Millions of people are now using generative AI across our products to do things they couldn’t even a year ago, from finding answers to more complex questions to using new tools to collaborate and create. At the same time, developers are using our models and infrastructure to build new generative AI applications, and startups and enterprises around the world are growing with our AI tools.

This is incredible momentum, and yet, we’re only beginning to scratch the surface of what’s possible.

We’re approaching this work boldly and responsibly. That means being ambitious in our research and pursuing the capabilities that will bring enormous benefits to people and society, while building in safeguards and working collaboratively with governments and experts to address risks as AI becomes more capable. And we continue to invest in the very best tools, foundation models and infrastructure and bring them to our products and to others, guided by our AI Principles .

Now, we’re taking the next step on our journey with Gemini, our most capable and general model yet, with state-of-the-art performance across many leading benchmarks. Our first version, Gemini 1.0, is optimized for different sizes: Ultra, Pro and Nano. These are the first models of the Gemini era and the first realization of the vision we had when we formed Google DeepMind earlier this year. This new era of models represents one of the biggest science and engineering efforts we’ve undertaken as a company. I’m genuinely excited for what’s ahead, and for the opportunities Gemini will unlock for people everywhere.

Introducing Gemini

By Demis Hassabis, CEO and Co-Founder of Google DeepMind, on behalf of the Gemini team

AI has been the focus of my life's work, as for many of my research colleagues. Ever since programming AI for computer games as a teenager, and throughout my years as a neuroscience researcher trying to understand the workings of the brain, I’ve always believed that if we could build smarter machines, we could harness them to benefit humanity in incredible ways.

This promise of a world responsibly empowered by AI continues to drive our work at Google DeepMind. For a long time, we’ve wanted to build a new generation of AI models, inspired by the way people understand and interact with the world. AI that feels less like a smart piece of software and more like something useful and intuitive — an expert helper or assistant.

Today, we’re a step closer to this vision as we introduce Gemini , the most capable and general model we’ve ever built.

Gemini is the result of large-scale collaborative efforts by teams across Google, including our colleagues at Google Research. It was built from the ground up to be multimodal, which means it can generalize and seamlessly understand, operate across and combine different types of information including text, code, audio, image and video.

Gemini is also our most flexible model yet — able to efficiently run on everything from data centers to mobile devices. Its state-of-the-art capabilities will significantly enhance the way developers and enterprise customers build and scale with AI.

We’ve optimized Gemini 1.0, our first version, for three different sizes:

• Gemini Ultra — our largest and most capable model for highly complex tasks.
• Gemini Pro — our best model for scaling across a wide range of tasks.
• Gemini Nano — our most efficient model for on-device tasks.

State-of-the-art performance

We've been rigorously testing our Gemini models and evaluating their performance on a wide variety of tasks. From natural image, audio and video understanding to mathematical reasoning, Gemini Ultra’s performance exceeds current state-of-the-art results on 30 of the 32 widely-used academic benchmarks used in large language model (LLM) research and development.

With a score of 90.0%, Gemini Ultra is the first model to outperform human experts on MMLU (massive multitask language understanding), which uses a combination of 57 subjects such as math, physics, history, law, medicine and ethics for testing both world knowledge and problem-solving abilities.

Our new benchmark approach to MMLU enables Gemini to use its reasoning capabilities to think more carefully before answering difficult questions, leading to significant improvements over just using its first impression.

Gemini surpasses state-of-the-art performance on a range of benchmarks including text and coding.

Gemini Ultra also achieves a state-of-the-art score of 59.4% on the new MMMU benchmark, which consists of multimodal tasks spanning different domains requiring deliberate reasoning.

With the image benchmarks we tested, Gemini Ultra outperformed previous state-of-the-art models, without assistance from optical character recognition (OCR) systems that extract text from images for further processing. These benchmarks highlight Gemini’s native multimodality and indicate early signs of Gemini's more complex reasoning abilities.

See more details in our Gemini technical report .

Gemini surpasses state-of-the-art performance on a range of multimodal benchmarks.

Next-generation capabilities

Until now, the standard approach to creating multimodal models involved training separate components for different modalities and then stitching them together to roughly mimic some of this functionality. These models can sometimes be good at performing certain tasks, like describing images, but struggle with more conceptual and complex reasoning.

We designed Gemini to be natively multimodal, pre-trained from the start on different modalities. Then we fine-tuned it with additional multimodal data to further refine its effectiveness. This helps Gemini seamlessly understand and reason about all kinds of inputs from the ground up, far better than existing multimodal models — and its capabilities are state of the art in nearly every domain.

Learn more about Gemini’s capabilities and see how it works .

Sophisticated reasoning

Gemini 1.0’s sophisticated multimodal reasoning capabilities can help make sense of complex written and visual information. This makes it uniquely skilled at uncovering knowledge that can be difficult to discern amid vast amounts of data.

Its remarkable ability to extract insights from hundreds of thousands of documents through reading, filtering and understanding information will help deliver new breakthroughs at digital speeds in many fields from science to finance.

Gemini unlocks new scientific insights

Understanding text, images, audio and more

Gemini 1.0 was trained to recognize and understand text, images, audio and more at the same time, so it better understands nuanced information and can answer questions relating to complicated topics. This makes it especially good at explaining reasoning in complex subjects like math and physics.

Gemini explains reasoning in math and physics

Advanced coding

Our first version of Gemini can understand, explain and generate high-quality code in the world’s most popular programming languages, like Python, Java, C++, and Go. Its ability to work across languages and reason about complex information makes it one of the leading foundation models for coding in the world.

Gemini Ultra excels in several coding benchmarks, including HumanEval , an important industry-standard for evaluating performance on coding tasks, and Natural2Code, our internal held-out dataset, which uses author-generated sources instead of web-based information.

Gemini can also be used as the engine for more advanced coding systems. Two years ago we presented AlphaCode , the first AI code generation system to reach a competitive level of performance in programming competitions.

Using a specialized version of Gemini, we created a more advanced code generation system, AlphaCode 2 , which excels at solving competitive programming problems that go beyond coding to involve complex math and theoretical computer science.

Gemini excels at coding and competitive programming

When evaluated on the same platform as the original AlphaCode, AlphaCode 2 shows massive improvements, solving nearly twice as many problems, and we estimate that it performs better than 85% of competition participants — up from nearly 50% for AlphaCode. When programmers collaborate with AlphaCode 2 by defining certain properties for the code samples to follow, it performs even better.

We’re excited for programmers to increasingly use highly capable AI models as collaborative tools that can help them reason about the problems, propose code designs and assist with implementation — so they can release apps and design better services, faster.

See more details in our AlphaCode 2 technical report .

More reliable, scalable and efficient

We trained Gemini 1.0 at scale on our AI-optimized infrastructure using Google’s in-house designed Tensor Processing Units (TPUs) v4 and v5e. And we designed it to be our most reliable and scalable model to train, and our most efficient to serve.

On TPUs, Gemini runs significantly faster than earlier, smaller and less-capable models. These custom-designed AI accelerators have been at the heart of Google's AI-powered products that serve billions of users like Search, YouTube, Gmail, Google Maps, Google Play and Android. They’ve also enabled companies around the world to train large-scale AI models cost-efficiently.

Today, we’re announcing the most powerful, efficient and scalable TPU system to date, Cloud TPU v5p , designed for training cutting-edge AI models. This next generation TPU will accelerate Gemini’s development and help developers and enterprise customers train large-scale generative AI models faster, allowing new products and capabilities to reach customers sooner.

A row of Cloud TPU v5p AI accelerator supercomputers in a Google data center.

Built with responsibility and safety at the core

At Google, we’re committed to advancing bold and responsible AI in everything we do. Building upon Google’s AI Principles and the robust safety policies across our products, we’re adding new protections to account for Gemini’s multimodal capabilities. At each stage of development, we’re considering potential risks and working to test and mitigate them.

Gemini has the most comprehensive safety evaluations of any Google AI model to date, including for bias and toxicity. We’ve conducted novel research into potential risk areas like cyber-offense, persuasion and autonomy, and have applied Google Research’s best-in-class adversarial testing techniques to help identify critical safety issues in advance of Gemini’s deployment.

To identify blindspots in our internal evaluation approach, we’re working with a diverse group of external experts and partners to stress-test our models across a range of issues.

To diagnose content safety issues during Gemini’s training phases and ensure its output follows our policies, we’re using benchmarks such as Real Toxicity Prompts , a set of 100,000 prompts with varying degrees of toxicity pulled from the web, developed by experts at the Allen Institute for AI. Further details on this work are coming soon.

To limit harm, we built dedicated safety classifiers to identify, label and sort out content involving violence or negative stereotypes, for example. Combined with robust filters, this layered approach is designed to make Gemini safer and more inclusive for everyone. Additionally, we’re continuing to address known challenges for models such as factuality, grounding, attribution and corroboration.

Responsibility and safety will always be central to the development and deployment of our models. This is a long-term commitment that requires building collaboratively, so we’re partnering with the industry and broader ecosystem on defining best practices and setting safety and security benchmarks through organizations like MLCommons , the Frontier Model Forum and its AI Safety Fund , and our Secure AI Framework (SAIF) , which was designed to help mitigate security risks specific to AI systems across the public and private sectors. We’ll continue partnering with researchers, governments and civil society groups around the world as we develop Gemini.

Making Gemini available to the world

Gemini 1.0 is now rolling out across a range of products and platforms:

Gemini Pro in Google products

We’re bringing Gemini to billions of people through Google products.

Starting today, Bard will use a fine-tuned version of Gemini Pro for more advanced reasoning, planning, understanding and more. This is the biggest upgrade to Bard since it launched. It will be available in English in more than 170 countries and territories, and we plan to expand to different modalities and support new languages and locations in the near future.

We’re also bringing Gemini to Pixel . Pixel 8 Pro is the first smartphone engineered to run Gemini Nano, which is powering new features like Summarize in the Recorder app and rolling out in Smart Reply in Gboard, starting with WhatsApp, Line and KakaoTalk 1 — with more messaging apps coming next year.

In the coming months, Gemini will be available in more of our products and services like Search, Ads, Chrome and Duet AI.

We’re already starting to experiment with Gemini in Search, where it's making our Search Generative Experience (SGE) faster for users, with a 40% reduction in latency in English in the U.S., alongside improvements in quality.

Building with Gemini

Starting on December 13, developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI .

Google AI Studio is a free, web-based developer tool to prototype and launch apps quickly with an API key. When it's time for a fully-managed AI platform, Vertex AI allows customization of Gemini with full data control and benefits from additional Google Cloud features for enterprise security, safety, privacy and data governance and compliance.

Android developers will also be able to build with Gemini Nano, our most efficient model for on-device tasks, via AICore, a new system capability available in Android 14, starting on Pixel 8 Pro devices. Sign up for an early preview of AICore .

Gemini Ultra coming soon

For Gemini Ultra, we’re currently completing extensive trust and safety checks, including red-teaming by trusted external parties, and further refining the model using fine-tuning and reinforcement learning from human feedback (RLHF) before making it broadly available.

As part of this process, we’ll make Gemini Ultra available to select customers, developers, partners and safety and responsibility experts for early experimentation and feedback before rolling it out to developers and enterprise customers early next year.

Early next year, we’ll also launch Bard Advanced , a new, cutting-edge AI experience that gives you access to our best models and capabilities, starting with Gemini Ultra.

The Gemini era: enabling a future of innovation

This is a significant milestone in the development of AI, and the start of a new era for us at Google as we continue to rapidly innovate and responsibly advance the capabilities of our models.

We’ve made great progress on Gemini so far and we’re working hard to further extend its capabilities for future versions, including advances in planning and memory, and increasing the context window for processing even more information to give better responses.

We’re excited by the amazing possibilities of a world responsibly empowered by AI — a future of innovation that will enhance creativity, extend knowledge, advance science and transform the way billions of people live and work around the world.

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The 5 Stages in the Design Thinking Process

Design thinking is a methodology which provides a solution-based approach to solving problems. It’s extremely useful when used to tackle complex problems that are ill-defined or unknown—because it serves to understand the human needs involved, reframe the problem in human-centric ways, create numerous ideas in brainstorming sessions and adopt a hands-on approach to prototyping and testing. When you know how to apply the five stages of design thinking you will be impowered because you can apply the methodology to solve complex problems that occur in our companies, our countries, and across the world.

Design thinking is a non-linear, iterative process that can have anywhere from three to seven phases, depending on whom you talk to. We focus on the five-stage design thinking model proposed by the Hasso Plattner Institute of Design at Stanford (the d.school) because they are world-renowned for the way they teach and apply design thinking.

What are the 5 Stages of the Design Thinking Process

The five stages of design thinking, according to the d.school, are:

Empathize : research your users' needs .

Define : state your users' needs and problems.

Ideate : challenge assumptions and create ideas.

Prototype : start to create solutions.

Test : try your solutions out.

Let’s dive into each stage of the design thinking process.

• Transcript loading…

Hasso-Platner Institute Panorama

Ludwig Wilhelm Wall, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons

Stage 1: Empathize—Research Your Users' Needs

Empathize: the first phase of design thinking, where you gain real insight into users and their needs.

© Teo Yu Siang and the Interaction Design Foundation, CC BY-NC-SA 3.0.

The first stage of the design thinking process focuses on user-centric research . You want to gain an empathic understanding of the problem you are trying to solve. Consult experts to find out more about the area of concern and conduct observations to engage and empathize with your users. You may also want to immerse yourself in your users’ physical environment to gain a deeper, personal understanding of the issues involved—as well as their experiences and motivations . Empathy is crucial to problem solving and a human-centered design process as it allows design thinkers to set aside their own assumptions about the world and gain real insight into users and their needs.

Depending on time constraints, you will gather a substantial amount of information to use during the next stage. The main aim of the Empathize stage is to develop the best possible understanding of your users, their needs and the problems that underlie the development of the product or service you want to create.

Stage 2: Define—State Your Users' Needs and Problems

Define: the second phase of design thinking, where you define the problem statement in a human-centered manner.

In the Define stage, you will organize the information you have gathered during the Empathize stage. You’ll analyze your observations to define the core problems you and your team have identified up to this point. Defining the problem and problem statement must be done in a human-centered manner .

For example, you should not define the problem as your own wish or need of the company: “We need to increase our food-product market share among young teenage girls by 5%.”

You should pitch the problem statement from your perception of the users’ needs: “Teenage girls need to eat nutritious food in order to thrive, be healthy and grow.”

The Define stage will help the design team collect great ideas to establish features, functions and other elements to solve the problem at hand—or, at the very least, allow real users to resolve issues themselves with minimal difficulty. In this stage, you will start to progress to the third stage, the ideation phase, where you ask questions to help you look for solutions: “How might we encourage teenage girls to perform an action that benefits them and also involves your company’s food-related product or service?” for instance.

Stage 3: Ideate—Challenge Assumptions and Create Ideas

Ideate: the third phase of design thinking, where you identify innovative solutions to the problem statement you’ve created.

During the third stage of the design thinking process, designers are ready to generate ideas. You’ve grown to understand your users and their needs in the Empathize stage, and you’ve analyzed your observations in the Define stage to create a user centric problem statement. With this solid background, you and your team members can start to look at the problem from different perspectives and ideate innovative solutions to your problem statement .

There are hundreds of ideation techniques you can use—such as Brainstorm, Brainwrite , Worst Possible Idea and SCAMPER . Brainstorm and Worst Possible Idea techniques are typically used at the start of the ideation stage to stimulate free thinking and expand the problem space. This allows you to generate as many ideas as possible at the start of ideation. You should pick other ideation techniques towards the end of this stage to help you investigate and test your ideas, and choose the best ones to move forward with—either because they seem to solve the problem or provide the elements required to circumvent it.

Stage 4: Prototype—Start to Create Solutions

Prototype: the fourth phase of design thinking, where you identify the best possible solution.

The design team will now produce a number of inexpensive, scaled down versions of the product (or specific features found within the product) to investigate the key solutions generated in the ideation phase. These prototypes can be shared and tested within the team itself, in other departments or on a small group of people outside the design team.

This is an experimental phase, and the aim is to identify the best possible solution for each of the problems identified during the first three stages . The solutions are implemented within the prototypes and, one by one, they are investigated and then accepted, improved or rejected based on the users’ experiences.

By the end of the Prototype stage, the design team will have a better idea of the product’s limitations and the problems it faces. They’ll also have a clearer view of how real users would behave, think and feel when they interact with the end product.

Stage 5: Test—Try Your Solutions Out

Test: the fifth and final phase of the design thinking process, where you test solutions to derive a deep understanding of the product and its users.

Designers or evaluators rigorously test the complete product using the best solutions identified in the Prototype stage. This is the final stage of the five-stage model; however, in an iterative process such as design thinking, the results generated are often used to redefine one or more further problems. This increased level of understanding may help you investigate the conditions of use and how people think, behave and feel towards the product, and even lead you to loop back to a previous stage in the design thinking process. You can then proceed with further iterations and make alterations and refinements to rule out alternative solutions. The ultimate goal is to get as deep an understanding of the product and its users as possible.

Did You Know Design Thinking is a Non-Linear Process?

We’ve outlined a direct and linear design thinking process here, in which one stage seemingly leads to the next with a logical conclusion at user testing . However, in practice, the process is carried out in a more flexible and non-linear fashion . For example, different groups within the design team may conduct more than one stage concurrently, or designers may collect information and prototype throughout each stage of the project to bring their ideas to life and visualize the problem solutions as they go. What’s more, results from the Test stage may reveal new insights about users which lead to another brainstorming session (Ideate) or the development of new prototypes (Prototype).

It is important to note the five stages of design thinking are not always sequential. They do not have to follow a specific order, and they can often occur in parallel or be repeated iteratively. The stages should be understood as different modes which contribute to the entire design project, rather than sequential steps.

The design thinking process should not be seen as a concrete and inflexible approach to design; the component stages identified should serve as a guide to the activities you carry out. The stages might be switched, conducted concurrently or repeated several times to gain the most informative insights about your users, expand the solution space and hone in on innovative solutions.

This is one of the main benefits of the five-stage model. Knowledge acquired in the latter stages of the process can inform repeats of earlier stages . Information is continually used to inform the understanding of the problem and solution spaces, and to redefine the problem itself. This creates a perpetual loop, in which the designers continue to gain new insights, develop new ways to view the product (or service) and its possible uses and develop a far more profound understanding of their real users and the problems they face.

The Take Away

Design thinking is an iterative, non-linear process which focuses on a collaboration between designers and users. It brings innovative solutions to life based on how real users think, feel and behave.

This human-centered design process consists of five core stages Empathize, Define, Ideate, Prototype and Test.

It’s important to note that these stages are a guide. The iterative, non-linear nature of design thinking means you and your design team can carry these stages out simultaneously, repeat them and even circle back to previous stages at any point in the design thinking process.

References & Where to Learn More

Take our Design Thinking course which is the ultimate guide when you want to learn how to you can apply design thinking methods throughout a design thinking process. Herbert Simon, The Sciences of the Artificial (3rd Edition), 1996.

d.school, An Introduction to Design Thinking PROCESS GUIDE , 2010.

Gerd Waloszek, Introduction to Design Thinking , 2012.

Hero Image: © the Interaction Design Foundation, CC BY-NC-SA 3.0.

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Common Problem-Solving Models & How to Use Them

Problem – solving models are step-by-step processes that provide a framework for addressing challenges. Problems arise in every facet of life. From work. to home. to friends and family, problems and conflicts can make life difficult and interfere with our physical and mental well-being. Understanding how to approach problems when they arise and implementing problem-solving techniques can make the journey through a problem less onerous on ourselves and those around us.

By building a structured problem-solving process, you can begin to build muscle memory by repeatedly practicing the same approach, and eventually, you may even begin to find yourself solving complex problems . Building a problem-solving model for each of the situations where you may encounter a problem can give you a path forward, even when the most difficult of problems arise.

This article will explore the concept of problem-solving models and dive into examples of such models and how to use them. It will also outline the benefits of implementing a problem-solving model in each area of life and why these problem-solving methods can have a large impact on your overall well-being. The goal of this article is to help you identify effective problem-solving strategies and develop critical thinking to generate solutions for any problem that comes your way.

Problem-Solving Model Defined

The first step in creating a problem-solving plan is to understand what we mean when we say problem-solving models. A problem-solving model is a step-by-step process that helps a team identify and effectively solve problems that they may encounter. This problem-solving approach gives the team the muscle memory and guide to address a conflict and resolve disputes quickly and effectively.

There are common problem-solving models that many teams have implemented, but there is also the freedom to shape a method to fit the needs of a specific situation. These models often rely on various problem-solving techniques to identify the root cause of the issue and find the best solution. This article will explore some common problem-solving models as well as general problem-solving techniques to help a team engage with and solve problems effectively.

Benefits of Implementing Problem-Solving Models

Before we discuss the exact models for problem-solving, it can be helpful to discuss why problem-solving models are beneficial in the first place. There are a variety of benefits to having a plan in place when a problem arises, but a few important benefits are listed below.

Guide Posts

When a team encounters a problem and has a guide for how to approach and solve the problem, it can be a relief to know that they have a process to fall back on when the issue cannot be resolved quickly from the beginning. A problem-solving strategy will serve as a guide for the parties to know which steps to take next and how to identify the appropriate solution.

It can also clarify when the issue needs to stay within the team, and when the issue needs to be escalated to someone in a position with more authority. It can also help the entire team solve complex problems without creating an issue out of the way the team solves the problem. It gives the team a blueprint to work from and encourages them to find a good solution.

Creative Solutions That Last

When the team or family has a way to fall back on to solve a problem, it takes some of the pressure off of coming up with the process and allows the parties to focus on identifying the relevant information and coming up with various potential solutions to the issue. By using a problem-solving method, the parties can come up with different solutions and find common ground with the best solution. This can be stifled if the team is too focused on figuring out how to solve the problem.

Additionally, the solutions that the parties come up with through problem-solving tools will often address the root cause of the issue and stop the team from having to revisit the same problem over and over again. This can lead to overall productivity and well-being and help the team continue to output quality work. By encouraging collaboration and creativity, a problem-solving technique will often keep solving problems between the parties moving forward and possibly even address them before they show up.

Common Models to Use in the Problem-Solving Process

Several models can be applied to a complex problem and create possible solutions. These range from common and straightforward to creative and in-depth to identify the most effective ways to solve a problem. This section will discuss and break down the problem-solving models that are most frequently used.

Standard Problem-Solving Process

When you search for a problem-solving technique, chances are you will find the standard model for saving problems. This model identifies and uses several important steps that will often be used in other models as well, so it can be helpful to begin the model-building process with an understanding of this model as a base. Other models often draw from this process and adapt one or more of the steps to help create additional options. Each of these steps works to accomplish a specific goal in furtherance of a solution.

Define the Problem

The first step in addressing a problem is to create a clear definition of the issue at hand. This will often require the team to communicate openly and honestly to place parameters around the issue. As the team defines the problem, it will be clear what needs to be solved and what pieces of the conflict are ancillary to the major issue. It helps to find the root causes of the issue and begin a process to address that rather than the symptoms of the problem. The team can also create a problem statement, which outlines the parameters of the problem and what needs to be fixed.

In addition to open and honest communication, other techniques can help to identify the root cause and define the problem. This includes a thorough review of the processes and steps that are currently used in the task and whether any of those steps are directly or indirectly causing the problem.

This includes reviewing how tasks are done, how communication is shared, and the current partners and team members that work together to identify if any of those are part of the issue. It is also the time to identify if some of the easy fixes or new tools would solve the problem and what the impact would be.

It is also important to gain a wide understanding of the problem from all of the people involved. Many people will have opinions on what is going on, but it is also important to understand the facts over the opinions that are affecting the problem. This can also help you identify if the problem is arising from a boundary or standard that is not being met or honored. By gathering data and understanding the source of the problem, the process of solving it can begin.

Generate Solutions

The next step in the basic process is to generate possible solutions to the problem. At this step, it is less important to evaluate how each of the options will play out and how they may change the process and more important to identify solutions that could address the issue. This includes solutions that support the goals of the team and the task, and the team can also identify short and long-term solutions.

The team should work to brainstorm as many viable solutions as possible to give them the best options to consider moving forward. They cannot pick the first solution that is proposed and consider it a successful problem-solving process.

Evaluate and Select

After a few good options have been identified, the next step is to evaluate the options and pick the most viable option that also supports the goals of the team or organization. This includes looking at each of the possible solutions and determining how they would either encourage or hinder the goals and standards of the team. These should evaluated without bias toward the solution proposed or the person putting forward the solution. Additionally, the team should consider both actual outcomes that have happened in the past and predicted instances that may occur if the solution is chosen.

Each solution should be evaluated by considering if the solution would solve the current problem without causing additional issues, the willingness of the team to buy in and implement the solution, and the actual ability of the team to implement the solution.

Participation and honesty from all team members will make the process go more smoothly and ensure that the best option for everyone involved is selected. Once the team picks the option they would like to use for the specific problem, they should clearly define what the solution is and how it should be implemented. There should also be a strategy for how to evaluate the effectiveness of the solution.

Implement the Solution and Follow Up

Once a solution is chosen, a team will often assume that the work of solving problems is complete. However, the final step in the basic model is an important step to determine if the matter is resolved or if additional options are needed. After the solution has been implemented by the team, the members of the team must provide feedback and identify any potential obstacles that may have been missed in the decision-making process.

This encourages long-term solutions for the problem and helps the team to continue to move forward with their work. It also gives the team a sense of ownership and an example of how to evaluate an idea in the future.

If the solution is not working the way that it should, the team will often need to adapt the option, or they may get to the point where they scrap the option and attempt another. Solving a problem is not always a linear process, and encouraging reform and change within the process will help the team find the answer to the issues that they face.

GROW Method

Another method that is similar to the standard method is the G.R.O.W. method. This method has very similar steps to the standard method, but the catchiness of the acronym helps a team approach the problem from the same angle each time and work through the method quickly.

The first step in the method is to identify a goal, which is what the “g” stands for in “grow.” To establish a goal, the team will need to look at the issues that they are facing and identify what they would like to accomplish and solve through the problem-solving process. The team will likely participate in conversations that identify the issues that they are facing and what they need to resolve.

The next step is to establish the current reality that the group is facing. This helps them to determine where they currently are and what needs to be done to move them forward. This can help the group establish a baseline for where they started and what they would like to change.

The next step is to find any obstacles that may be blocking the group from achieving their goal. This is where the main crux of the issues that the group is facing will come out. This is also helpful in giving the group a chance to find ways around these obstacles and toward a solution.

Way Forward

After identifying the obstacles and potential ways to avoid them, the group will then need to pick the best way to move forward and approach their goal together. Here, they will need to create steps to move forward with that goal.

Divide and Conquer

Another common problem-solving method is the divide-and-conquer method. Here, instead of the entire team working through each step of the process as a large group, they split up the issue into smaller problems that can be solved and have individual members or small groups work through the smaller problems. Once each group is satisfied with the solution to the problem, they present it to the larger group to consider along with the other options.

This process can be helpful if there is a large team attempting to solve a large and complex problem. It is also beneficial because it can be used in teams with smaller, specialized teams within it because it allows each smaller group to focus on what they know best.

However, it does encourage the parties to shy away from collaboration on the overall issue, and the different solutions that each proposes may not be possible when combined and implemented.

For this reason, it is best to use this solution when approaching complex problems with large teams and the ability to combine several problem-solving methods into one.

Six Thinking Hats

The Six Thinking Hats theory is a concept designed for a team with a lot of differing conflict styles and problem-solving techniques. This method was developed to help sort through the various techniques that people may use and help a team find a solution that works for everyone involved. It helps to organize thinking and lead the conversation to the best possible solution.

Within this system, there are six different “hats” that identify with the various aspects of the decision-making process: the overall process, idea generation, intuition and emotions, values, information gathering, and caution or critical thinking. The group agrees to participate in the process by agreeing on which of the hats the group is wearing at a given moment. This helps set parameters and expectations around what the group is attempting to achieve at any moment.

This system is particularly good in a group with different conflict styles or where people have a hard time collecting and organizing their thoughts. It can be incredibly beneficial for complex problems with many moving parts. It can also help groups identify how each of the smaller sections relates to the big picture and help create new ideas to answer the overall problem.

However, it can derail if the group focuses too heavily or for too long on one of the “hats.” The group should ensure that they have a facilitator to guide them through the process and ensure that each idea and section is considered adequately.

Trial and Error

The trial and error process takes over the evaluation and selection process and instead chooses to try out each of the alternatives to determine what the best option would be. It allows the team to gather data on each of the options and how they apply practically. It also provides the ability for the team to have an example of each possible answer to help a decision-maker determine what the best option is.

Problem-solving methods that focus on trial and error can be helpful when a team has a simple problem or a lot of time to test potential solutions, gather data, and determine an answer to the issue.

It can also be helpful when the team has a sense of the best guess for a solution but wants to test it out to determine if the data supports that option, or if they have several viable options and would like to identify the best one. However, it can be incredibly time-consuming to test each of the options and evaluate how they went. Time can often be saved by evaluating each option and selecting the best to test.

Other Problem-Solving Skills

In addition to the methods outlined above, other problem-solving skills can be used regardless of the model that is used. These techniques can round out the problem-solving process and help address either specific steps in the overall method or alter the step in some way to help it fit a specific situation.

Ask Good Questions

One of the best ways to work through any of the problem-solving models is to ask good questions. This will help the group find the issue at the heart of the problem and address that issue rather than the symptoms. The best questions will also help the group find viable solutions and pick the solution that the group can use to move forward. The more creative the questions , the more likely that they will produce innovative solutions.

Take a Step Back

Occasionally, paying attention to a problem too much can give the group tunnel vision and harm the overall processes that the group is using. Other times, the focus can lead to escalations in conflict. When this happens, it can be helpful to set aside the problem and give the group time to calm down. Once they have a chance to reconsider the options and how they apply, they can approach the issue with a new sense of purpose and determination. This can lead to additional creative solutions that may help the group find a new way forward.

Final Thoughts

Problem-solving can be a daunting part of life. However, with a good problem-solving method and the right techniques, problems can be addressed well and quickly. Applying some of these options outlined in this article can give you a head start in solving your next problem and any others that arise.

To learn more about problem-solving models, problem-solving activities, and more, contact ADR Times !

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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