Literary Insights

BOOK LINK:

The book explores the concept of “open talent”, which refers to leveraging a globally distributed, digitally connected workforce to access specialized talent on demand rather than solely relying on full-time employees.
Major trends driving this change include the digital transformation of work, the pandemic-fueled shift to remote work, and the rise of technologies like generative AI that can augment human abilities.
An open talent approach allows companies to tap into a vast network of people and organizations around the world to access diverse perspectives, expertise, and creativity to solve problems at unprecedented scale.
Some of the benefits include accessing specialized talent as needed, fostering a culture of collaboration and continuous innovation, and addressing challenges with more agility.
The book provides a roadmap for companies to build open talent capabilities, including designing centers of excellence, assessing needs and experimenting with solutions, building external talent clouds, open innovation capabilities, internal talent marketplaces, and scaling the networked organization model.
It offers practical guidance and examples on integrating open talent strategies, developing necessary operations and leadership, and accelerating adoption through cultural and mindset changes.
The future of work is being transformed around talent like programmers, content creators, innovators etc. due to technologies like remote work enabled by digital platforms.
Open talent platforms are helping organizations source talent globally to solve problems and drive innovation in a cost-effective way. This is accelerating digital transformation.
Early adopters of open talent and crowdsourcing included magazines, marketing agencies, and platforms like Topcoder. This challenged traditional talent models.
Companies like CP+B experimented with crowdsourcing ideas from online platforms, receiving thousands of high quality submissions and changing their view of where talent could come from.
Open talent approaches like those pioneered by companies and platforms are now becoming more widely adopted as organizations realize they need new talent strategies to solve challenges and keep up in a digital world where remote independent talent is increasingly accessible.

So in summary, it outlines how digital technologies have transformed where and how organizations can source talent globally, the early pioneers of these open talent models, and why these new approaches are becoming more necessary for organizations.

A software engineer was leaving his job at SEI (a software company) to take a fully remote job at a startup paying $225k, almost triple his current salary.
This highlighted the challenges companies face retaining talent as employees rethink how/where they want to work post-pandemic. Remote work has become the norm.
SEI’s physical campus, once meant to attract talent, has now become a liability as they compete with virtual startups. This is causing talent shortages and issues keeping up with demand.
Surveys show 41% of global workforce considered leaving jobs in early 2021. The Great Resignation began in late 2021 with millions quitting jobs each month in the US.
This shifted power to employees, who now have more choice and control over their work through remote opportunities and freelancing platforms.
One example is Jan, a bookkeeper who made $42k at a firm but now earns $300k freelancing on platforms after being laid off. This shows how talent now has more options than just full-time roles.
This talent crisis and shortage, exacerbated by the pandemic, requires a new mindset and model called “open talent” to access skilled workers on demand through platforms. Companies must attract talent differently now that people have more autonomy.
The introduction discusses how open talent platforms can help companies hire freelancers or contractors to help meet deadlines, especially for small and mid-sized businesses that need agile resources.
It then provides a historical example of Victors & Spoils winning the Harley-Davidson ad account using freelancers on their platform, highlighting how open talent was disruptive even in the past.
Next, it discusses how the Great Resignation was initially concerning for companies but ended up showing the benefits of open talent platforms in efficiently matching jobs to contractors. This helps both talent through flexible work and companies through cost savings.
Open talent also improves diversity, equity and inclusion by creating various roles that can leverage different skills and minimize barriers. If used properly, open talent should not exploit workers but empower them through flexible work opportunities.
Historically, large companies had a arrogant view of talent acquisition but the pandemic was a wake-up call on the need for more digital and agile processes. Open talent helps transform outsourcing, contingent work and problem solving by efficiently connecting internal and external talent.
Many organizations are already using open talent/contractors/freelancers from platforms like Upwork to solve problems discreetly, but not as a strategic solution. Managers use them to get work done faster when the internal teams are slow or bureaucratic.
However, larger companies are hesitant to fully embrace open talent platforms as a strategic solution due to existing investments in old models and reluctance to change. Smaller companies have an advantage as they are more adaptable and realize benefits like lower costs and flexibility.
As work becomes more digital, companies need to change how they access and organize talent. They should view all talent through a single lens as a global network that can be tapped as needed for specific tasks. This involves breaking down projects into discrete tasks and sourcing the right freelancer for each task.
The book aims to provide a guide for companies to systematically adopt an open talent strategy and transform into fully networked organizations that can leverage the global workforce to solve challenges. It addresses challenges around security, compliance and provides case studies and steps for implementation.

Here is a summary of the key points from next:

Chapter 1 discusses how organizational mindsets have evolved over history and why many companies now face talent scarcity issues.
It introduces Balaji Bondili of Deloitte, who pioneered an open talent strategy at the large professional services firm to address challenges finding the right talent.
Bondili shifted from a fixed to a growth mindset, believing talent can be developed through effort and learning rather than being innate. He realized Deloitte needed to tap external talent platforms.
Bondili set up an internal center called Deloitte Pixel to source crowds for project tasks using platforms. This helped “taskify” work and find talent more quickly.
The center gained momentum as partners recognized it could decrease short-term revenues but produce more work and revenue long-term.
Bondili later had to change many minds at Deloitte, including serious naysayers, to fully transform the organization’s approach.

So in summary, the chapter introduces the concept of open talent and how one leader at Deloitte was able to pioneer this strategy by shifting his own mindset and ultimately transforming the organization.

The chapter discusses how most leaders’ and organizations’ mindsets and work processes are still rooted in the industrial age, despite major shifts in technology and the nature of work over time. The pandemic has exposed the inadequacies of this outdated approach and created an opportunity for redesign.

Historically, managers have organized work based on breaking tasks into regimented sequences suited for factories. While technology has advanced through different industrial revolutions, management structures have not adapted sufficiently. Today’s knowledge/digital work does not fit the old command-and-control model.

The pandemic accelerated pre-existing issues around an impending talent crisis as worker expectations evolved. It served as a “black swan” event that calls assumptions into question. Leaders now have a chance to redesign their approach to better meet modern workforce demands and position their organizations for the future. An open talent mindset that recognizes skilled workers’ power could enable positive changes on the scale seen after past disruptive events like the 1918 pandemic.

Recent advances in technology and the pandemic have accelerated the need for companies to change their organizational structures. Traditional command-and-control management styles no longer work in this new environment where remote work, AI and digital platforms are increasingly important. Companies that do not adapt their structures may not survive.

The emergence of digital platforms like Amazon, Facebook and Uber have fundamentally changed industries by connecting people and talent in new ways. Platforms shape how different participants interact and mediate work. New operating models need to consider how to reframe work and tasks in this environment.

Talent can now work independently and globally through platforms. The media industry is an example - audiences want to both consume and create content through sharing on social media platforms. This connectivity has changed expectations around how people interact. We are moving from ownership to an access model.

As technologies like AI become more advanced, individual talent will be able to take on exponentially more work through tools. Companies need to organize themselves differently to take advantage of new forms of talent and collaboration enabled by digital transformation. Leaders must recognize the pace and scale of changes happening to successfully change and adapt their organizations.

Open talent strategies require a comprehensive shift in organizational culture to fully realize their potential, not just fragmented individual efforts.
Early adopters of open talent fall into two categories: super users who want to use it for individual gain, and those who tried on their own and failed due to resistance from the existing culture.
For open talent to succeed at scale, companies need to adopt it as a strategic, company-wide approach and shift their culture to one that encourages collaboration, knowledge sharing, flexibility and responsiveness.
Leaders who keep open talent solutions to themselves rather than sharing them don’t help organizations tap into opportunities to scale. Maps built through open talent need to be expanded and shared to change the overall culture.
The experience of NASA is used as an example of initially successful pilots but later resistance without a top-down cultural shift to embrace open talent platforms more broadly across the organization.

So in summary, adopting open talent requires transforming organizational culture company-wide, not just fragmented individual efforts, in order to fully realize the benefits at scale.

This summary discusses key insights from the chapter:

Cultural shifts are nothing new, as seen when society transitioned from agricultural to industrial. Adaptation is required to integrate new technologies.
Similar cultural shifts occurred with the automobile and later with agile software development methods. Both required adaptation away from older hierarchies.
A new model is proposed for open talent that parallels frameworks for radical, incremental, modular, and architectural innovation.
Specialization is limited in the digital age due to skills multiplication. Architectural talent innovation creates a new work paradigm not controlled by any one party.
Architectural shifts in knowledge change what’s possible for firms through more agile means than outsourcing. It allows tapping global talent platforms to access diverse skills.
The goal is to “create an ecology” to get all smart people contributing, as no one firm employs all the best talent. Platforms provide utility, speed and cost-effective access to necessary skills.

The passage discusses the rise of the “passion economy” and how digital platforms are enabling remote workers to connect directly with companies for projects on a task-by-task basis.

Two thought leaders coined the term passion economy to refer to workers using the digital revolution to combine their careers with other passions and aspects of life. People are drawn to platforms because they offer more flexibility to pursue personal interests along with work.

Remote work allows greater balance, quality of life, and control over how, when and how much one works. This taps into people’s desire for more freedom and autonomy. Organizations will need to acknowledge these desires and access talent through platforms if they want to succeed in the future.

The passage then provides an example of how the Broad Institute crowdsourced an algorithm challenge through a platform. Although the engineer who solved it had no prior knowledge in the domain, he was motivated by interest in the problem and desire to improve his skills. Intrinsic rewards like learning, impact and peer recognition drive many problem solvers.

Finally, it summarizes Jimmy Chin’s journey leveraging platforms and social media to grow his personal brand in photography and filmmaking around his passions of mountain climbing and adventure. Digital tools empower individuals to pursue passions both within and alongside their careers.

Jimmy Chin is an Instagram influencer and professional climber/photographer with over 3.4 million followers.
Before social media, brands would have to sponsor his adventures or pay publications to run ads alongside articles about him.
Now, brands can work directly with him on Instagram. He charges up to $50k per branded post to his large following.
This allows brands to reach a huge audience instantly for much less money than traditional media.
It has created a new class of “creative entrepreneurs” who can monetize their followers directly.
It cuts out middlemen like publications. While empowering for influencers, it has impacted media jobs.
The story is used as an example of how platforms can connect talent directly to opportunities, shifting costs from fixed to variable for companies. This networked model is argued to be the future for organizations.

The passage discusses different kinds of relationships that platforms can facilitate:

Many-to-many relationships involve large groups looking for answers from a distributed crowd, similar to a wiki.
One-to-one relationships match a single entity with a specific resource, which most gig platforms succeed at.
Many-to-one mechanisms involve a single entity finding solutions from a pool of people, like crowdsourcing.

It notes not every platform excels at all three types. Jin realized this, saving NASA time and money.

The passage then talks about how companies need a growth mindset and culture that engages external talent alongside internal employees. This makes them “networked organizations” that co-create solutions with passionate freelancers and customers.

It discusses adopting attributes like Jimmy Chin that drew on skills of free agents. Leaders need to ditch scarcity mindsets, help employees become solution seekers not just problem solvers, and use branding to attract top external talent like Chin.

Here are the key points about how an open talent center of excellence (COE) differs from a traditional COE:

Scope of talent pools: An open talent COE deals with both internal employees and external talent from outside the organization, integrating different talent pools. A traditional COE focuses only on internal employees and resources.
Sourcing methods: An open talent COE leverages platforms, marketplaces, challenges, competitions, and other open methods to source talent openly from around the world. A traditional COE relies more on internal resources and conventional recruitment/hiring.
IP/compliance focus: Open talent introduces more variables like intellectual property protection, compliance with different jurisdictions, worker classification, etc. A COE needs expertise managing these aspects.
Cultural change: An open talent strategy requires a cultural shift within the organization to embrace external talent. The COE plays a key role driving this cultural change through awareness, training, integration efforts, etc.
Relationship management: The COE takes on the tasks of actively managing relationships with both internal and external talent pools as strategic resources.
Innovation focus: Open talent supports innovation through idea competitions, hackathons, challenges, etc. which require specialized expertise that the COE can provide.

So in summary, an open talent COE has a more expansive remit to strategically integrate internal and external talent pools, focuses on cultural and relationship aspects, and leverages open methods of sourcing talent to support innovation.

A Center of Excellence (COE) provides expertise, institutional knowledge, helps shape culture, tackles barriers in processes/procedures, and bridges services and platforms for an organization.
Its role is to assess needs, experiment with initiatives, build an external talent cloud, open innovation capabilities using crowdsourcing, and an internal talent marketplace.
The goal is a fully “networked” organization that taps into talent globally as needed.
NASA created its COECI when the White House encouraged other agencies to follow NASA’s success with pilots tapping a global talent pool.
Under Lynn Buquo’s leadership, NASA’s COECI grew awareness of open talent from 10% to over 50% of the agency in 10 years. She aligned with legal/procurement and chose strategic battles to support projects.
COE leaders need abilities like NASA’s slow and steady approach or a more improvisational “jazz band” style depending on the situation, to coordinate talent internally and externally.
When Wipro acquired Appirio, the CEO Abidali Z. Neemuchwala wanted to create one of the largest cloud transformation practices. Appirio owned Topcoder, an open talent platform with over 1.5 million members.
Neemuchwala discovered thousands of Wipro employees were also members on Topcoder, doing freelance work. Rather than punishing them, he celebrated their initiative to learn new skills. He encouraged others to do the same and offered bonuses.
A Center of Excellence (COE) should lead an organization’s transition to open talent. But reinventing talent strategies requires starting where the organization currently is, not with a predefined end state.
The COE should provide multiple entry points for internal employees and external workers to get involved in projects. This removes friction and allows organizations to move faster than competitors by tapping crowdsourced expertise when needed.

The key points are how the Wipro CEO handled discovering employees doing outside work, the role of a COE in managing an open talent transition, and the importance of starting from the organization’s current state rather than a preconceived end goal when reinventing talent strategies. The COE should create frictionless ways for varied participants to contribute using open talent approaches.

Companies should assess their open talent strategy and readiness for change before implementing new initiatives. This includes understanding current use of open talent, collaboration ecosystems, internal mobility, stakeholder support, and metrics for goals.
A Center of Excellence (COE) can guide the assessment and subsequent phases of learning, experimentation, building out capabilities, and scaling initiatives.
The assessment involves surveying stakeholders, mapping dimensions like vision and operating models, setting goals and metrics, and understanding timelines and responsibilities.
Assessing the collaboration ecosystem examines existing vendor/sourcing relationships and how open talent is already informally used, like employees moonlighting. This provides a baseline for further planning.
Questions around internal mobility, mentorship programs, innovation initiatives, and attrition help evaluate organizational culture and opportunities for employees. Filling openings internally and engagement have benefits.
Platform usage by employees with corporate emails indicates wider informal use of open talent than formal enterprise partnerships, which should inform the strategy. A thorough assessment is key before major changes.
Companies are freely using external talent on platforms without fully assessing why and how this practice should work at an organizational level. A comprehensive assessment needs to be done.
It is recommended to create an open talent vision document approved by senior leadership to provide clarity and direction. Success metrics should be defined and stakeholders engaged early.
Talent acquisition, HR and procurement processes may need to change to accommodate open talent. Onboarding times especially need to be assessed and streamlined if possible.
Risks around data, IP, security etc. need to be evaluated and mitigation strategies developed without compromising security.
Internal talent mobility opportunities should be explored to better utilize current employee skills and interests.
An open talent operating model needs to be developed outlining governance, decision rights, technology and data management over time.
The readiness assessment should identify alignment with business goals and any gaps. Early experiments can help establish proof of concepts and build momentum. Learning from experiments is key to refinement.
The Wright brothers extensively published and shared their knowledge on aeronautics to advance the field, even without monetary gains. Others like Hargrave also chose to openly share designs rather than patent them.
Open sharing of innovations has many benefits but less incentive for scaling up ideas. IP protection is still important for most organizations.
Platforms like Topcoder, Kaggle, and Wazoku connect businesses to global talent networks to source ideas and solutions. von Hippel’s research supports benefits of “free innovation” but challenges of scaling without IP.
Successful platforms structurally organize previously informal work like Uber, Airbnb, letting producers interact globally. This lowers collaboration costs.
Companies should develop a learning culture and tap external talent markets by aligning interests with workers’ passions. Freelancers often spend more time learning than employees.
To use open talent, companies need to understand platforms, learn from others, and create a Center of Excellence (COE) to organize activities, experiment, and scale an open talent system. There are over 1,000 platforms across industries and task types.
Idea management platforms help companies identify market opportunities, cost savings, technology improvements, and innovations. They can source ideas either broadly or deliver ready-to-license products.
Internal talent marketplaces (ITMs) allow employees within an organization to collaborate more openly and work across silos using internal software platforms. This enhances ideas and outputs. Good ITMs automate the matching and feedback processes.
Some cases discussed include crowdsourcing challenges on platforms like HeroX to solve problems for clients like NASA. Another example highlights how reframing a snack company’s problem unlocked more diverse solutions.
ITMs like those used by SEI, Unilever, and NASA facilitate sharing skills, breaking down silos, and finding solutions internally. NASA’s platform in particular has delivered significant cost savings and faster development times.
In summary, these platforms and marketplaces aim to tap external and internal talent more efficiently to generate innovative ideas, solutions, and business opportunities. Sharing talent and knowledge within and across organizations is a key goal.
The passage discusses best practices for experimenting with open talent solutions, which leverage external communities to solve organizational problems.
It advocates starting with small pilots to test solutions while relying on platforms to handle security, IP protection, and administration challenges. The goals are to form an implementation plan and get stakeholder buy-in.
Key steps include finalizing problem definitions, selecting appropriate platforms, measuring results, and communicating learnings widely to stakeholders. Evaluation should focus on learning, not business metrics.
Budgets should support both business unit innovation and longer-term corporate projects. A plan for failures is also important since many projects will not succeed.
Effective communication targets expert problem solvers, leadership, stakeholders, and a general audience to promote work and results.
Resistance is expected since open talent challenges existing structures. However, it can complement rather than replace internal work if framed appropriately.
The passage provides tips on focusing on pain points rather than proposed solutions, and giving credit to internal teams to boost employee morale and acceptance of open talent approaches.

An external talent cloud (ETC) allows companies to systematically hire freelance workers through online platforms to fill skills gaps. ETCs provide strategic advantages like improved productivity and speed, as well as cost savings through lower talent costs and the ability to shift fixed costs to variable.

Companies should assess their internal needs, research various talent platforms, and run pilot projects to build their ETC infrastructure. Large platforms like Freelancer.com give companies access to a global pool of freelancers across many skills. Studies show platforms can source talent 4x faster than traditional methods at 30% lower cost and 40% higher productivity.

Platforms grew rapidly as startups consolidated through acquisitions. Freelancer.com for example acquired competitors to reach critical mass, then invested in data analytics to improve the platform experience and attract more users/projects. Today platforms provide efficient, transparent matching of supply and demand for on-demand work at scale. ETCs integrate this model into company operations for strategic and financial benefits.

Freelancer.com acquired several other online freelancing platforms between 2019-2020, including EUFreelance, LimeExchange, Scriptlance, and Rent-A-Coder (vWorker) to rapidly expand its user base.
To reduce churn from the acquisitions, Freelancer.com seamlessly transferred users’ profiles, projects, payments, reputations, messages, etc. from the acquired sites to Freelancer.com within 24 hours so they would not lose access to their accounts.
Growing demand from large enterprises for external talent led Freelancer.com and other platforms to develop comprehensive enterprise strategies.
As a case study, in 2019 Freelancer.com partnered with Arrow Electronics, a $30B company, to launch ArrowPlus which provides access to 500,000 skilled engineers through Freelancer.com’s platform.
The passage then discusses strategies for companies to build and manage their own external talent clouds (ETCs), including choosing platforms, defining tasks, and tools like Microsoft 365 that can help integrate external talent.

A creative assistant to support marketing campaigns and events

Programmers: 2 devs to support app build

Platform: The platform(s) that will be used to source the open talent.

Upwork

CrowdC

Sourcing: Process and timeline for sourcing talent

Post roles with descriptions, 2 week timeline

Post roles with descriptions, 1 week timeline

Screening: Process for screening candidates

Review portfolios, conduct phone screens

Review code samples, conduct technical tests

Selection: Process for selecting ideal candidates

Make selection based on top 3 candidates

Make selection based on top candidates technical evaluation

Onboarding: Process for onboarding and ramping up

Schedule onboarding calls, provide documentation

Conduct onboarding session, pair programming

Management: Process for managing work and deliverables

Biweekly standups, project management tool

Daily standups, project management tool

Payments: Process for setting up billing and payments

Upwork billing/payment process

Invoicing process through accounting

Metrics: Key metrics to measure success of pilots

Customer satisfaction, project turnaround time

Number of features delivered, code quality

Evaluation: Process for evaluating pilot outcomes

Survey stakeholders, document lessons learned

Code reviews, survey stakeholders

FIGURE 5-2

A matrix method for mapping out pilots across different platforms and teams

Parameter

Creative Assistant

(Upwork)

Programmers

(CrowdC)

UX Researchers

(Catalant)

Requirements Platform Sourcing Screening Selection Onboarding Management Payments Metrics Evaluation

The assessment model outlines an ideal timeline for assessing the necessary processes and changes needed to implement open talent at scale. This includes an initial 4-week sprint to get a high-level view of changes before detailed design.
Key areas to assess include contracts, screening, access requirements, major process changes like interviewing and review, coordination rules, quality management, and involving all existing talent hiring teams.
Special attention should be paid to typical friction areas like demand forecasting, onboarding, IT security, billing, and cost center alignment.
The organization may need to be formalized to extend the Center of Excellence’s responsibilities and implement governance as open talent is scaled up.
Pilot projects can help plan for later measurable success and iteration of the operating model. The assessment model outlines primary characteristics of enterprises showing high maturity with open talent.
Upwork has evolved to better serve enterprises through solutions like Private Talent Cloud, helping large companies integrate insights and improve user experience when accessing open talent at scale.
Upwork Enterprise helps large companies manage freelance workers in a systemic way by addressing risks and simplifying tasks like central management of remote work. It grew in popularity with the shift to remote work during the pandemic.
Features like Bring Your Own Talent allow companies to onboard existing non-Upwork freelancers for centralized management including payroll, collaboration, cost management, and reporting.
Upwork Enterprise focuses on sourcing expert assistance in finding talent and onboarding/contracting processes to minimize delays in starting work.
Flexera uses Upwork successfully by having HR be early adopters to show usefulness. It onboards talent 24x faster and saves over 60% of traditional costs. The CEO sees outside talent as a competitive advantage.
Future platforms like Braintrust are decentralized autonomous organizations on the blockchain that give more control and rewards to freelancers by eliminating fees and sharing ownership. This could disrupt current platforms’ models of inequitable rewards between companies and workers.
Platforms are developing skills tracking and collaboration recommendation technologies to better match workers to tasks beyond ratings. External talent clouds provide strategic advantages, cost savings, and flexibility for companies.
An external talent cloud (ETC) involves using freelance or contract workers instead of full-time employees for some roles within a company. This allows companies to obtain specialized skills as needed without growing their permanent workforce.
To build an effective ETC, companies should first analyze which roles are suited for freelance workers while ensuring key positions remain full-time.
Companies need to partner with online platforms that provide access to freelance talent. They should consider the different features and specializations of various platforms.
Tools like Microsoft 365 can help set up task management and communication structures. Companies should identify pain points and optimize processes for replicable tasks.
Communication is important to reassure full-time employees about job security amid the changes.
Emerging technologies like web3 and tokens may allow freelancers to have more ownership over work and earn additional benefits like referrals in the future. Companies should consider how to leverage these kinds of opportunities.
In summary, an ETC utilizes online platforms and process optimization to access specialized freelance talent for roles that don’t require permanent employees, while maintaining core full-time positions and communication.
The case study focused on developing solutions for improving cancer diagnostics and treatment through a crowdsourced contest.
Specifically, the goal was to create a digital assistant using AI that could help doctors more precisely identify cancer cells and target radiation therapy, reducing risks of harming healthy cells.
Identifying cancer cells from medical imaging like CT scans is challenging and time-consuming for doctors currently. Commercial software is cumbersome and outline tracing can take 30 seconds to 30 minutes.
The contest aimed to crowd-source ideas for developing AI algorithms that could analyze imaging data and more accurately segment tumors, helping radiation oncologists deliver more precise treatments.
The hope was that even a small-scale crowdsourced contest could generate useful insights and lead to iterative refinement and development of solutions to improve lung cancer diagnostics and outcomes over time.
The hospital recruited data scientists through Topcoder, a crowdsourcing platform, to develop an algorithm that could replicate the skill and expertise of an expert radiologist in segmenting lung tumors.
The challenge was run over 3 phases, with feedback and refinement between phases. Over 500 contestants from 62 countries participated.
In the first phase, the goal was to identify tumors. Based on feedback, phase 2 focused on contouring tumors instead, given a marker on tumors. This improved results by 40%.
In phase 3, top performers collaborated to further refine the algorithm, improving results another 10%. The final algorithm matched the accuracy of a human expert, but was much faster.
Running the challenge on a platform allowed access to a large, diverse pool of data scientists globally. Providing a real dataset, scoring system, and expert feedback helped contestants develop effective solutions. The prize money incentivized participation and improved results.
The crowdsourced approach successfully replicated an expert’s skill and knowledge, with the goal of transferring this expertise to under-resourced healthcare settings to improve radiation therapy quality.

Based on the information provided:

Who is responsible for this problem or opportunity? - No specific responsibility is mentioned. The summary is talking generally about best practices for running data science challenges on multiple platforms.
Do you have an executive sponsor? - No information is given about any specific executive sponsor. The summary is providing general advice about the importance of getting executive support.
Do you have a multivendor contract in place? - The summary mentions that Topcoder and DrivenData have partnered with HeroX on some NASA challenges, suggesting those organizations have agreements in place to work together. But no specific contract is referenced.
How will you measure success? - The summary lists some typical metrics for defining initial success, such as engagement, ideas taken forward, and benefits tracking. But it does not provide details on measuring any particular challenge.
How will you evaluate your contest? - Evaluation criteria are discussed generically, but no evaluation process or criteria are attributed to a specific challenge.
Timelines for carrying out your contest? - Typical timelines for ideation and solution generation contests are outlined, but no specific scheduled is mentioned.

So in summary, while best practices are discussed, there is no mention of responsibility or details regarding a particular problem, opportunity, executive sponsor, contract, or evaluation process being referred to in the summary. The information appears to be general advice rather than focused on any single challenge.

The submission phase for contests allows time for people to submit their ideas/solutions with relatively little oversight or review. This phase should be used to prepare for the review processes.
Reviews will differ depending on whether the criteria are objective (e.g. for data/software contests) or subjective (e.g. for idea contests). Objective contests have preset benchmarks and scoring done before the contest by organizers. Subjective contests involve committee reviews of submissions.
For objective contests, a leaderboard gives provisional scores but only the final submission counts. Additional internal testing may be needed after selecting a winner. For subjective contests, submissions are reviewed in waves or all at once by a committee.
Managing reviewer biases is important for subjective contests, like having two judges per submission and averaging scores.
NASA faced challenges gaining adoption of contests internally but took steps like reducing some project budgets to incentivize teams to utilize contests, which eventually led to contests accounting for a significant portion of some teams’ development work. Radical approaches like budget cuts can help propel new practices.
The passage discusses how organizations can leverage open innovation through contests and challenges to source novel ideas from both internal and external talent.
It provides guidance on when to run a contest, how to structure roles and responsibilities, how to measure success, how to evaluate contests, and typical timelines.
Key considerations include idea generation vs solution generation, recruiting judges, defining evaluation criteria, being fair and consistent, and allowing sufficient time for each phase.
It also shares lessons from NASA’s Center of Excellence for Collaborative Innovation on gaining long-term traction. NASA aligned with other departments, shifted budgets, established repeatable processes, implemented training programs, and improved metrics over 10+ years.
The last paragraphs introduce the next chapter topic of building an internal talent marketplace to better manage and access internal talent through a digital platform, providing previously unavailable insights into employee skills.

In summary, the passage provides guidance and best practices for organizations looking to leverage open innovation through contests and challenges, both internally and externally, and introduces the concept of utilizing an internal talent marketplace platform.

Otake used to work as an engineer in R&D roles at Unilever but became interested in diversity, equity, and inclusion (DEI). When a job opened on the DEI team, she applied and got the role.
Unilever encourages employees to spend 15-20% of their time on cross-functional projects through a platform called flex-work. This allows for mobility across roles.
Flex-work has unlocked 500,000 hours of employee engagement and led to 41% improved productivity since 2018 when launched.
It also helps reskill employees for roles automation may replace, which is important as 50% of jobs may need reskilling by 2025.
Flex-work is an example of an internal talent marketplace (ITM) that allows companies to tap diverse skills within to work on projects.
ITMs motivate employees to develop skills and managers to access diverse capabilities. They complement traditional HR and are a tool for digitally transforming talent practices.
The potential benefits of ITMs include higher satisfaction, retention and improving companies’ talent brand by promoting internal career development.

So in summary, the text discusses how Unilever uses an ITM platform called flex-work to facilitate cross-functional mobility and reskilling of employees, unlocking significant benefits for both employees and the company.

An internal talent marketplace (ITM) allows organizations to more flexibly deploy talent where most needed, such as shifting employees between roles or duties. This helps avoid layoffs and underutilization of skills.
Many companies have untapped potential in employees’ side projects. An ITM can encourage employees to apply those skills to organizational problems and encourage cross-pollination of ideas.
To encourage adoption, organizations should share success stories from the ITM widely. Traditional silos need to be broken down to allow for collaboration.
An ITM can improve engagement, retention and career development by empowering employees with more opportunities to learn and grow.
Setting up an ITM requires executive support, assessing current talent practices, establishing skills mapping, and envisioning new operating models to support flexible talent deployment at scale.
Pilot programs can test out an ITM, such as Commonwealth Bank of Australia’s platform for employees to submit and collaborate on ideas. Data and talent can be more flexibly applied to better serve customers.
NASA used an internal platform called NASA@Work to crowdsource solutions to challenges. This helped identify an existing prototype that saved $1.3M and 3-5 years by bypassing traditional IP processes.
NASA incentivizes participation through “cool experiences” like interacting with Mars rovers, since it cannot provide cash bonuses as a government agency.
Feedback and recognition are important for engagement. Praising effort and providing constructive criticism can motivate participants even if their ideas weren’t selected.
Internal recognition like noting contests in performance reviews and having senior leaders acknowledge participation helps signal initiatives are being taken seriously.
To mitigate biases, NASA had challenges evaluated by both subject matter experts and non-experts to capture novel ideas that might challenge existing approaches.
Crowd voting can improve participation, transparency and help ensure all ideas are considered rather than just those managers prefer. It’s also important not to close initiatives too early before all ideas are captured.
Managing the process closely, involving diverse judges, and collecting ongoing input helps expand the internal talent marketplace over time through innovative collaborative tools.

Here are the key points about scaling an internal talent marketplace (ITM):

Scaling is the most difficult phase of transitioning to a networked organization. You need to build on what worked in previous assess, learn and build phases.
Replicate successful practices and solutions across different parts of the organization, but do so gradually rather than all at once to avoid overreach.
Establish a center of excellence (COE) to lead and manage the change process. It gives the initiative structure and home within the organization.
Change mindsets first before attempting large-scale rollout. Havas tried to force adoption without addressing culture and understanding, leading to failure.
Small companies can scale more easily since the organization is less complex. But large firms need a phased approach over time.
Most organizations using open talent today are small-to-midsize businesses spending on average $8,000 annually on platforms. Very few large companies have fully scaled networked solutions.
Executive sponsorship is important to promote talent renting and reinventing structures based on skills rather than roles when scaling.

So in summary, the key is going gradual, building on successes, addressing culture change, and having a COE to guide the process as an organization scales its use of an ITM.

Here are the key points about scaling digital networks and open talent from the passage:

Deloitte scaled use of the talent platform Experfy across the company through a process-focused approach led by Balaji Bondili. They “sold it door to door” and introduced goals and rewards into performance evaluations to encourage use.
NASA scaled open talent programs like Centennial Challenges through cultural shifts led by Jenn Gustetic. The open approach allows for broader upskilling, knowledge/network growth, and more creative problem-solving.
Effective communication is key to scaling open approaches. Leadership must craft narratives to appeal to different audiences and address concerns directly. Transparency and sharing results/lessons builds trust.
The organizational structure, reward systems, operations, and training need to align with and support the open talent approach for real behavioral change. Leadership, ambassadors, and feedback loops are important.
The COLOR framework outlines best practices for scaling open talent through communications, organizational structure, leadership, operations, and relationship-building both internally and with external partners. Monitoring progress and celebrating successes also helps adoption.

In summary, the passage discusses how companies like Deloitte and NASA have effectively scaled open talent programs through cultural shifts led by strong internal champions, communication of benefits, and organizational alignment behind the new approaches. The COLOR framework provides guidelines for other companies looking to scale digitally.

While new structures like open talent initiatives may be put in place, the existing organizational culture can undermine them if people revert to old behaviors and beliefs. Research shows many executives are afraid to innovate or change culture.
To successfully scale open talent, five guidelines are suggested: start at the top with executive support; involve all levels of the organization for input; model new behaviors to drive cultural change; provide continuous engagement; and leverage informal influencers both inside and outside the organization.
Commonwealth Bank of Australia is used as an example, utilizing an internal platform called Unleashing Innovation to crowdsource ideas from employees and customers, engage people through voting and feedback, and implement successful ideas. Over 25% of employees actively participated.
Leadership is identified as paramount for scaling open talent. Leaders must drive the effort through passion, modeling openness, and taking responsibility for both successes and failures to drive learning.
James “Hondo” Geurts is highlighted for transforming a secretive military organization through his open talent platform SOFWERX, bringing in outsiders through challenges, scaling engagement networks, and promoting entrepreneurship to accelerate innovation in a traditionally bureaucratic system.

This summarizes the key points about UST’s efforts to scale open talent:

UST is a large technology services company that faced talent shortages exacerbated by the Great Resignation.
COO Manu Gopinath tasked VP Vinod Kartha with exploring open talent to address this issue. Kartha partnered with Open Assembly CEO Barry Matthews.
Kartha quickly formed a COE and began pilots with Upwork to source freelancers for marketing work.
Kartha pushed for a disruptive rather than incremental approach, seeing open talent as a necessity given remote work trends.
A key challenge was ensuring client data security when using remote freelancers. Kartha solved this with a virtual desktop infrastructure (VDI) solution.
UST now has answers for common client questions around privacy, security, verification and interviews when using open talent.
The benefits grow exponentially when an open talent ecosystem is shared with clients and suppliers, not just used internally. UST refers to open talent as a “plus-one” to augment existing talent.

Here are the key ideas about accelerating the adoption of open talent:

Leaders need to continually push the boundaries of their mindset towards abundance rather than scarcity. This means recognizing that change and discomfort can breed innovation, and avoiding failure is not the same as pursuing success.
An abundant mindset believes talent, creativity and knowledge can be developed through learning and experimentation, not something fixed or innate. It sees outsiders as potential collaborators, not threats.
Leaders should refocus culture shifts around tasks, not talent. Take smaller steps first to test new approaches, like what Kilian Jornet did in building skills for Everest.
AI will augment but not replace talent. It offers new ways to match tasks and talent effectively. Leaders must partner with talent in how AI is developed and used.
To accelerate adoption, clearly communicate the benefits of open talent approaches for tasks, costs and client/business value. Give choices and make options tangible through comparisons.
Scale through a learning-focused center of excellence that continually assesses, experiments and builds on what works across the organization. Maintain momentum while exploring new pathways.

The key is for leaders to keep challenging assumptions through an abundant mindset focused on tasks and talent as partners, not competitors or possessions. Start small, learn fast and scale solutions that create value for all.

Leaders who embrace an abundant mindset are not afraid to fail and have the resilience to tweak failed ideas and try again. They also have the fortitude to cut losses on ideas that don’t work out after sufficient testing.
They are proactively focused on continuous development and adding new functionality. They encourage experimentation and value potential over rigid processes.
They are adaptable and constantly improve existing processes/systems while also adopting new ones to help achieve organizational goals and vision.
Learning is a top priority and they keep up with latest technologies/trends, sharing what they learn to inspire others to continuously develop.
They are “open talent super users” and leverage open talent platforms themselves, not just for their organizations. They lead by example in embracing more networked and flexible approaches.
The passage advocates for shifting from a focus on talent/hiring to focusing on tasks - breaking down work into specific tasks that can be accomplished flexibly using both internal talent and external contractors as needed.
This represents a mindset shift for many leaders accustomed to traditional hiring and staffing models, but is presented as necessary to thrive in today’s dynamic environment.
Leadership of networked organizations can apply lasting principles to different areas by using talent outside their normal boundaries to solve difficult problems. Examining issues deeply connects root causes to potential solutions.
Most leaders know they have a talent problem but don’t realize how self-made it is. They must question assumptions and ask the right questions, like defining problems.
To compete with open talent, organizations should rethink the mantra “don’t bring problems, bring solutions.” In this climate of ubiquitous solutions, leaders should focus on drafting the right questions and solving issues given available talent.
Balaji Bondili of Deloitte couldn’t find enough in-house talent, so he used platforms like Topcoder. This led him to create Deloitte Pixel for open talent hiring. It threatened partners’ compensation system but Bondili gained support by proving its value.
Bondili used Experfy to access data scientists when in-house hiring took too long. This success helped save Deloitte Pixel by increasing revenue and profit. Bondili had to address doubts about open talent’s ability to scale by rerunning past projects with better results.
The passage discusses the mutual reliance of AI and human judgment for accurate decisions, as well as new roles that blend both artificial and human intelligence through deeper contextual knowledge. Companies now assess skills over just qualifications for talent.
Organizations are increasingly using generative AI and technology-enabled platforms to access open talent workers in a more efficient way. Generative AI tools can help automate repetitive tasks for content creation, marketing, and customer engagement, freeing up workers’ time.
Emerging platforms are leveraging AI capabilities like NLP, computer vision, and GANs to go beyond traditional matching algorithms. AI can analyze large amounts of data to better understand customer needs and how various workers on different platforms may be suitable matches.
Some platforms are implementing testing and evaluations to more accurately assess workers’ skills beyond just past ratings and self-reported profiles. Skill ratings could become transferable between platforms.
While AI enhances capabilities, organizations still need protocols to ensure responsible usage and human oversight. Generative AI should aid, not replace, workers. A collaborative relationship between humans and AI is important.
Effectively scaling open talent programs requires centers of excellence to manage the transition and transformation, which remains a challenge for many companies. Persistence is needed to fully realize the long-term benefits of an open talent strategy.
The passage discusses the potential impact of AI, specifically generative AI tools like ChatGPT, on open talent/freelance workers.
There is a risk that many knowledge-based jobs could be replaced by AI, threatening some workers’ job security. However, AI can also increase productivity by automating routine tasks and freeing up workers to focus on more valuable work.
Skills related to interpreting context, boosting innovation, testing/editing AI outputs may become more valuable for workers to have as AI capabilities advance.
Natural language processing tools will also be important for analyzing ideas/feedback from focus groups, hackathons, employee surveys more efficiently.
Skills development will need to be more continuous and tailored to new opportunities emerging from a more fluid workforce environment enabled by technology. Digital training will be important to guide workers.
A balanced approach is needed to manage the impact - using AI to increase productivity but recognizing its limitations, and supporting workers’ skills development and ability to take advantage of new opportunities. Both human and AI contributions will likely be combined in work.
Creating a positive perception of open talent solutions and transforming them into systemic processes within an organization will be essential for future-proofing R&D.
Even when senior leadership supports open talent, not everyone will be convinced. Formal training can help change mindsets and perceptions after a policy change from executives.
Piloting different open talent approaches like expert crowdsourcing or innovation contests can help align with what the company needs at different times.
Navigating internal policies around funding, HR, etc. will always present barriers, so focusing on results and allowing policy to follow practical implementation is important.
Building complete transparency like NASA’s Center of Excellence for Collaborative Innovation (CoECI) did by using government credit cards helped prove they operated openly within existing rules, gaining acceptance.
Transforming an organization’s culture from within through perseverance and incremental opportunities is key to successfully implementing and scaling open talent solutions long-term.

The key idea is that future-proofing R&D requires positive perception of open talent as a systemic process, which involves changing mindsets, aligning initiatives practically, navigating policies pragmatically through results, and transforming culture from within over time.

Here is a summary of the article “The 8.5 Trillion Talent Shortage” by Marc Laouchez:

The article discusses a report by Korn Ferry that predicts a global talent shortage of over 85 million skilled workers by 2030.
This talent shortage is estimated to cost the global economy $8.5 trillion in unrealized annual revenues by 2030 due to lack of skills needed.
Factors contributing to the shortage include an aging population, lack of reskilling and upskilling of current workforce, and lack of younger workers entering occupations facing shortages.
Industries facing the biggest shortages include technology, healthcare, construction, and manufacturing. Roles in high demand include engineers, coders, welders, nurses, etc.
The talent shortage poses major challenges for businesses in filling roles crucial to their growth strategies and global competitiveness. It also threatens economic growth and productivity gains.
Solutions proposed include increased investments in education and vocational training, use of technology to automate roles, fostering immigration for needed skills, and reskilling/upskilling current workforce through lifelong learning.

In summary, the article discusses a major projected global talent shortage of over 85 million skilled workers by 2030 according to Korn Ferry estimates, potentially costing the world economy $8.5 trillion. It analyzes factors driving this shortage and proposes solutions to address the shortage through education, training, technology and immigration changes.

Unfortunately there is no summary provided for the specific interviews listed. The passage mostly lists references/sources without providing details about their content.

It lists several interviews that were conducted by John Winsor in 2022-2023 as references.
It also lists some academic articles and cases as references for different chapters.
At the end it lists several interviews conducted by Jin H. Paik in 2022-2023 as references for the conclusion.
No summary or context is given about the actual content or findings of the specific interviews listed. They are simply listed as references without more context.

Here is a summary of the key points about managing crowdsourcing contests from the provided text:

Managing crowdsourcing contests involves roles like owners, sponsors, and managers who are responsible for running the contests.
Important aspects of management include setting timelines, goals/objectives, specificity of the problem or task, platforms used, and assessing performance/results.
Metrics like number of submissions, quality of submissions, and implementation of solutions help evaluate the success of a contest.
Management should consider when to run contests, how to generate solutions, and how to structure responsibilities and rotational training.
Factors like having clear ownership, using standardized processes and templates, and managing multi-vendor contracts can impact effective management.
Running contests at companies like NASA has shown they can produce valuable solutions when properly managed. Overall performance and results depend on how well the contests are run and managed.
Open talent refers to incorporating external independent contractors, freelancers, and crowds into an organization’s workforce on an ongoing basis. It provides benefits like access to new skills and agility.
Early adopters of open talent at organizations like NASA, Deloitte, and Microsoft saw benefits and used it to accomplish projects. Understanding open talent requires assessing its fit with an organization’s culture.
Elements of open talent include platforms for finding and engaging external talent, partnerships, assessments, and operating principles. Integrating it requires addressing culture, operations, and leadership.
Mindset is important - moving from fixed to growth mindsets helps adoption. Assessing open talent use helps determine next steps. Networked organizations and centers of excellence facilitate open talent.
Platforms proliferated and enable finding and engaging talent. Internal platforms can build knowledge sharing. Contests are used for projects utilizing skills crowds. Metrics help evaluate performance.
Scaling open talent requires addressing governance, processes, partnership management, and organizational structures to support distributed work. Leadership modeling open talent helps adoption.

Here are the key points of gratitude expressed in the acknowledgments:

Thanks his partnership with Alex Bogusky at Crispin Porter + Bogusky for galvanizing his passion for open talent.
Thanks the launch of Victors & Spoils with Claudia Batten and Evan Fry for allowing him to pursue his vision for open talent.
Thanks David Jones, now CEO of the Brandtech Group, for helping take his vision for open talent global.
Thanks Karim Lakhani, Mike Tushman, and Bharat Anand from Harvard Business School for helping him think more strategically about open talent and encouraging him to create adoption models.
Thanks Karim for giving him the opportunity to work at the Laboratory for Innovation Science at Harvard (LISH) where he met Jin Paik and began planting seeds for his future work.

In summary, the acknowledgments express gratitude to various collaborators and influencers who helped support and advance the author’s work and thinking on open talent over the years through opportunities, guidance, and partnerships.

Here is a summary of the information provided about the authors:

John Winsor is a globally renowned thought leader in marketing, open talent, collaboration, cocreation, and crowdsourcing. He is the founder and chairman of Open Assembly, which connects organizations with freelance talent. He is also the executive-in-residence at Harvard Business School’s Laboratory for Innovation Science.
Winsor has authored several books on topics like innovation, marketing, and the future of work. He regularly shares his insights through speaking, writing, and podcasting.
Jin H. Paik has worked at the intersection of digital transformation, AI, and the future of work for over 15 years in both academia and industry. He is a co-founder of an AI consultancy and was previously head of labs at Harvard’s Digital, Data, and Design Institute.
Paik held previous roles directing research projects with organizations like NASA and Harvard Medical School. He has taught courses at Harvard.
Paik holds degrees from the University of Michigan, Harvard, and New York University, where he studied innovation and leadership. He now lives in Florida with his wife and four children.

#book-summary

Open Talent Leveraging the Global Workforce to Solve Your Biggest Challenges - John Winsor