Disruptive technologies create tremendous opportunity for organizations to become smarter, more agile, more flexible, and more responsive. But as employees deploy new applications, they are encountering challenges that create reputational and even financial risks for their organizations.
Some companies that don’t see technology as their core business may assume …
Flexible delivery of emerging technologies to drive business outcomes is fast becoming today’s competitive battleground.
Deloitte research found that 56 percent of CIOs expect to implement Agile software development, DevOps, or a similar flexible IT delivery model to increase IT responsiveness and help spur broader innovation ambitions.
But there is …
Making the right decisions, patient by patient, day by day is at the core of what every healthcare provider must do. These decisions often have to be made fast, under the pressure of extremely high workload, complex bureaucracy and long working hours.
Quick and easy access to information is critical here, and digitalization can contribute significantly to improving decision-making along the whole patient pathway. With modern and connected healthcare solutions, the complete patient history and previous tests become available anytime to be able to decide on the right next steps.
This kind of data-based clinical decision support is predicted to have substantial impact as we move towards value-based care that focuses on improving outcomes, increasing efficiency and reducing costs.
In the U.S., the Centers for Medicare & Medicaid Services (CMS) are increasing their emphasis on the use of clinical decision support (CDS) tools, recognizing their role in reducing care costs and improving care quality. Clinicians who are already using these decision tools report they help significantly in diagnostic and therapeutic decision-making because they provide access to relevant patient information and to diagnosis-specific order sets. This information enables adherence to evidence-based guidelines, risk stratification and treatment options. They also report use of the tools can stimulate provider-patient discussions about appropriate care.
We at Siemens Healthineers know that digitalization can make lives of caregivers and patients easier. However, it is neither efficient nor sufficient to add one isolated solution to the other. Healthcare providers can benefit even more if they use a platform that integrates the global expertise of a reliable partner with healthcare domain ‘know-how’ based on a large, installed base and long-term experience.
The teamplay digital health platform of Siemens Healthineers is designed to act as a scalable backbone giving a kickstart into digitalization, improving operational efficiency and reducing costs by providing the right data at the right time.
Moreover, caregivers become future-ready by getting the chance to apply latest innovations like AI to support decision-making and better care along the entire patient pathway.
Siemens Healthineers offers a variety of products in its portfolio to support clinical experts in diagnostic and therapeutic decision-making. For instance, the AI-Rad Companion*, a family of AI-based reading assistants, supports radiologists in routine tasks. this is too technical for our audience
The semi-automation of these reading processes with repetitive tasks and high case volumes helps to ease the daily workflow, so that experts can focus on more critical issues.
The portfolio also includes magnetic resonance imaging with solutions for analysis and prostate biopsy support. Siemens Healthineers solutions not only help to ease routine activities, but also support decision-making in multidisciplinary teams so they can find the best individual treatment options.
The first application AI-Pathway Companion* is designed for oncology and prostate treatment. Further applications for lung cancer and cardiovascular are currently in development and will follow. The AI-Pathway Companion* applications help to match the data available for the individual patient with the guidelines to identify the possible treatment approach and facilitate the appropriate disease management.
Learn more about innovative digital health solutions and the possibilities of AI by clicking here.
From left to right: Narumi Teizo, CEO of glafit, Okai Daiki, CEO of Luup, Hyuga Ryo, CEO of mobby ride.
When the glafit E-bike was introduced in 2017, it offered a new experience on Japanese roads: a ride on a lightweight two-wheeler that could switch between motor mode, pedal mode and hybrid mode and then easily collapse and fold up for storage.
But the E-bike came with a challenge: How should the use of E-bikes be regulated? Japanese traffic laws covered bikes, scooters and motorcycles, but it was unclear just where and how this hybrid innovation should operate in all its different modes.
Regulatory review threatened to slow the passage of new rules that would allow the E-bike onto bike lanes or other off-highway paths – crucial to its use when riders want to switch to pedaling in crowded cities or rural settings.
But under the Japanese government’s new “sandbox” program, glafit entered a demonstration program with Wakayama City so the E-bike can operate in multiple settings while the city and company collect use and safety data that can be used to create new regulations.
Wakayama City Hall official Kobayashi Kenta, Section Chief of the Industrial Policy Division, said the experiment will both support a hometown startup – glafit is based in Wakayama – and encourage the use of hybrids, which could help the environment and the local community.
“We decided by supporting glafit, we have the possibility also of solving social issues,” he said. “We have a shortage of transportation in our rural areas, and we need to diversify affordable transportation methods for elders. Inside the city, we think pushing [forward] with the usage of these bikes will also relieve traffic congestion and reduce the environmental footprint of vehicles.”
The Government of Japan introduced the sandbox framework in 2018 as one mechanism for regulatory reform to support the development of innovative technologies and business models in Japan. The framework does not limit the area of regulations, but currently covers those in financial services, the health care industry, mobility and transportation.
Any company, including overseas companies, can apply to conduct demonstrations under this new framework and test the possibilities of innovative technologies such as AI, IoT or blockchain for future business, especially if they cannot start new businesses using these technologies due to existing Japanese regulations. The projects are monitored, so the government can review the social and economic viability of the technology, how the technology fits in with current regulations, and what changes need to be made.
Japanese experiments in innovation with blockchain and fintech have garnered attention for the sandbox framework. But the program has attracted a wide range of applications that are taking on social challenges as well as creating economic growth in Japan, said Deputy Director-General Kazeki Jun of Japan’s Economic Revitalization Bureau.
The 128 companies that currently operate under the sandbox framework are exploring a wide range of projects, from finance and insurance to domestic recycling, health care services and transportation experiments.
“We have an aging population and a shrinking labor market, but with technology we can develop new approaches,” Kazeki said. “We want to create a broad range of projects with flexibility to experiment quickly.”
Speeding up the approval cycle for projects will increase investment and economic returns, he added.
“In the past it has been difficult – particularly for startups – to do business, and the path to approval was not clear,” he said. “Investors were not sure they should invest in these companies. It took a long time – and time is money. Now we can move quickly, and if a company is approved for a project, the investors know this startup can obtain results.”
The sandbox has attracted a number of companies in the transportation space. mobby ride, for instance, has introduced an electric kickboard scooter that integrates GPS and IoT sensor technology to operate in a controlled area, so that “share service” scooters could easily be set up in a defined urban area. Speed limitation in a specific area is also possible.
In Fukuoka City, which has become a national strategic special zone for scooter use, sandbox regulations allow city leaders to learn whether the vehicles can help solve traffic and parking problems in a compact town center where subway stations are few, bus routes are sometimes complicated and it can be difficult for tourists to navigate. The company is also running a demonstration project at Kyushu University Ito campus to collect data about the scooters on different types of roads.
“There are many opinions about the electric scooter, whether it is dangerous and just how safe it is, because it is a new vehicle,” said Atake Shuichi, who is in charge of mobby ride’s business development and public relations. “Now we can obtain quantitative data, such as how many accidents occur (of course, zero is preferable), and use it as negotiation data for future system reforms.”
Another mobility company, Luup, also is running an experiment with shared services vehicles, as well as a sandbox experiment program on a college campus that can evaluate the safety of several types of vehicles for both young users and the elderly.
“Considering safety aspects for the elderly is important because Japan is an aging society,” said Matsumoto Misato, Public Relations Manager of Luup. “Aspects such as speed and the balance of the vehicle are important. The university campus was a good testing ground because it contains both walk paths and car roads.”
For startups, whether in transportation or other industries, the sandbox is an opportunity to introduce new ideas and technologies and speed up regulatory reform, said CEO Narumi Teizo of glafit.
“Thanks to the approval of the regulatory sandbox framework, it is now possible for us to demonstrate the ride on lanes other than car lanes in our customers’ everyday use,” he said. “In addition, the approval under the sandbox framework makes our problems widely recognized and understood by stakeholders, including policymakers in Tokyo, the mayor of Wakayama, police departments, media, and current and future customers.”
“Without the sandbox framework, nobody would know about the regulation problem and the challenges we have faced for so long. We hope that this demo leads to regulatory reform in the near future.”
Few industries have been forced to navigate the disruptions arising from digital transformation the way that the publishing sector has in recent years. As advertisers moved from the physical world to digital, the strongest publishers survived by continuing to adapt. They’ve transitioned to programmatic advertising, where advertisers and publishers use a common digital platform to buy and sell advertising online.
Despite this shift some publishers continue to sell the majority of their premium ad products in more traditional ways, and struggle to migrate to programmatic advertising and automated reservations, a type of programmatic advertising that involves the direct sale of reserved ad inventory between buyers and sellers.
The publishers that have successfully pushed past these barriers are satisfying growing agency and client demand for automated guaranteed while seeing real productivity impact from streamlined operations.
Read or click below to learn more about how publishers are navigating change around programmatic advertising and automated reservations.
Welcome to the HBR Audio Quick Take. I’m Julie Devoll, editor of Special Projects and Webinars at HBR. And here with me today is Bonita Stewart, vice president of Global Partnerships at Google. Bonita brings extensive consumer technology and operational experience as her career spans over two decades of digital transformation across multiple industries. Since joining Google in 2006 she has driven adoption of digital technologies within the C-Suite in the US and globally. Bonita, thank you for joining us. How has programmatic advertising evolved over time, and how does the shift to automated reservations fit into that evolution?
Bonita Stewart, Google
As you know, there’s business transformation that’s happening across every single industry. There’s been a shift from print to digital. There’s been a transition from desktop to mobile, and now we’re seeing a movement toward what I’ll call the last mile of programmatic, which is automated reservations.
Julie Devoll, HBR
What do you think is holding publishers back from taking a more proactive approach to adopting automated reservations?
Bonita Stewart, Google
What we’ve been hearing from publishers is a lot of the challenges they’re dealing with are more operational. In order to do automated reservations well, it isn’t as easy as simply just flipping a switch. With any change, it’s quite complex. In this case, it does involve the organization, which means changing the sales structure, compensation plans, evolving workflows, updating training, as well as the product technology involved. I think many business leaders today are faced with that question around, how do you actually simplify complexity?
Publishers are also facing cultural resistance. This happens with employees when they’ve grown used to doing things in the way that they’ve always been done. But, equally at the top, I think we have to take responsibility as leaders. If the senior leadership is not bought in, then it’s going to be tough for any change to gain momentum within the organization.
Julie Devoll, HBR
For the publishers that are ahead of the curve in this area of automated reservations, what are they doing so well that sets them apart? Are there any best practices that other publishers can learn?
Bonita Stewart, Google
Absolutely. Organizations that have successfully made the shift to automated reservations, they use a couple of strategies to set themselves apart. One, they use a cross functional team approach. They secure buy-in from all of their account reps by showing them the value of investing in their careers. This is significant when you think about the importance of technologies in terms of enhancing your skills portfolio. Also, the leadership is involved early, they’re involved often, and they’re also invested in ongoing skills training.
Bonita Stewart, Google
The other thing that’s important to note is that these publishers also took the time to come up with a thoughtful plan, a very intentional plan, regarding the changes that are needed around the sales structure and the incentive plans. Early in the process, these early adopters defined what success with automated reservations meant to their organization so that they could track those success metrics on an ongoing basis. We all know what you measure matters.
Julie Devoll, HBR
You mentioned sales. What are some approaches publishers can take to adjust their sales teams to position themselves for automated reservations?
Bonita Stewart, Google
We’ve seen three approaches that publishers can take to adapt their sales team. One, they could decide to designate one or two key experts who understand automated reservations, and that individual could act as a liaison between the direct and the programmatic teams. We have found that this is sometimes a bit more difficult to scale, and it’s not necessarily recommended as a first approach.
Bonita Stewart, Google
The second one is, you could take your entire team, you could commingle, cross train the teams to understand automated reservations. Again, this is spreading it across, and it might take a bit more time. Then, lastly, one of the areas that we’ve found to be a highly effective strategy is to take and start by designating five to six people, depending upon your organization, to be the automated reservations specialists.
Julie Devoll, HBR
What about the buy side? What benefits are they realizing through this shift? What do buyers want to see from publishers regarding automated reservations?
Bonita Stewart, Google
Benefits by moving to automated reservations. This comes in the form of increase campaign efficiencies, which is important to the bottom line. Also, better campaign measurement across the board. They also have less operational burden. Because of these benefits, we also expect buyers to continue to shift campaigns at scale.
Bonita Stewart, Google
The other thing to note is that buyers want to work with publishers that have invested the time and the resources to properly support automated reservation transactions. Some of our buyers have commented that, as they shift to automated reservations at scale, they may only want to work with those publishers who have actually been proactive about servicing this transaction type versus a publisher who is new to the game, and they’re just dipping their toe in the water.
Julie Devoll, HBR
What is Google doing to help ease the transition to automated reservations for both publishers and advertisers?
Bonita Stewart, Google
We find it quite exciting right now in the programmatic landscape. We are leading from the front. Our teams are helping the sellers, the buyers transition to automated reservations, really, in three ways. One, it all starts with education. We’re educating the leaderships teams. We think it’s important to start from the top. Secondly, we’re providing very tactical training and education for the sales team so that they understand the product and the way to go to market. Lastly, we’re adding a product and enhancements as well as innovation.
Julie Devoll, HBR
Do you have any predictions on what we can expect from automated reservations, and more broadly programmatic, in the next five years, or even longer?
Bonita Stewart, Google
Yes. I think one of the areas that you’ll see is additional product innovation and enhancements. We’re going to continue to invest in the product in terms of bringing support for custom creatives, so it will allow publishers to sell their most custom and premium ad formats. They’re also looking for coverage around sponsorships as well as having make goods for their campaigns.
Bonita Stewart, Google
The other prediction that we have is, in order to run these campaigns, having the end-to-end reporting will be quite important for troubleshooting these campaigns. We’ve seen that, in making this transition over time, and looking at transformation, it will come fast. In fact, MAGNA Global is expecting programmatic to make up 84% of US ad sales by 2020. That’s up from 55% today. That’s a massive shift that’s occurring in this last mile. As well as, BCG predicts that the traditional direct reservation selling will represent just 37%.
Bonita Stewart, Google
Within Google, we can say that we’ve seen, globally, all of our programmatic guaranteed reservations from publisher earnings have grown over 2X year over year in 2018. We’re very excited. This is another business transformation. It is the last mile, but it’s quite exciting in terms of the benefits to the buyers, the sellers, as well as the overall end-to-end business operation.
Julie Devoll, HBR
Bonita, thank you so much for joining us today.
Bonita Stewart, Google
Thank you for having me.
Julie Devoll, HBR
If you’re interested in learning more, you can view the full Harvard Business Review Analytic Service white paper, Arming Publishers to Migrate to Automated Sales Processes, at HBR.org by clicking here.
Leaders, revising their five-year plans every quarter, are constantly seeking ways to reinvent their companies and stay ahead of the pack, given competitors of varying capabilities and scale and customers who expect more for less. For many companies, the answer is large-scale global transformation. Eighty percent of CEOs in one study claim to have transformations in place to make their businesses more digital; 87 percent expect to see a change in their operating models within three years.
But leaders see establishing those operating models as their top challenge in achieving digital transformation. So how might they move forward? A review of transformations across industries reveals a common theme: Successful transformations realign the organization to a singular vision; failed endeavors typically do not.
An organization has a far better chance at succeeding when its operating model—or how the organization creates value—is aligned to its strategy. And this means that for transformations to succeed, leadership teams should examine and possibly revise their organizations’ operating models. Given the pace of change, executives may struggle to determine where to place bets, how much to invest, and when to do it. Wait too long, and they risk seeing market value quickly erode; invest inefficiently or ineffectively, and they could face a cash crunch or investor backlash.
The good news for companies born before the digital era is that they often quickly understand the value in transforming to agile, adaptive, and responsive enterprises because they already have the other intangibles in place: strong brands, an entrenched customer base, established sales methods, and partners—suppliers, distributors, and technology.
Successfully driving these changes, though, depends on executives addressing a range of organizational barriers and risks—particularly functional silos, incomplete enterprise data, and a product-out (versus a market-in) philosophy of value creation. A well-designed and purposefully executed enterprise operating model can help companies balance growth with risk and overcome organizational barriers.
Target Operating Model Poll any number of executives, and you’ll likely find yourself with as many definitions of operating model. But most commonly, operating model transformations are associated with cost takeouts or organizational redesigns. While these can be byproducts of an operating model shift, the common associations are myopic and discount the full value.
Instead, leaders should think about their operating models as their unique set of capabilities aligned to the enterprise’s strategy, with skilled leadership teams, tailored metrics, unique investment profiles, and tight coordination across the value chain.
What Work Needs to Be Done? In moving forward with a digital transformation, the first step is to identify the holistic set of capabilities required to meet the enterprise’s strategic ambitions. The capability set should include both existing capabilities and new ones (as needed) and address front-, mid-, and back-office functions across all product lines.
For example, product strategy is a capability that creates product road maps to realize customer requirements; campaign management is a capability that launches, measures, and reports on the success of marketing campaigns. When brought together, capabilities comprise a capability map, representing the collective set required to execute against the strategy and business model. A capability map provides a foundation on which organizations can build their target operating model. It can be used to determine skill set requirements, hire talent, set performance metrics, build teams, and identify partnership opportunities.
Where Does the Work Get Done? Once leaders have established the capability map, the next step is sourcing capabilities. Several capabilities will likely already exist—some mature or fit-for-purpose, others recent arrivals. This step is often the most difficult to execute, as companies can be resistant to changing their existing ways of working when instead they can leverage the opportunity to untether themselves from legacy processes and technologies.
Enterprises typically have four sources for capabilities: They can develop, transform, or mature them internally (use as is); they can acquire capabilities through targeted hires or outright M&A; they can partner to access them; or they can outsource the capabilities and have them delivered as-a-service. The decision to develop, acquire, partner, or outsource is a critical one, since each lever provides organizations with unique advantages. Executives should consider the following in making decisions:
• Speed. How urgently do we need this capability? • Control. How important is it that we control the outcomes? • Specificity. To what degree do we need to tailor this capability to our business? • Competitive advantage. To what extent does this capability provide us an edge over competitors? • Operational leverage. How much do we want to take on in fixed/on-balance-sheet commitments?
Who Does the Work? This step involves allocating work to the most efficient parts of the organization.
Capabilities typically provide one of two types of value: demand-side or supply-side. Demand-side advantages drive increased attention toward a company’s offering, driving up pricing, revenues, and margins. These include capabilities such as sales, product engineering, recruiting, branding, and corporate strategy, where processes and skill sets are less repeatable, and where talent is a significant driver of value. Supply-side advantages allow a company to operate more effectively and get the most out of resources. These usually include areas in which value is related to scale, such as sales-quote capabilities, self-service, accounting, and manufacturing.
Similarly, the relationship to the business is twofold. Capabilities significantly tethered to the line of business often rely on some expert ability such as localization, R&D, product marketing, or technical sales. Those with limited relationships to the business—for instance, M&A, e-commerce, and supply chain management—rely on generalist skill sets and play across the enterprise.
Each capability has a different place within the operating model, and companies can opt for different ways to deliver similar capabilities. Those decisions should be closely linked to the strategy.
How Can Organizations Drive Better Outcomes? Operating models are ever-evolving, driven by feedback from employees and customers, the effectiveness of business processes, and evolving competitive landscapes. Leading organizations augment their capabilities through simple cross-functional processes, hyper-focused incentives, and best-in-class tools to drive simplicity, clarity, and speed in execution.
Our research points to at least six ways to potentially increase your chances of building a model that can help guide a successful digital transformation:
• Nominate and empower function and business leaders early on to drive the cultural change required across the organization. • Define clear roles and responsibilities across businesses, regions, and functional support groups. • Create complementary incentives and goals for businesses and functions to reduce conflict and optimize resource allocation. • Establish cross-functional debriefs to keep relevant parties informed, and nominate an owner to manage the process early. • Institute a governance model with clear KPIs for each leadership team—one that supports quick, independent decision-making. • Standardize resource and knowledge exchange to ensure that skill sets are cultivated and proliferated.
Transformation demands that leaders develop a clear sense of their strategic ambitions—where to play and how to win—and the business models they wish to employ, including target customer segments, channels, pricing, and delivery models. There are many questions to be answered. Both the strategy and the business model directly influence the operating model design.
Organizations that try to short-cut their way to a new operating model may find the design ineffective and the implementation lacking employee traction—or worse, dilutive to value.
Most critically, an organization’s operating model must be inextricably linked to the corporate and business-unit strategy and varying business models. The operating model is the anchor for the enterprise and is critical to the strategy’s effectiveness and longevity. And understanding how your organization maps onto the model is key to an effective digital transformation.
To learn more about how successful transformations align models with clear strategies click here.
Most of today’s jobs will not be here tomorrow. The World Economic Forum predicts that 65 percent of children entering primary school today will ultimately end up working in completely new job types that don’t exist today.
This represents an opportunity for government organizations and employees to intentionally redesign work and jobs to not only accommodate the role of technology and machines, but also to design for broader economic, workforce, and societal shifts.
For example, a government HR manager who now only hires full-time employees may need to start tapping into a pool of crowd workers or gig workers for certain types of work. A procurement department may now need talent with blockchain expertise to manage secure supply chains. Or given the increased use of algorithms in government systems, agencies now need to prevent algorithmic bias from creeping into public programs.
In a recent Deloitte survey of more than 11,000 business leaders, 61 percent of respondents said they were actively redesigning jobs around artificial intelligence (AI), robotics, and new business models.
The prevalence of automation, as well as machines working alongside humans, is increasing in government too. According to the US Office of Personnel Management, almost one-half of government agencies’ workloads could be automated and close to two-thirds of federal employees could see their workloads reduced by as much as 30 percent.
That makes it an imperative to focus not only on how humans and machines can best collaborate at work, but also how that collaboration can enable better work processes and create more value.
Although there are many ways in which humans and machines can work together, we typically identify the human as the supervisor or the primary worker. This view can be limiting. Machines work for us, with us, and sometimes they even help guide us.
To harness the real potential of the human-machine partnership in the workplace, we should consider the full spectrum of possibilities. Machines are already taking on a wide range of routine, manual tasks. When this lower-value, tedious work is automated, opportunities are created to reduce cost and redeploy staff to more valuable activities. For instance, the Food and Drug Administration’s Center for Drug Evaluation and Research (CDER) uses robotic process automation (RPA) in its drug application intake process. This slashed application processing time by 93 percent, eliminated 5,200 hours of manual labor, and saved $500,000 annually.
Automation also allows jobs to be split up between humans and machines. When a job is broken into steps or pieces, automating as many as possible, humans are left to do the rest and, when needed, supervise the automated work. U.S. Citizenship and Immigration Services uses chatbots to answer basic questions. This frees up time for employees to respond to more complicated inquiries.
There are many lenses to use when thinking about these partnerships:
Shepherd: A human manages a group of machines, amplifying their productivity. In this instance, a human might manage a fleet of autonomous buses. Or a nurse manager could oversee a group of hospital robots.
Extend: A machine augments human work, combines their strengths to achieve faster and better results, often doing what humans simply couldn’t do before. For example: A department of human services could use cognitive technology to help predict which child welfare cases are likely to lead to child fatalities. Once high-risk cases get flagged, they are carefully reviewed and the results are shared with frontline staff, who then choose remedies designed to lower risk and improve outcomes.
Guide: A machine prompts a human to help them adopt knowledge. Machines help humans learn new knowledge and skills; or adopt desirable attitudes and behaviors. For instance, a researcher can set up a custom digital assistant that not only knows what current research a person is doing but can also crawl the web for old and new research relevant to the topic that the researcher might not be aware of.
Redesigning the Work Work redesign is fundamentally about making sure that government agencies—their work, workforces, and workplaces—keep pace with shifting opportunities and needs and prepared for the future. But architecting jobs can feel overwhelming without a clear idea of where to start. We recommend these three steps:
Step One: What Will the Future Look Like for Your Organization? The first step is to think long-term. Imagine what the future could look like, determine how this could impact your organization, and plot out the course. There are a host of external forces—technological advancement, automation, changing customer demands and behaviors, or the rise of new business models—that could impact how your organization will deliver on its mission in the future. Imagine you’re looking out to 2030. People are living longer and staying in the workforce longer. The composition of the workforce has changed too. Digital natives have joined the workforce, as well as more freelancers and contingent workers. Technology is omnipresent and AI, augmented reality, the Internet of Things (IoT), and robotics are integral parts of the workplace. Organizations may have new analytical capabilities, thanks to unprecedented volumes of data and increased computing power.
Think about what all these factors and the changes could mean for your organization. By zooming out, you will explore beyond what technology and other disrupters can allow you to improve on what you’re already doing. And envision how these changes could enable you to unlock entirely new outcomes.
Step Two: What Work Should Be Done Differently? Given the vision you’ve imagined for your organization and the role disrupters can play, think about the current state of work across the organization. Ask: “What might we do differently across different jobs and work units to achieve greater impact and desired outcomes in the future?”
The process of deconstructing (and then reconstructing) work, and then defining the new roles that can support the new disciplines, can be broken down into four focused pieces with a simple framework: start, stop, change, and continue (S2C2). Some work will be catalyzed by technology and other disrupters and start anew. Meanwhile, some dull, dirty, or dangerous work may be automated or stopped entirely. Some work will be still critical to mission and business outcomes but will be changed by the application of technology. And, lastly, some work will continue relatively unchanged.
For example, a government agency might start hiring gig workers for certain skills such as data science. A transportation department might stop installing and maintaining traffic lights when autonomous vehicles become the norm. Given the dangerous nature of their work, firefighters might change how they extinguish fires and use drones and robots instead to assist them. Social workers will continue to visit their clients in their homes and build a personal connection.
The S2C2 lens can be applied to different levels of an organization—to a single role, across stages of a program, or at a high level within an organization’s department. Agency leaders can use this information to gauge the downstream impacts on jobs. Starting new activities might require leaders to create new roles while changing and stopping certain activities might require reskilling and redirecting talent.
In the future, the “matching” of evolving skills to evolving work within the context of a redesigned job should be very intentional. This can help ensure that you build a job that is a logical, holistic combination for a single person to have, rather than a somewhat haphazard mix as it often is today.
Think of it like this: What if by 2030, your current role no longer existed? How would the work get done? How would you rethink doing the work?
Step Three: Who Should Do the Work? While reconstructing work, asking the question, “Who should do the work?” can help organizations explore new talent options such as crowd workers, gig workers, or digital labor. Depending on factors such as how specialized the task is, or whether the desired capability requires a security clearance, some talent options might be more suitable than others.
To engage outside perspectives, leaders may want to develop or use a new crowdsourcing platform. Working with digital labor or AI may require leaders to select the most appropriate form of human-machine collaboration to answer the previously asked question, “Should an AI technology augment the human worker or relieve them?” All these considerations feed into work redesign.
Looking to The Future By reconstructing work, government organizations can not only capture efficiency gains through human-machine collaboration; more importantly, they can find new avenues to create value that may not have been possible before.
Future work scenarios don’t simply feature the human as supervisor of the machine. Instead, they consider the full spectrum of possibilities for human-machine pairings. And as humans and intelligent machines working together becomes the norm in the workplace, organizations have an opportunity to maximize the potential of both. This effort, when realized, can fundamentally help create better work processes for everyone and more value to taxpayers.
Read the full article here for a step-by-step guide to optimizing human-machine collaboration.
Medical researchers have leveraged technology to create major breakthroughs in the past few decades, accelerating the understanding of diseases, and their causes and treatment.
Our accumulating knowledge also has accelerated the ability to translate science into practical therapies, but there are still many challenges: while researchers seek the right drug compounds that can target and deliver treatment for specific diseases, traditional drug innovation models can be slow and come with high costs.
Japan’s rising biotech company, PeptiDream, is tackling these issues, deploying a unique proprietary drug development technology and an innovative business model that will further research on and development and manufacture of peptides to deliver new medical therapies.
“We really want to be a drug discovery engine,” says CEO Patrick Reid.
Until recently, most advances in drug delivery have focused on small-molecule and large-molecule drugs, also known as antibodies. But now macrocyclic peptides are emerging as an important new avenue.
Research Lab at PeptiDream
How are peptides different? Both small- and large- molecule drugs come with advantages – and limitations. The small molecule drugs are chemically synthesized in a lab and taken as a pill or capsule, so the active ingredient is easily absorbed into the bloodstream. Because they are small, molecules can penetrate cell membranes, making these drugs highly effective. But they can be unstable and they break down in the body, creating unwanted side effects. Formulating these drugs to take on specific new targets also can be slow and expensive.
Protein-based therapeutics (large-molecule drugs) are made by using living cells. They typically are not pills, but instead must be injected or infused. These large- molecule therapies, unlike the smaller-molecule drugs, cannot penetrate cells. But these drugs are easier to design for specific targets — typically a cell-surface receptor on the outside of the cell. However, these therapies cannot reach all required targets, and they can stay in the body too long causing side effects.
Enter peptides, compounds that consist of amino acids linked together and can be synthesized in the lab. Pioneering research by Suga Hiroaki, PeptiDream co-founder and professor at the University of Tokyo, established a way to ensure that a new kind of peptide compound can remain stable in the body and find a range of therapeutic targets with high specificity. They can also be broken down by and cleared from the body with greater specificity, making them an important new development in pharmaceuticals.
And these macrocyclic peptides can be combined, using a much larger set of amino acids than occur in nature – giving researchers the ability to experiment with many more combinations. PeptiDream’s Peptide Discovery Platform System (PDPS) is a proprietary technology that allows drug researchers to make trillions of peptide libraries. Reid describes PeptiDream as “platform company” that enables his researchers and others to make the process of discovering “hits” — the starting point for developing drugs — more efficient.
“We are not simply developing a single drug and trying to bring that all the way to approval; we are championing and developing an entirely new class of molecules,” Reid says.
The platform has created an unusual set of collaborations for PeptiDream, whose drug discovery partnerships have included Merck, Bayer, Genentech and Novartis. This collaborative network has accelerated peptides development, Reid says, creating a large wave of compounds that should move into the clinic in the next few years.
“Our network of partners has allowed PeptiDream to function as company ten to twenty times its actual size,” he says. “With more than 100 discovery programs in parallel across a wide range of diseases, targets and administration routes, we are expanding the knowledge, understanding and appreciation of these molecules in therapeutics and diagnostics and more.”
It also was crucial that PeptiDream, as a startup, was able to focus on developing the platform for peptide drug discovery, something large pharmaceuticals had not done because of the cost and the long, uncertain time horizon. Japan embraced PeptiDream, initially as a largely bootstrapped company, and then when it went public in 2013, Reid says.
“In the U.S. and Europe, we probably would have been pressured to borrow funds in order to grow faster,” he says. “Many companies in the U.S. with internal pipelines fail due to time and pressure constraints. They burn a lot of money very quickly.”
PeptiDream is now a $7 billion company and is also a founding investor in a contract manufacturing company, PeptiStar. Collaboration with other companies is crucial, says PeptiStar CEO Kameyama Yutaka, as new ecosystems for research, manufacturing and supply of peptide drugs are developed. In fact, PeptiDream, together with other co-founding investors Shionogi and Sekisui Chemical, has attracted additional ten investors as active R&D collaborators.
Kameyama Yutaka, CEO, PeptiStar
In order to accelerate the practical application and market creation of peptide therapeutics as next-generation drugs beyond biopharmaceuticals, the Japanese government also supports PeptiStar, providing 9 billion-yen (about $83 million) grant as part of the government’s program Cyclic Innovation for Clinical Empowerment, under the National Healthcare Policy. The money will allow PeptiStar, established in 2017, to become a leader in both scientific and business process innovation, says Kameyama.
“The current capacity of peptide manufacturing is limited, and it could be a big bottleneck of peptide medicine developments,” he says. “This quick fundraising will accelerate the development and commercialization of our ability to prepare the peptide compounds. And the support they have given us will also encourage many other partners in the important development of peptides.”
“Peptides have not been around very long, and as with any new technology, there is room for improvement, including costs,” Kameyama says. “We want to make production cheaper and higher quality, and collaboration is a competitive advantage. If a company established its own manufacturing facility, it would take time and money. But with a joint venture like ours, the cost and the sharing of technology and knowledge are very different.”
Both Reid and Kameyama credit the Japanese research and business ecosystem with their success. Professor Suga’s breakthrough work is just one spinoff of innovation coming from Japanese universities, where a pool of highly skilled research workers has developed.
Japan’s challenge to create peptide drug market continues. To learn more click here.