How will smart leaders create smarter systems of the future?
It starts with the people who transform the culture of how we work.
The United States has the potential to become one of the healthiest places to live, work, thrive, and enjoy a meaningful life. As a White House Presidential Innovation Fellow, my mission is centered in the “health” of how we work together, better. Alongside other public sector trailblazers like the Better Government’s pioneer leader Amy Wilson, I design new approaches that cultivate a more modern work culture. Leaders that enable innovation by introducing more productive ways for our federal communities to interact with one another are leaders of the future. Healthy relationships are the birthplace of innovation as they are the source of information exchange — a process otherwise known as communication. And it takes a lot of work in our workforce, workplaces, and workflow to communicate better and maintain innovative relationships.
When we improve the ways we communicate with one another and dissolve institutional silos, we take the critical steps needed towards transformation. As I’ve previously cited, I am currently exploring workforce innovation at the National Cancer Institute (NCI). With a FY2018 budget of $5.6 billion, NCI is composed of 30 brilliant yet separate divisions, offices, and centers, each with their own unique vision and portfolio of resources; 69 NCI-designated Cancer Centers; several dozen federally appointed committees and hundreds of subsequent working groups. Strengthening connections between these valuable yet disconnected assets is proving to be a process of many ups and downs. Reconstructing business practices is no easy lift and it can take years for the impact to become clear, as evidenced in my 15 years to help redesign Detroit’s economic fabric. Big change requires small steps over a long time horizon.
Taking my research and development at NCI as an immediate example, it is clear that we need fresh teams within a collaborative infrastructure to achieve lofty 21st-century public health goals, such as automating the ways that patients access preventative cancer interventions and therapeutic cancer treatments. Implementation of lifesaving advancements will lean on artificial intelligence (AI) to create new forms of communication to supplement the explosion of our nation’s oncology needs. Solutions, supported by AI, will help mitigate some of the risks we face. These risks include shortage of oncologists over the next decade amidst soaring cancer demands, as noted in the American Society of Clinical Oncologist’s report on The State of Cancer in America. These facts underscore the urgency of optimizing federal agency data supply chains to help all of us make better decisions, faster. If our federal workforce remains disjointed and entrenched in static silos, we will not foster clear pictures of our data and we will continue to struggle to meet the health needs of our nation.
Relationships drive the future of work.
How do our ideas about the future of work (such as The White House announcements on reskilling) evolve when we acknowledge that success hinges on improving the interactions between people? People hold the keys to unlocking the harmonization of peer-to-peer data sharing or the redesign of how we deliver service to citizens. Groundwork for smarter, more intelligent cancer care systems rest on the relationships between the people who lead the way work gets accomplished. Progress is possible when more of us (leaders) realize our problems are less about abstract “barriers” — technology, resources, policy — and more about how we interact among ourselves and communicate with one another as humans. We will succeed in developing smarter systems by establishing new and more innovative norms for three core innovation zones: 1. workforce, 2. workplaces, and 3. workflow development.
1. Workforce: keep our people at the center.
People themselves are at the source of either maintaining or reimagining our silos. In order to operationalize communication tools informed by AI, we need to connect a lot of discordant data, a dissonance that ultimately represents how we currently behave as humans. Consider how data moves at the speed of trust and trust is rooted in human relationships. If data is ultimately how we extract value from the information exchanged between people, then we’re looking at a very slow process ahead. Public health and taxpayer-supported institutional research are rich sources of information that need to be less about isolated tidepool territories and more about interconnected, coordinated, and interoperable networks that aim to serve our citizens, better. Expediting the way we build these networks can start with improving relationships between people—this is where the investment in innovation teams and change management is required. As noted in an Economist article about AI, there is a lot of frustrating attention being spent in unpacking the “black boxes” of our current systems. I especially appreciate their perspective when they write,
“Society already has plenty of experience dealing with problematic black boxes; the most common are called human beings.”
We must establish a more effective, efficient organizational structure. What will leaders need to support more direct interactions between disjointed data and help facilitate exchange between diverse teams (i.e. innovation) that at the core of smarter systems? This necessity, critical to AI, manifests across most of the federal spaces I interact with, including my work in supporting the White House Machine Learning and Artificial Intelligence R&D Subcommittee. When we improve the way our real-life leaders work together, we enable a future of easier and automated searching across our diverse and enriched data networks. Only when we begin to resolve some of our people problems, we can then invite everyday browsers to crawl a network of our research data (think of the Internet), or support industry leaders who have already been building tools to improve search between a variety of sources, like Google’s Knowledge Graph of real-world connections. AI can do the work of improving our networks once we have modeled good network behavior between ourselves as people, first.
2. Workplace: we accelerate the impact and outcomes of public health solutions by making better places for us to work together, better.
Placemaking is critical to shaping the future of work. If a modern, effective workforce relies on sharing fresh information, quickly iterating on systems, and consistently investing in the power of a culture of continuous improvement — what does this actually mean for people who are currently stifled in offices that do not support ongoing collaboration? Cubicles or physical elements keep people disconnected on a daily basis. A suboptimally designed office layout or offices that put teams 15 miles apart without good communication infrastructure, for example, leads to reduced interaction and collaboration. Our physical barriers continue to trap us, whether it is poorly design workspaces or disconnected databases.
US Food and Drug Administration (FDA) Commissioner Scott Gottlieb, M.D. makes an astute point with regards to data and shared ways of working to produce data in one of his recent blog posts, A Cross-Cutting Data Enterprise for Real World Evidence. In response to reimagining the business practices that keep data (and the people who create the data) segregated, he acknowledges that “different groups may collect the same information in different ways” and yet one key in the effort to harmonize and optimize for better interoperability is bringing stakeholders together in shared spaces to work through a collaborative process. He notes how it is critical for teams of diverse people to agree “upon definitions that allow different groups to meaningfully share their data.”
3. Workflow: we don’t harmonize efforts as well and as frequently as we could.
Many federal agencies are still operating from a patchwork of balkanized data tide pools. As far as the AI data supply pipeline goes, too many leaders are stuck thinking in a bygone era where linear supply chains rule, instead of networks of multiple empowered data ecosystems that extend simultaneous connections (as opposed to erecting walls). Failure to optimize our respective data on a continuous improvement schedule (or what I’ve been referring to as an “innovation loop” at NCI) is a failure to serve the needs of our users — most importantly our citizen patient’s own health data. I can’t see how the holy grail of interoperability will be achieved when limited to archaic, linear assemblies of
As noted in a recent article in the Wall Street Journal on the work it takes to train AI like IBM Watson, “recommending personal medical treatment is a taller order. The software needs to be trained with data on what has worked in the past…that information is often recorded in different formats and owned by different (entities) and isn’t always complete or consistent.” It may be too late to surface every last byte from our legacy data treasure troves, however we can take more cues from other industries, like forms of “just in time” networks that prevail over the vestiges of America’s linear assembly models, postindustrial wreckage I know too well from my hometown Detroit.
The power of taking small steps start to generate big change begins with the freedom to make more decisions quickly.
It is we, the people, who can lead the way by showing others how to let go of our old barriers. Decisions can be made quicker by sharing across networks of empowered peers as opposed to pushing decisions up a limited number of hierarchical silos. The more we remain in our silos, the greater risk we take of good ideas falling backwards into a lifeless jumble of unanswerable, unfulfilled action potentials. An empowered workforce is important not only for my ability to do my work (and do it well) but for attracting next generation leadership who will continue to shape the future of America’s public health systems.
We must go deeper to rewire our organizational learning and memory systems to dissolve the barriers — the physical and intangible silos — that keep us from using smarter systems to tackle our nation’s most complex 21st-century problems. Reconstructing organizational architecture to be more compatible with deep learning architecture challenges us to reconsider what we imagined our individual identities to be. Redefining our culture — less collective focus on the technical “what” and more energy about redefining “how” will allow us to work together, better. It all starts with people and the culture of how we work. As the White House’s Matt Lira stated, when unveiling the modernization priorities of the Office of American Innovation,
“The challenge is not to deliver a shiny object but to build an organizational structure, continuously updating.”
Garry Kasparov, author of Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, also notes that “education and retraining a workforce to adapt to change is far more effective than trying to preserve that workforce in some sort of…bubble. But that takes planning and sacrifice, words more associated with a game of chess than with today’s leaders.” It is we, the people, who can lead the way by showing others how to let go of our old barriers. We are responsible for the decisions about how we cultivate our future workforce, workplaces, and workflow to improve the delivery of our services to our citizen customers. We must dissolve the silos and continuously invest in a healthier leadership ecosystem to work together better as a smarter system of humans, first. AI can then amplify from the foundation we build as people.
Who else is going to step up and join those of us shaping the future of government?