The current moment is bursting with opportunity to the bold and fearless business leader of tomorrow. We are collectively at a point of great change, where the nature of work has become increasingly more fluid. Today, organizations require dynamic talent, possessing a diversity of skills and a hunger to solve problems alongside colleagues that previously may have been separated into another department.
The lawyer of tomorrow is not just a lawyer. They must understand the various applications of artificially intelligent solutions and be at least moderately fluent in a common language for determining how these technologies can solve their greatest problems. While companies emerge from the COVID-19 pandemic, they are realizing that talent management has become even more urgent. Given the disruptions to so many business models, organizations are grappling with how to make hybrid teams function better, which new skills are needed, and what the workforce of the future will look like more broadly. Then there is the perennial challenge of how to deploy talent to the highest-value opportunities within an organization.
To adapt business models into a world where talent can be accessed virtually and cross both time zones and borders, legal talent management must adapt. New sources of data, fed into systems powered by machine learning and AI, are at the heart of this transformation. The information flowing through the physical world and the global economy is staggering in scope. It comes from thousands of sources: sensors, satellite imagery, web traffic, digital apps, videos, and credit card transactions, just to name a few. These types of data can transform decision-making.
The potential is being borne out every day—not only in the business world but also in the realm of public health and safety, where government agencies and epidemiologists have relied on data to determine what drives the spread of COVID-19 and how to reopen economies safely. But the sheer abundance of information and a lack of familiarity with next-generation analytics tools can be overwhelming for most organizations. No where is this more evident within the legal realm than the challenges surrounding processing and reviewing disparate forms of chat data. To prevent knee-jerk reactions to future obstacles, legal teams must evolve into “Flow to Work” operating models.
How organizations can adapt by harnessing data at the frontier:
- New forms of data are giving organizations unprecedented speed and transparency
When a CEO wants an answer to a complex question, a team might be able to get it in a couple of months—but that may not be good enough in a world where competition is accelerating. One of the biggest advantages of an automated, data-driven AI system is the ability to answer strategic questions quickly. To deliver, however, the team must align its skills to appropriately maximize resources and deliver.
- Specialist firms are refining and connecting data
Since the universe of data is so broad, legal service providers are carving out specialized niches in which they refine a variety of complex and even messy raw sources, feeding the data into machine learning– or AI-powered tools for analysis.
- Most non-tech companies are lagging, but new tools can get them in the race
Adapting to an era of more data-driven or even automated decision making is not always a simple proposition for people or organizations. The companies that have been fastest out of the gate already have data science chops. Now a growing range of available tools and platforms can help them catch up. The number of companies working with data today is sharply higher than it was even five years ago. Back then, it took a world-class engineer to extract value from that information, and non-tech companies had difficulty attracting the few at the cutting edge of data science. But new platforms and analytics tools are leveling the playing field—as is the vast array of data that is free, open, or available at relatively low cost
- It takes domain experts to extract the real value from data
Data science teams can build models with miraculous capabilities, but it’s unlikely that they can solve highly specific business problems on their own. Data engineers and scientists may not understand the subtleties of what to look for—and that’s why it’s critical to pair them with domain experts who do.
Adapting talent for the moment – flow to work
The pandemic has increased pressure on organizations to respond to three long-term talent trends that have been building for at least a decade: on-demand skills are scarce, made worse by digitization and automation; responding rapidly to changing and uncertain conditions is essential; and flexibility reigns supreme. Reskilling is an important part of the response to all three challenges, especially as the scarcity of skills continues. But organizations also need to get better at utilizing employees’ existing skills and capabilities.
To make this pivot, forward-looking organizations are choosing flow-to-work operating models, which create pools of resources that can be deployed flexibly and on demand. These pools are formed based on similarity of skills, rather than similarity of business functions, making it easier for organizations to access the right skills when they need to. The leader of these resource pools matches and deploys workers to tasks or projects based on the highest-priority work areas for the organization and the combination of skills required to complete them.
A flow-to-work model is an effective organizational response to all three talent challenges highlighted above. By deploying scarce talent to the highest-priority work, companies can avoid the inefficiency of hoarding valuable skills in just a few parts of the organization. By creating mechanisms to reallocate and redeploy talent based on evolving priorities, organizations are well placed to respond rapidly to external changes, including shifts in customer or business demand. By forming flexible teams, roles can be tailored to best match the skills to the work to be undertaken, with individuals playing different roles on different teams as required.
Changing the operating model in this way isn’t an overnight task, and it requires significant process and mindset shifts. However, it can be a critical way of improving organizational speed and responsiveness, people leadership, and talent development.