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 // . //  Oliver Wyman Health //  Avoiding The Gen AI Hype Machine


AVOIDING THE GEN AI HYPE MACHINE

By embedding innovation into the planning process, leaders can navigate the
challenges of introducing new technologies and drive meaningful business
outcomes.

Nikhil Sarathi , Eric Lu , Mofeed Sawan , and  Shreya Gandhi-Gupta

5 min read



Table of Contents

 * Adopting a business leader mindset
 * Setting an organization’s level of risk tolerance
 * Embedding innovation in process planning
 * Don’t get distracted

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Generative AI has caused seismic shifts in nearly every aspect of our daily
lives. The natural language processing tool makes it easier for people to
develop code, write research papers, or come up with new dinner recipes. How to
use — and control — generative AI is a hot topic of conversation from boardrooms
and classrooms to the halls of power in Washington, D.C., and everywhere in
between.

Healthcare is not immune from the hype. Generative AI is positioned as a
game-changer for every industry segment. Life sciences companies hope it will
accelerate drug discovery. Technology vendors and health systems see generative
AI as a tool to enhance electronic health record systems and create more
predictive care. For insurers, it may improve risk management and member
engagement. The adoption of generative AI in healthcare is expected to soar over
the coming years, with market value projected to hit $22 billion by 2032, up
from $1 billion in 2022.

While generative AI and other innovative solutions open the door to new
possibilities in healthcare, they risk being a siren song if technology leaders
aren’t careful. Leaders must operate at two speeds simultaneously — maintaining
existing technology all the while assessing, testing, and learning how and when
to adopt a groundbreaking innovation like generative AI.


ADOPTING A BUSINESS LEADER MINDSET

Technology leaders often face internal and external pressure to pursue an
attractive, disruptive new technology. That’s certainly the case with generative
AI where there’s been an arms race across industry sectors to experiment with
various use cases for the technology.

Instead of falling into this trap, technology leaders must adopt a business
leader mindset. Working with other parts of the organization, they need to
establish a set of outcomes and key performance indicators that they want to
hit. At that point, they can decide which — if any — new technology is right for
the job. This approach allows leaders to keep their eyes on business objectives,
effectively manage a portfolio of existing and new solutions, and drive
accountability for themselves and their teams. To gain buy-in from the rest of
the organization, technology leaders should be explicit about this process and
make it clear that everything links back to organizational goals.


SETTING AN ORGANIZATION’S LEVEL OF RISK TOLERANCE

Leaders also face a risk-reward tradeoff when deciding if a new and untested
technology solution is worth the sizeable investment. Having a firm
understanding of and process for evaluating risky initiatives is critical. But
let’s be clear, managing risk does not equate to avoiding risk. There will
always be opportunities and reasons to go into the relative unknown. The point
is that leaders must weigh the opportunity cost of dedicating resources towards
a new technology like generative AI against doing nothing or focusing more on
existing solutions.

This is where taking a portfolio view comes into play. Leaders should map out
all the opportunities that lie ahead and be ruthless in prioritizing which
should be acted on in the near- and long-term. A prioritization process also
allows leaders to choose what not to do, which is especially necessary during
times of budget constraints.


EMBEDDING INNOVATION IN PROCESS PLANNING

As leaders map out a planning process for innovative technologies, they should
embed these three foundational capabilities to drive success.

 * Embrace product-based development: A product-operating model allows
   organizations to set up business processes that address gaps in the usual
   project-centric execution. Under this approach, organizations can move away
   from funding individual ideas in favor of a holistic, enterprise-wide product
   vision that enables more solutions to cut across multiple use cases. This
   leads to more stable funding. Support for projects like generative AI cannot
   be flipped on and off; they require a steady stream of funding for a
   consistent period. That’s why starting with the use case is so important.
   
   Stable resourcing of design and execution teams is crucial here, particularly
   when customer needs are unclear, which is occurring with generative AI as
   overarching business goals are uncertain. A cohesive team that works on a
   unified problem can foster innovation and co-creation, which is not possible
   with standard project-centric execution. Integral to this team approach is
   setting clear expectations and holding everyone accountable for outcomes.
   
   Business and IT leaders need to be in lockstep on what the operating model
   looks like — who are the business stakeholders, who make up the product
   organization, and what day-to-day processes look like. This takes commitment,
   dedicated resources, and permission of joint teams to evolve and overcome
   obstacles that are bound to arise.
 * Evolve data governance processes: Healthcare organizations should already
   have a data governance process in place, although some are more evolved than
   others. Deploying and scaling a tool like generative AI necessitates
   additional considerations, especially as lines continue to blur on who owns
   the data.
   
   Generative AI’s ability to quickly structure unstructured data is one of its
   superpowers. But it can be unwieldy and organizations will see a muted impact
   if they start by looking at the data. Instead, begin with use cases and have
   a strong partnership between business and IT to ensure the data used is clear
   and well-governed. Beyond that, it is critical that the data being fed into
   the model is accurate and reliable to avoid potentially misleading or
   incorrect outputs. Security will also be paramount as organizations scale
   their use of generative AI and start pulling data from other sources. Going
   hand-and-glove with security is accountability and the ability to trace data
   back to its original creators. Technology teams must ensure that they can
   source data to ensure its accuracy and legitimacy.
 * Modernize the data environment: Organizations need to be far along in their
   broader data infrastructure and capabilities modernization efforts before
   attempting to deploy and scale generative AI applications. Benefits like
   computational scalability, cost-effective deployment of resources, and a data
   environment that’s accessible across the enterprise are all critical to
   unleashing the full benefits of generative AI.
   
   Beyond those data infrastructure musts, organizations need to establish
   strong data tracking processes, especially the large amount of unstructured
   data that is already in use. That means identifying where it comes from, how
   it has evolved, and where it is being used.
   
   However, one of the benefits of generative AI is its ability to create
   synthetic data outputs that complement existing data environments. Generative
   AI enables business users to quickly test ideas without waiting for proper
   data capture and cataloging typically done in a production run.


DON’T GET DISTRACTED

This is a watershed moment for healthcare. Generative AI has the potential to
push drug development, care delivery, and even back-office functions to new
levels. Technology leaders will play a pivotal role in ensuring that their
organizations do not get swept up in the hype machine. They must work on consort
with business and clinical leaders to strike a balance between innovation and
effective business operations. By embedding innovation into their planning
process, leaders can successfully navigate the challenges of introducing new
technologies and drive meaningful business outcomes.

In this video, we delve deeper into some of the unsexy parts of digital
transformation that are essential for successfully deploying generative AI.


 * Digital
 * Health and Life Sciences
 * Healthcare
 * Industry Transformation

 * Nikhil Sarathi Partner
 * Eric Lu Principal
 * Mofeed Sawan Engagement Manager
 * Shreya Gandhi-Gupta Associate

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