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ARE YOU AI READY?


PREPARING YOUR ORGANIZATION FOR DISRUPTIVE INNOVATION

Digital and Analytics

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May 28, 2024


AI IS A ONCE-IN-A-LIFETIME OPPORTUNITY FOR BOARDS AND CXOS TO FUNDAMENTALLY
REIMAGINE THEIR BUSINESS. TO USHER YOUR ORGANIZATION INTO THE AI-FIRST ERA,
YOU’LL NEED A MULTIPRONGED EFFORT. THE TIME TO ACT IS NOW.


The advent of AI is already shaping up to be a defining moment of this decade.
This once-in-a- generation technology is poised to become a disruptor across
industries and has been a harbinger of hope to all kinds of organizations—from
those looking for the next innovation S-curve to those struggling with shrinking
margins. However, there is also a growing concern about the ethical and security
implications of AI. Regardless, one thing is for certain: every organization
needs to understand AI, think about AI, and have a plan for AI.

The last part can be particularly challenging given the pace at which this space
is evolving. In our study from earlier this year, only 21 percent of senior
leaders agree or strongly agree that their organization has the capabilities to
deal with the expected disruption of AI in the next five years. So how do you
get your organization AI ready?

Embracing the AI opportunity through a multipronged approach

To begin the journey toward AI-readiness, organizations need to focus on four
dimensions (see figure 1).


Think: setting a bold AI vision and identifying high-impact use cases

Leading organizations set bold visions for AI and think about the disruptive
impact the technology could have on their business model. However, there needs
to be a strong focus on creating value since many use cases can become white
elephants that don’t offer much of a return on the investment. Also, much of the
innovation and narrative around the use cases has been inward-focused, such as
improving efficiencies and productivity. The leading disruptors are already
thinking about how AI will add value for their customers.

When crafting your AI vision and use case approach, three moves are essential:

Build AI platforms for creating internal efficiencies and customer value. The
value of AI accrues when it is used to fundamentally shift the business model.
Take a thematic approach with a platform-building mindset aimed at creating
internal and external value. Isolated builds of discrete use cases won’t unlock
the full potential of AI. Leading organizations focus on improving
profitability, enhancing employee productivity, and upgrading the overall
efficiency of day-to-day operations. However, the pioneers are also focusing on
use cases that create long-term value for customers. This can be observed in the
AI-driven simplification of service support, hyper-personalization of offerings,
and turbo-charging the marketing and sales engine to better serve customers.

Approach AI with a test-and-learn mindset to adapt and pivot as needed. Within
each identified theme, take a test-and-learn approach, swiftly validating the
value with proofs of concepts before progressing to minimum viable products and
investing in builds. As with any new technology, these initial implementations
will be rife with uncertainty. Don’t stall AI projects too early, and give
builds and implementations the room to fail. Leading organizations have sandbox
environments to develop, pilot, and mature the AI use cases for
organization-wide implementation—a proven approach that has yielded
fit-for-purpose use-case builds that maximize value.

Take a total cost of ownership view of AI investments, and align investment
cycles with AI innovation cycles. AI costs can vary significantly depending on
factors such as solution complexity, build-versus-buy decisions, and the data
preparation and maintenance efforts. A long-term, total cost of ownership view
can help accurately assess the investment’s viability and payback period. It’s
also important to establish investment cycles that enable and synchronize with
the rapid pace of AI innovation. Whether it’s ring-fencing specific budgets
early on in the annual operating plan or adapting funding approval processes to
release targeted tranches of resources, leading organizations have few
roadblocks when scaling AI initiatives to maturity.

Build: getting data and technology enablers right

Leaders continue the journey that started a decade ago in laying down the right
data and technology foundations to prepare for an AI-driven future. Without
these solid foundations, there can be no practical implementation of AI.

When pursuing this goal, two moves are vital:

Focus on data quality and using AI to enhance data. The ideal way to begin
improving the quality of your data is to adopt a use-case-by-use-case approach,
prioritizing specific business units or domains instead of the whole business,
which can be extremely slow and time-consuming. Further, AI, particularly GenAI,
can accelerate data cataloging efforts, process unstructured data, and automate
data access steps, which are often bottlenecks to deployment.

Think ahead to reducing complexity in your AI tech stack. Pursuing AI likely
means that your organization is collaborating with several partners—from
hyperscalers to system integrators. And each partner will have its own portfolio
of AI tools, large language models, and applications. This can add enormous
complexity to your tech stack and create the risk of getting tied down to a
partner’s innovation agenda rather than building toward your organization’s AI
priorities. For leading organizations, the north star is a tech stack that can
collaborate with multiple partners to rapidly fuel innovation, especially in the
initial stages of AI. For instance, building an interoperability layer in your
tech stack can enable you to collaborate with multiple technology partners,
accelerate innovation, and avoid dependency on a single player.

Scale: implementing operating model shifts to embed AI

Most organizations focus on identifying use cases and getting the foundational
enablers right (see figure 2). However, operating model shifts are perhaps the
hardest part of any AI strategy. Leading organizations recognize this and are
embedding AI at the heart of their organizations.



Implement structures that facilitate scaling AI across the organization. We see
three main archetypes emerging for scaling AI:

 * Federated model. Individual AI teams are spread throughout the organization,
   reporting to specific business or functional teams. A small central team has
   overall guidance of the organization’s AI priorities and tech stack or
   partnerships. Enablers such as data and governance are also led by business
   unit teams.
 * Centralized model. A core AI team, usually within the IT, digital, or data
   and analytics function, provides services to business or functional teams
   across the organization as a center of excellence. This team makes all
   decisions about AI.
 * Hybrid model. This is emerging as the most fit-for-purpose model because it
   offers a lot of flexibility. AI priorities and key enablers such as data,
   governance, the tech stack, and partnerships are centrally driven to create
   organization-wide synergies. However, there is also a recognition of the
   unique needs of specialized functions. For instance, operations-related AI
   priorities might differ slightly from the organization’s overall AI strategy,
   so the road map might be led by the operations and supply chain team. The
   intent is to capitalize on the flexibility to build deep organization-wide
   ownership of AI while creating synergies centrally.

Get your workforce ready to work with AI.

To facilitate organization-wide adoption of AI, invest in reskilling and
upskilling programs and make operating model shifts so that there are meaningful
incentives for employees to embrace AI.

We’ve seen the following effective strategies when designing AI literacy
programs:

 * Focus on experiential learning programs. Learning by doing helps AI and data
   capabilities stick. Organizing hackathons, inviting leading experts to build
   live proofs of concepts, and offering immersion workshops and learning tours
   are great ways to learn AI.
 * Create a sense of achievement with certification programs. Advanced
   certifications, especially external-facing ones, can be empowering for
   employees and offer a motivation to upskill.
 * Partner effectively to build a cutting-edge AI academy. Online training
   programs, often facilitated by a partner, offer up-to-date, relevant courses
   and are a good way to build your AI learning academy.
 * Incentivize time spent on reskilling and upskilling. As with any reskilling
   or upskilling program, give credit to employees who actively invest in
   bettering themselves.

Govern: pioneering the ethical use of AI

Underpin all AI efforts with principals, guardrails, and processes that ensure
AI is used in an ethical and responsible way.

Translate responsible AI principles into practical steps. Many leading players
have overarching principles for the responsible use of AI, but it’s important to
put them into action. The first step is ensuring that the principles are
cascading throughout the organization and are translated into practical tips for
teams and individuals. For instance, data governance principles that clearly
define which data can be used for which use case help put ethical AI into
practice and remove ambiguity. There also needs to be a concerted effort to
ensure that all local laws and regulations are always followed; this can be
achieved by elevating AI risk to the top of the organization’s overall risk
matrix.

Mitigate AI risk by having strong governance and embedding ethics at the heart
of AI builds. Establishing purposeful responsible AI principles and putting them
in action through trust and risk forums are essential to mitigate AI risks and
comply with all regulations. Poorly managed AI applications can create major
risks to a company’s financial stability and reputation. Data and security
breaches that compromise sensitive information have resulted in financial
losses, legal repercussions, and a damaging loss of customers. AI models also
carry the risk of “hallucination” when outdated, inaccurate data has generated
flawed or biased outcomes. This risk is compounded by a lack of model
“explainability” and the difficulty in tracing the decision-making process of
AI, leading to mistrust and compliance issues. Proactively address these risks
by using strong AI governance processes and laying down clear ethical guardrails
from the get-go of AI use-case builds.

Act now to get your organization AI ready

Many technologies have made a splash over the past two decades. However, AI is
fundamentally different. Several industry experts are calling it the most
disruptive change since the advent of the Internet. An early assessment of your
AI readiness and a well-defined execution road map will help unlock the full
potential of this disruptive technology. Kearney’s AI Readiness and Roadmap
approach can serve as the blueprint for building an effective AI strategy.

Without a well-thought-out AI plan, the return on AI investment will seldom
yield the desired results. Organizations will have to navigate a variety of
pitfalls, such as impractical use cases, limited improvements in data and tech
maturity, an underwhelming adoption of AI-altered processes, and non-compliance
with the AI risk guardrails, including regulations.

Charting a purposeful AI strategy and road map will be a seminal and long-term
business decision. The first step begins by asking yourself: are you AI ready?


Interested in learning more about our digital and analytics expertise?


Learn more





--------------------------------------------------------------------------------

Authors

Anshuman Sengar

Partner

Poush Bharadwaj

Partner

Nathan Bell

Partner

Rishabh Gupta

Consultant

Anshuman Sengar

Partner

Poush Bharadwaj

Partner

Nathan Bell

Partner

Rishabh Gupta

Consultant

--------------------------------------------------------------------------------

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