Expert advice: Accelerate innovation with genAI
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The IT and business challenges
of deploying a genAI solution
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Discover the opportunities and use cases that effectively and efficiently
deliver business value from generative AI implementations.
The CIO’s guide to
delivering value from genAI
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Generative AI’s moment of truth is rapidly approaching. It started with senior
executives who saw the promise of the technology and mandated IT
leaders to deploy it as quickly as possible to stay competitive.
Yet at least 30% of generative AI (genAI) projects will fall apart in
the coming year, according to Gartner.
“Executives are impatient to see returns on genAI investments,
yet organizations are struggling to prove and realize value,” says
Rita Sallam, distinguished VP analyst at Gartner. “As the scope of
initiatives widen[s], the financial burden of developing and
deploying genAI models is increasingly felt.”1
CIOs need to demonstrate not only that they’ve worked with their
business counterparts to identify the right use cases but also that
initial investments are contributing to critical business outcomes.
As organizations across all industries seek to enhance the customer and
employee experiences they’re delivering, it’s not an option to abandon
proofs of concept or sit on the sidelines.
The pressure to deliver on the promise of genAI may be leading many organizations to take a DIY
approach. In fact, Gartner research shows that 25% are customizing solutions.4 However, that
means IT leaders could face a host of potential obstacles and pitfalls, including in terms of
integration, costs, and user experience.
43%
40%
40%
55%
41%
strategic planning and decision-making
creative and innovative projects
learning and skills development
of organizations believing their tech infrastructure is not ready to work with genAI
citing data management and quality concerns
Considering the challenges and risks associated with genAI deployments, strategic planning is
critical, and the first step is to prioritize use cases. Evaluate potential business value — including
factors such as economics, sustainability, efficiency, and productivity — as well as the operational,
reputational, and competitive risks. For example, 44% of organizations are investing in genAI use cases to increase employee productivity.
Use cases that deliver rapid ROI
Bias, hallucinations, compliance, and the limitations of general search are all good reasons to
deploy a custom genAI solution. Yet building your own system can get expensive, especially if
your organization has limited AI or genAI expertise. The costs further escalate when that solution
needs to scale beyond its first use case. There are other risks to consider in the build-your-own
model, such as prepping data for accuracy and connecting data sources.
The value of plug-and-play genAI
User experience
Costs
GenAI solutions will need to effectively “speak” to disparate parts of an organization’s
tech stack, including their customer relationship management (CRM) system, marketing
tech tools, and customer service knowledge centers. The integration challenge is
daunting, according to a Deloitte report,5 with:
The good news is that genAI adoption across the workforce is rising: Data from EY shows that
uptake has gone from 22% of employees in 2023 to three-quarters of employees today.6
However, some of that could be “shadow AI,” where team members are using genAI tools not
sanctioned by their employer. Unauthorized use of these solutions can present serious risks to
the business, including inadvertent sharing of sensitive information, the potential for security
vulnerabilities, lack of data traceability and accuracy, as well as output biases. There is also still
a lot of work to do in tailoring genAI to specific job functions and reskilling or upskilling those
expected to use the technology.
Although genAI could lower expenses in some areas,
it will require many enterprises to keep pace with
government and industry efforts and place guardrails
around personal privacy and security. As a result,
KPMG’s report found, more than half of organizations
expect AI regulations to increase what they spend on
compliance activities. In turn, this also adds to the
costs of employing engineers and assigning other
team members to help maintain the technology.
Qlik Answers delivers rapid results through an out-of-the-box,
AI knowledge assistant tailored to your needs.
Jump-start your genAI use case
The business value of genAI
With the ability to produce content, write code, synthesize vast data sets, and ultimately turn
unstructured data into actionable answers, the return on investment (ROI) in genAI can be
calculated across multiple dimensions. Research from McKinsey, for example, found that genAI
adoption is already driving cost reductions2 in areas such as human resources (HR), service, IT,
and marketing while boosting revenue increases by improving supply chain management.
Revenue increases in supply chain and
inventory management through use of genAI
Source: McKinsey, “The state of AI in early 2024”
GenAI’s ultimate business value may lie in how it empowers employees to access the information
they need in order to collaborate and more quickly provide service. For example, KPMG3 found that
knowledge workers are reinvesting the time gained through use of genAI in the following areas:
The ability to quickly access underutilized data enables organizations to more effectively surface
insights and answer critical questions, which in turn enables employees think more strategically
and ideate new business opportunities.
5%
Customer support
Get started by aligning pilots and deployments in areas where employees need more context, clarity, and trustworthy information. Look for areas where team members are often mired in searching through knowledge centers, shared drives, and other repositories or where it’s up to them to make sense of unstructured information.
With AI that leverages retrieval-augmented generation (RAG), they can stop doing detective work
to get the answers they need. Here are some common examples of business functions where
this kind of genAI can deliver rapid ROI:
Customers may feel as though they’ve been left on hold
for interminable periods, waiting for answers to their
questions. Meanwhile agents must scour knowledge
centers and other resources to troubleshoot problems or
inquiries about product features and company policies.
It’s time-consuming, because they often need to search
multiple internal data silos and unstructured data sources,
such as PDFs, intranets, and emails. There's also the risk
that the information they find is inaccurate, out of date, or
simply not the best answer, and yet agents often feel
pressured to deliver a timely response, regardless of their
confidence in the information.
A holistic genAI solution can search all relevant internal
documents and transform that experience so that agents
as well as customers get what they need faster and more
efficiently. Download this white paper, “Empower customer
support with an AI knowledge assistant,” to learn more.
Expert advice: GenAI’s impact on productivity
Salespeople convert more prospects and attract more
business from their existing clients by proving they can
provide helpful, trustworthy advice. Developing those
relationships is possible only when they come armed
with a deep understanding of their firm’s product road
map, the latest pricing, and new offers.
GenAI can surface those details in real time, even from
unstructured content, to accelerate the selling process.
That means they can develop stronger sales proposals
and address every customer question or concern.
Download this white paper, “The secret of empowering
sales teams with genAI,” to learn more.
Sales enablement
Developing the creative assets required to tell a brand’s
story and compel customers to learn more has traditionally
been laborious and expensive, often necessitating the use
of external agencies. Public genAI models, such as
ChatGPT, are limited to whatever data inputs marketers can
upload, and they require monthly subscription payments.
This presents multiple risks, such as the potential for
inaccurate outputs or hallucinations based on limited inputs
and giving the model access to proprietary information.
GenAI is not only supercharging this area of marketing but
also doing so in a way that significantly reduces costs and
allows for more iterations, personalization, and testing.
Download this white paper, “How marketers can deliver on
the promise of genAI,” to learn more.
Marketing content
Expert advice: How to avoid genAI risks
The better approach is to look for self-service solutions that simplify deployments and ensure
ease of use and quality right out of the box. Further explore these considerations by watching this webcast, "Build or buy? Practical strategies for generative AI deployments," which features guidance from an IDC analyst.
As you explore plug-and-play solutions, ask potential genAI tech partners questions such as:
• How can genAI work with unstructured content without the need to prep or move it?
• What’s the best way to weave genAI capabilities into existing workflows?
• How can you assess whether genAI solutions provide trustworthy answers and full transparency into the ways answers are derived?
• What assurances are in place that genAI solutions will support governance as well as security requirements?
• What kind of genAI solutions not only reduce time-to-value but also provide a feedback loop that allows for continuous improvement without engineering support?
It’s an exciting time as organizational leaders explore how genAI can transform operations and
workflows. Yet CIOs know that ROI must remain at the heart of innovation. With the right strategic planning and partners, enterprises can successfully turn genAI from an area for experimentation into a source of value.
1 Gartner, “Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept by End of 2025,” July 29, 2024;
2 McKinsey, “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value,” May 30, 2024;
3 KPMG, “KPMG Survey: GenAI Dramatically Shifting How Leaders Are Charting the Course for Their Organizations,” August 15, 2024;
4 Gartner, “Gartner Survey Finds Generative AI Is Now the Most Frequently Deployed AI Solution in Organizations,” May 7, 2024;
5 Deloitte, “Now decides next: Moving from potential to performance,” August 2024;
6 EY, “EY 2024 Work Reimagined Survey,” October 15, 2024.
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