AI Quick Wins Matter, But Only If You Play the Long Game

When businesses spark their first quick AI win, it’s exhilarating—like striking a match in a dark room. The immediate insight promises new directions. But without a resilient AI value chain, those flashes quickly fade. For AI to keep delivering, each spark needs to fit into a larger strategy, building a fire that drives ongoing growth.

Today’s quick wins offer businesses proof of AI’s potential to, among other things, unlock insights from unstructured data. Yet they risk becoming only fleeting flashes if treated as isolated successes. The key lies in integrating these AI insights into a data foundation connected to a long-term strategy. Without that, quick wins fall short. But linked together, these insights transform into a powerful, continuous force that fuels sustained returns on AI investments, boosting growth, resilience and long-term competitive advantage.

From isolated insights to integrated strategy

Business data has evolved from simple rows and columns to encompass emails, chat threads and PDFs. Today, much of an enterprise’s knowledge is stored in unstructured formats that are harder to access and analyze for decision-making. This is where AI can rapidly transform unstructured data into actionable insights—a quick win that’s especially appealing.

“Unstructured data is where a lot of intellectual property ends up, created by a full ecosystem that includes employees, customers, partners and suppliers,” says Mike Capone, CEO of Qlik, a data integration, analytics and AI platform. “The challenge in creating value from it is, it’s fragmented, it’s hard to get to and it’s in wildly different formats.”

Organizations need tools to interrogate, collate and detect patterns in this data. These tools are integrated into Qlik Answers, a “gen AI-powered knowledge assistant” for business that analyzes unstructured data to deliver credible insights on a governed, independent platform. Qlik’s solution leverages retrieval augmented generation (RAG) to increase the accuracy of results.

These fast, useful outcomes—such as credit-card issuers instantly sifting through PDFs of legal and user agreements—are what Capone characterizes as the quick wins of generative AI. These proof points can help justify more ambitious spending as AI becomes integral to business strategy.

Beyond the quick win – a lasting AI strategy

Quick-win thinking may still dominate the current AI landscape. A 2023 Bloomberg Intelligence report found that while generative AI could attract 10% of all IT investment by 2032, current AI spending remains less than 1% of the share of total IT spending—inclusive of expenditures on hardware, software services, the gaming market and advertising. The findings indicate a lingering experimental attitude towards AI, rather than full-confidence adoption. 

While a quick insight from previously inscrutable data is clearly a win, Capone sees these numbers as evidence of an AI retrenchment.

“A lot of the money that was being spent is because of the race everybody’s in because their board or their CEO asked, ‘What are we doing about AI?’” he says.

Intentional AI benefits for business will last when stakeholders figure out how to merge the quick-win nature of chatbot data trawls with an overarching strategy and integration into workflows.

Integration and reliability are non-negotiable

Qlik Answers integrates with Qlik’s full AI value chain, including Qlik Talend Cloud, which provides real-time data from any source directly into Qlik Answers. This makes the process of generating answers from unstructured data a continuous cycle of learning and improvement, creating what Capone describes as a durable, sustainable framework.

To ensure its AI tools meet real user challenges, Qlik has also assembled an AI advisory council. In assessing Qlik’s tools, council member Rumman Chowdhury applies her background in ethical AI, which integrates human perspectives into what she calls the “AI supply chain.”

Currently CEO of Humane Intelligence, a nonprofit focused on improving AI models, Chowdhury believes many companies are too cautious with AI, using LLMs only as search tools for closed document sets.

While these are understandable impulses around AI, the world is not changed by making an LLM search tool, says Chowdhury. The world will be changed, she says, when the motivations driving its use are bolder and more ambitious—but we’re not seeing that bravery because of the lack of reliability.

To that end, Qlik developed their Qlik Talend Trust Score™ for AI to help users assess a data set’s suitability for feeding into any AI solution. The score utilizes layers of vectors that scan for quantity, bias, consistency, recency, data gaps and other metrics.

“It really ups the ante of being able to use gen-AI tools in a governed, highly secure, accurate way,” says Capone.

Building a sustainable AI roadmap

Arriving at reliable data strategies doesn’t happen overnight—it’s a process requiring patience and investment. Organizations first need clean, reliable data foundations with consistency, governance and quality controls. After establishing this groundwork, they can integrate AI into workflows to improve operations.

Qlik’s future plans are rooted in helping customers derive sustained value from all of their structured and unstructured data. With a vision to become the best independent platform for enterprise AI investments, Qlik aims to empower industries from healthcare to manufacturing with solutions tailored to meet their specific needs at scale.

Capone’s larger vision is to see industries soar through smart AI use.

“You should really consider deploying with a company that’s serious about doing the heavy lifting to achieve an end-to-end game,” he says. “Then, you’re going to accelerate the value you get out of your quick AI wins in the long term.”