Use cases
The mainstreaming of generative AI in the workplace
Unlike technologies with steep learning curves, the democratizing ease of generative AI usage has made it accessible across various domains.
It’s used by 74% of professionals for data analysis and visualization to extract actionable intelligence from data and to improve decision-making. A significant 66% harness generative AI tools to enhance collaboration and perform daily tasks. Of these, 63% use it to generate written content while 62% employ it for enhanced training/personalized learning.
Note that 66% of business leaders use generative AI at work. This reflects a growing confidence among C-suites in generative AI’s ability to enhance organizational operations. Juan Orlandini, CTO of Insight North America, believes that confidence explains the heightened interest in productization. “As leaders move into productionizing actual use cases,” Orlandini explains, “they will naturally consider the implications of enhancing or adapting public models to fit their purposes.”
Orlandini cautions, however, that such modifications require a delicate balance between customization and security to avoid exposing sensitive data to customers.
For his part, Amol Ajgaonkar, CTO of Product Innovation at Insight, suggests the practicality of generative AI applications obviates the need for extensive training. “Adapting use cases to what AI requires means focusing on intuitive interfaces over exhaustive instruction,” Ajgaonkar says. The emphasis on user-friendly AI solutions is echoed in business trends, where a focus on practical applications is becoming increasingly evident.
In addition to proper security measures, Carm Taglienti, Chief Data Officer at Insight, stresses the importance of knowing the intended outcomes when productizing use cases. “It’s about understanding what you’re trying to accomplish,” Taglienti advises, “and then demonstrating viability with the proprietary solutions like those we offer at Insight.”
FINDINGEnhanced natural language, semantic search to locate relevant information and documentation SUMMARIZING Extracting and condensing information from documents, meetings, case files, etc. GENERATING Creating new content based on existing document formats to integrate with new or existing business practices
That focus on practical implementation is also evident in our recent survey findings:
54% of businesses are using AI to drive productivity gains.
A significant 65% aim to use AI to enhance customer engagement.
The same proportion (65%) are involving employees in developing AI use cases.
AI’s role in critical business functions is expanding, with 59% in product design and 52% in software development indicating a rising trend in strategic AI integration.
As more business leaders get acquainted with generative AI’s potential, the focus on developing secure, intuitive applications that deliver strategic business impact can be expected to grow. Likewise, the increasing reliance on generative AI in the workplace will only strengthen the value of use case identification.
“When we develop AI solutions for a given industry, we sometimes forget the value of the lessons we’ve learned from applied AI in other industry verticals,” says Meagan Gentry, National AI Practice Manager at Insight. “Coming together as a team can revitalize those transferable use cases.”
Although use cases will vary across verticals, the mission of aligning in-house AI capabilities with short- and long-term business goals for sustainable success has quickly become a “tentpole issue” that unifies business leaders from every industry.
Content generation (written or visual)
Data analysis and visualization
Automation of manual tasks
Customer service enhancement
Product and design development
Other
See how a team of summer interns partnered with Insight sales teammates to accelerate client research from days to seconds.
Applying generative AI initiatives to on-the-ground organizational workflows requires a clear understanding of where the technology is effective and which challenges should be addressed. In this respect, there is a bright line distinction between what can be described as effective use cases on the one hand, and challenges that require special care and attention — if not expert consultation — on the other.
Content generationCreating written and/or visual content across a wide range of formats. Data generationSynthesizing new datasets for testing and development purposes.
Support for data-driven decision-making processes Enhancing strategic decisions based on company data gathered from AI analytics.
Understanding language Interpreting and processing natural language for diverse applications.
Code generationAutomating the creation of code snippets and programming constructs.
Human-like interactivity, contextuality and adaptability Engaging users with responses that mirror the intricacies of human dialogue.
Support for automationEliminating repetitive processes and manual tasks through intelligent AI systems.
Security and privacyProtecting sensitive data and user privacy in customer-facing AI applications.
ComplianceEnsuring AI systems align with industry regulations and legal standards.
Limited capabilities Recognizing and addressing the current functional limits of AI systems.
Dependency on quality dataRequiring high-quality datasets and modernized platforms for effective AI outcomes.
Trust Increasing user confidence by building reliability and credibility in AI systems.
BiasIdentifying and mitigating inherent prejudices in AI algorithms and training data.
Epistemological transparencyPossessing the knowledge required to clearly articulate (and justify) decision-making processes in AI.
Predictive diagnosticsThrough the analysis of patient data, potential health issues can be identified sooner.
Treatment optimizationWith vast amounts of data, AI can help healthcare providers customize a treatment plan for patients based on their medical history and risk factors.
Automated imaging analysisAI can be trained to analyze X-rays, MRIs and other screenings for signs of cancer or other ailments.
Regulatory monitoring and automationGeneration of legal and compliance documents is a burden that can be taken from healthcare professionals and streamlined with generative AI solutions.
Risk management and fraud detectionGenerative AI can perform predictive analytics and examine transactional patterns to identify risks and anomalies.Processing for lendingIn the underwriting process, AI can enhance efficiency and reduce human error, resulting in less risk for the client.Asset managementPortfolios can be optimized based on vast financial data and inform trading decisions.Personalized experiencesFrom chatbots that can answer specific questions to custom experiences that meet individual customer needs, AI can enhance the banking or investing experience.
Loyalty programsAI can develop personalized loyalty programs for customers based on previous activity and preferences.
Waste reductionWith the analysis of food waste data, AI can optimize stock and product placement to minimize loss.
ChatbotsA more intelligent chatbot can enhance the digital shopping experience without requiring large numbers of customer service staff to take inquiries.
Improved in-store experiencesFrom real-time stock monitoring to virtual fitting rooms, the brick-and-mortar shopping experience can be augmented to rival digital alternatives.
Simulation and modelingTest different manufacturing processes with AI to identify the most efficient and cost-effective options.
Supply chain optimizationAnalyze large datasets to forecast demand and optimize logistics.
Automated inspection and safety monitoringWith image recognition AI solutions, manufactured products can be assessed for defects, and potential risks to employees can be identified to prevent accidents.Dynamic pricingAI can assess a large array of information from material cost to production capacity to determine dynamic pricing as the market fluctuates to maximize ROI.
Valuable generative AI use cases can emerge from every corner of the organization. The integration of intelligent systems will continue to empower teams to devise creative — and sometimes unexpected — solutions with far-reaching implications for their workforce, if not the entire industry.
Uncover key strategies for using generative AI to drive enterprise-wide innovation with real-world use cases demonstrating its most powerful applications and benefits.
When we develop AI solutions for a given industry, we sometimes forget the value of the lessons we’ve learned from applied AI in other industry verticals. Coming together as a team can revitalize those transferable use cases.
