How Genai is Reshaping Industry Standards

What do a bank, a hospital, and a logistics firm have in common? They’re all quietly experimenting with GenAI in ways that actually matter. Not to win headlines. Not to build shiny copilots. But to drive results. That’s what stuck with me while exploring Deloitte’s GenAI Use Case Navigator. https://lnkd.in/eskkGqH4 It’s not just a catalog of AI ideas, it’s a reality check. Because here’s what it reveals: ➤ GenAI’s biggest impact isn’t in customer experience fluff. It’s in fixing the unseen bottlenecks that drag businesses down. ➤ The most transformative use cases? Not the ones that sound fancy—but the ones that reduce manual effort, save time, cut cost. ➤ Think: claims intake, RFP responses, contract summarization, fraud detection, supply chain prediction. Real examples? ➡️ A global insurer used GenAI to automate underwriting analysis, reducing quote generation time from 5 days to 30 minutes. ➡️ A healthcare system used it to summarize complex patient histories before physician review, cutting admin time by over 40%. ➡️ A logistics company deployed GenAI to optimize route planning and fuel usage, saving millions in operational costs. ➡️ A government agency implemented GenAI to automate the review of grant applications, ensuring consistency and reducing cycle times. ➡️ A legal team used it to draft NDAs and review contract clauses—freeing up attorneys for higher-value work. ➡️ A finance team built a GenAI-powered dashboard that answers natural language queries about spend, variances, and forecast anomalies—no analyst needed. They’re not talking about “prompt engineering.” This is so 2023. They’re engineering out inefficiencies. They’re not building AI for the sake of it. They’re using AI to solve what’s broken, fragmented, or too slow to scale. Because. ChatGPT is NOT your strategy. AI is NOT your strategy. Your strategy IS to run your business better. Smarter. Leaner. Faster. AI's power depends on where and how you use it. Because in the end, it’s not about being an “AI-first company.” It’s about being a results-first company. So here's the question: What’s the real ROI of GenAI? The pilot… or the process it quietly replaces forever?

📢 Organizations with over $500M in revenue are rewiring faster for GenAI...redesigning workflows, elevating governance, and mitigating risk. But most companies still lag behind. According to McKinsey’s latest State of AI report, only 1% of leaders say their organizations are truly mature in AI. So what separates the few that are capturing real bottom-line value? Here’s what stood out from the data: 🧠 CEO involvement is a game changer. ➡️ Only 28% of AI-using organizations say their CEO directly oversees AI governance but this group sees the highest impact on EBIT. ➡️ Leadership buy-in isn’t just symbolic...it shapes adoption, funding, and accountability across teams. ⚙️ Workflow redesign is the #1 driver of GenAI ROI. ➡️ McKinsey tested 25 organizational attributes—redesigning workflows had the biggest effect on EBIT gains. ➡️ The real value of GenAI comes from changing how work gets done, not just adding tools. 📊 AI is gaining ground fast but GenAI is the real accelerant. ➡️ 77% of companies now use some form of AI in at least one business function. ➡️ GenAI use has doubled year-over-year, with rapid adoption in marketing, sales, and HR. 📉 Risk awareness is rising but so are the blind spots. ➡️ 45% of respondents say their organizations are working to mitigate GenAI risks...yet only a fraction have formal processes in place. ➡️ Data privacy, accuracy, and ethical use still require much stronger guardrails. 📈 Hiring is only part of the answer. ➡️ Companies aren’t just adding AI specialists...they’re retraining internal teams to participate in AI deployment. ➡️ That shift reflects growing recognition that AI fluency is a cross-functional capability, not just a tech skill. 🧩 Joint ownership of governance is the norm. ➡️ Respondents report an average of two leaders overseeing AI governance often combining IT, legal, and ops. ➡️ This signals the cross-functional nature of responsible deployment and the need for shared accountability. 💡The takeaway? Organizations capturing real value from GenAI aren’t just adopting tools...they’re rewiring how decisions are made, work gets done, and who’s accountable. And People Analytics leaders have a critical role in shaping that foundation. Check out the comments below to see the full piece from McKinsey. Which of these shifts are you seeing in your organization? #PeopleAnalytics #GenAI #FutureOfWork #AIReadiness #TalentStrategy

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐢𝐧 𝐋𝐢𝐟𝐞 𝐒𝐜𝐢𝐞𝐧𝐜𝐞𝐬 & 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞: 𝐃𝐫𝐢𝐯𝐢𝐧𝐠 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 Generative AI is transforming industries, and nowhere is this impact more profound than in Life Sciences & Healthcare (LSHC). With its ability to analyze vast datasets, generate novel insights, and automate complex tasks, GenAI is redefining how we approach research, patient care, and operational efficiencies. 🔍 𝐊𝐞𝐲 𝐈𝐦𝐩𝐚𝐜𝐭 𝐀𝐫𝐞𝐚𝐬: 📌 Operational Efficiency – Automating medical coding, claims processing, and administrative workflows, reducing costs and enhancing speed. 📌 Hyper-Personalization – Enabling AI-driven virtual assistants, tailored patient engagement, and real-time personalized care recommendations. 📌 Accelerating Drug Discovery – Modeling proteins and biomolecules to accelerate the identification of new drug candidates. 📌 Regulatory Compliance & Risk Management – AI-powered compliance tools streamline regulatory adherence and mitigate compliance risks. 💡 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: ✅ Automating Denial Appeal Letters – AI extracts patient data, consults policies, and drafts structured appeals, reducing revenue loss. ✅ AI-Assisted Prior Authorization – AI automates payer-provider approvals, expediting patient access to necessary treatments. ✅ Smart Claims Processing – Generative AI categorizes claims, improving accuracy, efficiency, and reducing fraud risks. ⚠️ 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 & 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 🔹 Bias & Trustworthiness – Ensuring AI models are trained on diverse, unbiased datasets to prevent disparities in healthcare outcomes. 🔹 Data Privacy & Security – Protecting sensitive health data with strict compliance to HIPAA and GDPR regulations. 🔹 Regulatory Oversight – Aligning AI-driven decisions with evolving legal and ethical standards in the industry. Generative AI isn’t just an automation tool - it’s a strategic enabler that enhances decision-making, reduces inefficiencies, and fosters innovation across LSHC. As the technology matures, responsible AI governance and ethical deployment will be key to realizing its full potential. #GenerativeAI #LifeSciences #HealthcareAI #AIInnovation #DigitalTransformation #DataDrivenHealthcare

𝐓𝐡𝐞 𝐑𝐨𝐚𝐝 𝐭𝐨 𝐆𝐞𝐧𝐀𝐈 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 — everything you need to know 👇 — 𝐓𝐡𝐞 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧: ► 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 (#GenAI) is a groundbreaking technology that embeds intelligence at every layer of financial services, transforming core banking functions, payments, fraud detection, and customer experiences. ► Unlike traditional AI models, GenAI works with both structured and unstructured data, making banking systems more predictive, interactive, and automated. ► 97% of banks have already adopted a GenAI strategy, but scaling it enterprise-wide remains a challenge due to regulatory hurdles and legacy infrastructure. — 𝐀 𝐍𝐞𝐰 𝐄𝐫𝐚 𝐢𝐧 𝐁𝐚𝐧𝐤𝐢𝐧𝐠: The GenAI Impact on Payments ► Payments are no longer just a back-office function; they are now a strategic advantage for businesses. ► GenAI is reshaping the payments landscape by enabling: ✔ Conversational checkout experiences (AI-driven assistants for seamless transactions) ✔ Automated transaction processing (reducing manual intervention & errors) ✔ Enhanced fraud detection (real-time anomaly detection) ✔ More personalized payment journeys (AI-powered recommendations & insights) ► With real-time payments becoming faster and more complex, banks need AI-driven automation to process transactions 30-40% faster and reduce errors by 70%. — 𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐆𝐞𝐧𝐀𝐈 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 🔹 𝐅𝐫𝐨𝐧𝐭-𝐄𝐧𝐝 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: ✔ More streamlined checkout processes (i.e., AI-powered conversational checkout) – 44% ✔ Improved customer support & engagement – 44% ✔ More personalized transaction experiences – 41% 🔹 𝐁𝐚𝐜𝐤-𝐎𝐟𝐟𝐢𝐜𝐞 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: ✔ Optimization of working capital decisions through better insights – 49% ✔ More accurate cash flow forecasting – 41% ✔ More efficient fraud detection & prevention – 41% ✔ Enhanced real-time analytics & reporting – 36% ✔ Stronger security measures – 21% — 𝐑𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬 𝐭𝐨 𝐆𝐞𝐧𝐀𝐈 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬, while GenAI adoption is global, priorities differ by region: 🌎 𝐔𝐒 & 𝐀𝐏𝐀𝐂: ► Focused on using GenAI for competitive advantage, with 53% of US and 54% of APAC banks prioritizing AI to differentiate in the market. 🇪🇺 𝐄𝐮𝐫𝐨𝐩𝐞: ► Primarily focused on operational efficiency, with 48% of banks leveraging AI to streamline workflows and optimize internal processes. 🇮🇳 𝐈𝐧𝐝𝐢𝐚: ► GenAI is being deployed for straight-through processing (STP) of payments, reducing manual intervention in high-volume transactions (83% adoption). 🌎 𝐋𝐀𝐓𝐀𝐌: ► Focused on streamlining payment operations, addressing inefficiencies and improving financial inclusion. — Source: NTT DATA — ► Sign up to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬: https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... & Marcel van Oost #AI #Payments #FinTech #Technology

#GenerativeAI is enabling Intelligent Enterprises to learn, adapt, and self-optimize continuously by transforming data into strategic foresight, automating complex decision-making, and orchestrating seamless human-machine collaboration to drive exponential growth and sustained organizational advantage. 💎 My PwC Canada partner, Adam Crutchfield, and I had the opportunity to discuss value propositions of #GenAI for transforming industries and reshaping markets. 💡 Here is our actionable advice on how organizations can accelerate business model reinvention with GenAI to drive sustained outcomes and build trust: • Multimodal and Ensemble Architectures – Organizations no longer need to rely on a single AI model to drive innovation. By leveraging ensembles of specialized models, organizations can synthesize insights from structured and unstructured data, unlocking new frontiers in hyper-personalization, dynamic pricing, and predictive strategy. This orchestration of intelligence fuels decision-making agility, redefining organizational advantage. • Multimodal Models – The future of customer engagement is frictionless and immersive. Multimodal AI integrates text, voice, images, and video into a singular intelligence layer, enabling businesses to create richer, context-aware experiences. From retail to healthcare, this convergence is powering adaptive interfaces and next-generation digital ecosystems that eliminate barriers between human intent and machine execution. • Multiagent Systems and Collaborative Bots – AI is no longer a solitary engine of automation—it is a network of intelligent agents collaborating in real time. Multiagent AI ecosystems redefine operational efficiency, driving autonomous supply chains, self-optimizing financial services, and seamless human-machine workflows. In this paradigm, organizations transition from linear processes to dynamic, self-evolving systems that continuously optimize for resilience and growth. This is a series of 8 videos on the applications and implications of GenAI. Follow Adam and PwC to access previous videos in this series. 🌐

The biggest AI impacts won’t be borne out in a calculus of jobs but rather in seismic shifts in the level of expertise required to do them. In our article in Harvard Business Review, Joseph Fuller, Michael Fenlon, and I explore how AI will bend learning curves and change job requirements as a result. It’s a simple concept with profound implications. In some jobs, it doesn’t take long to get up to speed. But in a wide array of jobs, from sales to software engineering, significant gaps exist between what a newbie and an experienced incumbent know. In many jobs with steep learning curves, our analysis indicates that entry-level skills are more exposed to GenAI automation than those of higher-level roles. In these roles, representing 1 in 8 jobs, entry-level opportunity could evaporate. Conversely, about 19% of workers are in fields where GenAI is likely to take on tasks that demand technical knowledge today, thereby opening up more opportunities to those without hard skills.   Our analysis suggests that, in the next few years, the better part of 50 million jobs will be affected one way or the other. The extent of those changes will compel companies to reshape their organizational structures and rethink their talent-management strategies in profound ways. The implications will be far reaching, not only for industries but also for individuals and society. Firms that respond adroitly will be best positioned to harness GenAI’s productivity-boosting potential while mitigating the risk posed by talent shortages.   I hope you will take the time to explore this latest collaboration between the The Burning Glass Institute and the Harvard Business School Project on Managing the Future of Work. I am grateful to BGI colleagues Benjamin Francis, Erik Leiden, Nik Dawson, Harin Contractor, Gad Levanon, and Gwynn Guilford for their work on this project. https://lnkd.in/ekattaQA #ai #artificialintelligence #humanresources #careers #management #futureofwork

Conversational AI platforms provide today's benchmark for self-service and AI-driven customer engagement. The core capabilities of these platforms span 4 areas: • Integrations with back-end systems, communication channels, and knowledge sources. • AI technologies for speech and natural language processing, understanding, and generation (NLP/NLU/NLG). • No-code conversation design environment. • Toolsets for defining, testing, and refining intents and entities. In just 18 months, GenAI has reshaped the conversational AI market. Platforms have undergone two rounds of evolution—sometimes requiring a complete rebuild of functions—and must keep pace with relentless innovation. A new generation of platforms is emerging, driven by key trends and evolving needs: 1) Proprietary NLP/U is no longer the differentiator—platforms must orchestrate best-of-breed AI models and enable the combination of multiple specialized models. 2) GenAI simplifies intent management, but a new toolset is needed to customize and optimize models beyond basic prompting and RAG. 3) Voice AI requires best-in-class speech-to-text, text-to-speech, and speech-to-speech to meet performance and experience demands. 4) Platforms need to support both transactional and informational interactions. 5) Deterministic workflows will dominate CX and sales in the short term, but autonomous agents will redefine application development. 6) Integration capabilities will evolve into orchestrated, agent-driven ecosystems with robust governance. 7) Platforms must manage context over longer conversations. 8) Orchestration must extend beyond interactions and AI to enable sophisticated AI-human collaboration. 9) Platforms need to enable faster iterations and continuous expansion of use cases The tension between disaggregating functions for independent evolution and assembling an expanding set of technologies makes it difficult to predict what platforms will look like in a few years. Not all providers will successfully transition—some, burdened by technical debt, will be forced to pivot toward specialized solutions. When evaluating platforms, the key is to define the flexibility you truly need and make tradeoffs accordingly. A purpose-built solution may be a better fit than a broad platform, allowing you to leverage the vendor’s deep domain expertise. But that doesn’t eliminate the need for rigorous validation of their technology stack and architecture. Given that 'platform' is a catch-all term in vendor messaging, it’s essential to cut through the noise and classify offerings accurately. As conversational AI evolves toward the orchestration of conversations, technologies, and human-AI collaboration, use these trends as strategic lenses to guide your decisions. Above all, prioritize openness to navigate this evolving landscape. I trimmed the article to fit this post; the full version is linked in the first comment. #conversationalai #ai #cx #salestech

It’s fascinating how the introduction of GenAI has reshaped development cycles for business teams. Traditionally, development plans for AI systems focused on clear metrics, rigorous validation, and structured feedback loops to ensure confidence before deployment. However, with GenAI, I’ve noticed a shift: teams often rely on a subjective “developer feel” metric rather than crafting a robust, well-defined initial version during system testing. This approach leaves a critical gap between technical development and business readiness, creating challenges when these systems are handed over to business teams for real-world use. This stands in stark contrast to time-tested practices in data science, where creating and rigorously maintaining training, test, and validation datasets was once non-negotiable. In many cases, there’s little effort put into creating datasets to evaluate how closely responses align with business requirements. Instead, teams often resort to brute-force testing with developer-generated test cases, overlooking the importance of structured validation. Why is this happening? Is it due to poor problem formulation, the pressure of business needs, overconfidence, or simply a lack of awareness about these fundamentals in today’s democratized AI landscape? Whatever the reason, the impact is undeniable, and it’s the business that bears the brunt. Trust in the technology diminishes, and doubts arise about the robustness of the system. It creates the impression that every new prompt requires manual intervention to fix issues, leading to a patchwork approach rather than a dependable solution. Sometimes, going back to the basics, especially structured problem-solving, rigorous validation, and thoughtful testing, delivers far more value than chasing the best models and hoping they’ll work one day. Fundamental flaws in problem solving cannot be fixed by any model. Happy New Year, everyone! #ExperienceFromTheField #WrittenByHuman #EditedByAI

With GenAI transforming the business world as we know it, more business leaders are increasing their investments in this next-gen tech to revolutionize how they tackle data management, cloud consumption, and cybersecurity.    Alongside my co-authors Brenna Sniderman and Diana Kearns-Manolatos (she/her), we explored this trend and uncovered how industry leaders are leveraging this GenAI to drive significant business outcomes (https://deloi.tt/3Rm9ENW). Those areas include: ◼  Strategic Data Management: Among commercial industries, 79% of consumer organizations and 74% of tech media and telecommunications companies plan to boost their investments in data management. Effective data management is seen as crucial for enabling successful gen AI implementations. ◼ Enhanced Cybersecurity: 74% of financial services institutions are set to increase their cybersecurity investments, ensuring the protection of sensitive data and supporting gen AI initiatives. This focus on cybersecurity is echoed across industries, reflecting its critical role in today's digital landscape. ◼ Cloud Consumption: While 62% of leaders anticipate increased cloud investment, this is the least pronounced among the intertwined capabilities. Cloud infrastructure remains vital for scaling AI applications. To truly harness the power of GenAI, it’s essential to integrate investments strategically and ensure robust data management, cybersecurity, and scalability throughout an organization’s cloud applications. By doing so, organizations can unlock new levels of agility and innovation, driving superior market performance. 

How will Generative AI transform Life Sciences and Health Care in the year ahead?      As Neal Batra and I recently discussed at #CES2025 — GenAI is on its way to becoming as ubiquitous as electricity and as indispensable as the internet. Not only is GenAI aiding in diagnosis and treatment, but it is also redefining roles within health care. We’re just beginning to understand how it will shape the future.      In particular, we discussed two fundamental actions necessary for life sciences and health organizations to harness the power of GenAI:     1) Establishing an ethical framework. Embedding trust into GenAI applications through explainability, transparency, avoiding hallucinations, and recognizing the technology’s limitations is essential.     2) Scaling for outcomes. Scaling GenAI requires organizations to focus and invest in the pragmatic GenAI use cases that can help us achieve our shared goals for the industry––lowering costs, improving care quality, and driving greater accessibility.      Both of these are front and center in Deloitte’s just-released State of Generative AI In The Enterprise Survey: Explore it here ➡️ https://deloi.tt/42gWJTQ. Recommend a read and thank you to Jim Rowan, Beena Ammanath, Costi Perricos, Brenna Sniderman, David Jarvis and more for continuing to document this exciting GenAI progress to help organizations optimize outcomes.