Agentic AI enables orchestrated, autonomous collaboration among AI agents to manage and optimize complex workflows – a natural fit for banks' multi-step cross-functional processes.

In client service environments like contact centres, where customers expect increasingly personalized and seamless service, agentic AI assigns different agents specific tasks that collectively drive faster and smarter outcomes. One agent might analyze customer interactions in real time. Another triages calls based on client type, while others surface relevant precedent cases or pull in CRM data to personalize the resolution. This division of labour not only accelerates service but enhances the overall customer experience and drives more value for the bank.

To track impact, banks can move beyond traditional handling time metrics to more strategic indicators – such as resolution rates for priority segments and gains in customer lifetime value (CLTV).

Predictive modelling relies on accurate data and speedy analysis to deliver the most value – particularly in areas like credit, market, and operational risk. Agentic AI enables banks to generate more accurate and cost-efficient models by accelerating the ingestion, processing, and interpretation of data. For example, faster modelling can help banks detect early signs of fraud or flag risks in a credit application. It also supports better client service, as it can carry out steps for underwriting and loan approvals while integrating client data and customer service actions in the process.

On top of the day-to-day process executions, agentic AI can track outcomes over time to generate standardized audit documentation, recommend process improvements, and deliver personalized client recommendations.

Although banks must consider governance and compliance policies before implementation, the potential boost in speed and data quality will lead to shorter development cycles to drive growth faster – the right balance of efficiency and effectiveness.

With 15 and 25% of a bank’s workforce⁴ engaged in software development or maintenance, there’s a substantial opportunity to reduce inefficiencies, lower integration costs, and accelerate timelines.

Banks that embed AI into their software operations can experience significant productivity gains – between 20 to 40% by 20284, translating into as much as USD 1.1 million in savings per engineer. These savings come from accelerating design, coding, testing, and deployment, while minimizing rework.

The end goal is to accelerate decisions and free employees for higher-value work, which results in a change in time savings and process. Success looks like reduced process cycle time, a greater proportion of talent using AI, and value creation through quantifiable hours reinvested in high-value work.

Insurance underwriting, claims processing, policy management, and risk management analysis might take hours, days, or weeks, depending on the complexity of a case or policy. AI technology can digitize data access in real-time, skyrocketing productivity and save staff members hours in a workday.

Reduced average claims processing time and lower loss ratios signal a strong start. Now is the time to modernize core underwriting and claims platforms while embedding AI to maximize productivity and cost benefits. When you notice higher contract rates and policies per agent, it means your staff has translated GenAI adoption into tangible growth for your insurance business.

Data analytics can respond faster to policyholder needs with historical data that enables insurers to offer personalized recommendations and faster resolutions to inquiries and issues. Results like higher customer satisfaction scores and conversion ratios show how data and analytics bring significant benefit to the bottom line.

Faster speed-to-market for products was as a top driver for modernizing core platforms for 86% of insurers. Core modernization will allow insurers to be more flexible and responsive to market changes, which will allow them to move forward, grow, and scale. This means quicker development of new products in response to market conditions.

Today’s insurance landscape brings more complex challenges to the business than ever before. But modernizing your technology with AI, including core platforms, will bring you the benefits needed for a viable and sustainable business model in the long term.

Tenants and landlords can identify and report on maintenance issues like water leaks, broken elevators, or temperature changes in real-time on an integrated platform. But an integrated solution goes beyond human input. Instead, it uses GenAI to automatically monitor water and energy activity, occupancy changes, and data trends to enable landlords to respond to setbacks confidently with a strategy backed by data. These systems can also connect landlords to nearby service responders with documented experience on a particular issue and arrange a quick response.

Integrated solutions also make it easier for landlords to conduct preventative maintenance before problems arise.

When we prioritize health and wellness in an integrated real estate solution, we see a chain reaction that drives productivity. Smart buildings can report on data like occupancy trends and maintenance issues, as well as indicators for health and wellness.  

For example, a landlord could monitor air quality across a real estate portfolio of commercial buildings and use those findings to implement health initiatives. This might look like replacing cleaning products with toxic ingredients, prioritizing paint that absorbs latent particles in the air that cause headaches. These changes improve productivity with reduced turnover and sick time from staff. On top of that, they allow your organization to be proactive and highlight data that brings meaningful value to your investors and tenants.  

When real estate companies de-silo data, they can draw lines and connections between poor performance and variables like rental pricing, maintenance activity, tenant relationships, and more. This broader access to data¹² makes it possible to improve strategic decisions and scale them to multiple properties. For example, an integrated solution could record higher returns on dynamic rental pricing, or efficiencies from services like occupancy analytics and energy management for tenants. These insights make it possible to scale those high-performing strategies into a larger real estate ecosystem, not just one group of parties.

The cost of an integrated solution might not feel intuitive in a time of rising costs across the board. But the productivity wins from smart buildings speed your business’s path from small-scale property ownership to a larger portfolio.

Natural-language processing (NLP) can partially automate traditionally lengthy tasks like fund and security research and trade reconciliations. It also doubles as a real-time monitoring tool for market changes to equip firms with the right data to act on a strategy. Success looks like an increase in revenue per advisor, which is made possible by the freed-up time for IM advisors to focus on more high-stakes work, like relationship-building with clients and reviewing automation outputs instead of generating them.

New clients expect seamless digital experiences with onboarding, customer service, and performance data access. Centralized wealth tech can help firms save time in the sales process with consolidated client data to help with profiles and targeting. Real-time portfolio alerts can also deliver insights and advice to clients faster, improving their retention and satisfaction.

IM firms rely on clean, accurate data to inform predictive models and influence portfolio performance. Automation and machine learning¹⁵ can process alternative and historical data to speed up model development, testing, and monitoring for better quality assurance and more accurate results.

Today’s climate calls for IM firms to streamline research, sales, and distribution with AI, while using data insights to retain investors with more personalized products and service.