Artificial Intelligence has ceased to be merely a familiar technological advancement of the future. Instead, AI has become the fundamental tool that enables vital changes within an organization, from the operators to the final decision-makers. More recently, sales leadership has been one of the areas gaining the most from AI advancements. As organizations continue grappling with the increasing complexity and competitiveness of the business environment, IT sales are being propelled by AI and Generative AI, which are solving problems and providing sales leaders with new technologies on how to interact with customers, run queues, and make management decisions based on metrics.

Initially, the main task of artificial intelligence was the mechanization of monotonous actions. At present, however, it has matured into providing actionable insights, forecasts, and suggestions. In a recent Salesforce report, 52% of IT sales leaders deploy AI technology for enhanced productivity, with a further 85% predicting that it will form a core part of their strategies within the next five years. Companies that apply AI to their business report an increase in conversions of 10 to 15% and a growth of 5 to 10% in the rate of client retention.

Managing complex sales pipelines and client contracts is a constant headache for IT sales leaders. Failing to remember a renewal date or a milestone shoots a company in the foot, resulting in incoming revenue loss and, in the future, an opportunity loss as well. AI-based applications like Salesforce Einstein, Microsoft Dynamics, and HubSpot bring in real-time alerts for such cases, along with reminders for renewals and milestones. They allow sales teams to pursue customers and improve chances for retention as well as upsells. A Salesforce global report showed that the participating companies that had AI integrated within the sales cycle saw their pipeline accuracy increase by 50%.

A case in point here is Schneider Electric, a global energy management and automation leader, that implemented Salesforce’s Sales Cloud and Service Cloud as part of its “One Schneider” strategy to unify its systems and create a 360-degree view of customers. The company enhanced its CRM with AI-powered CRM Analytics and Einstein Discovery, which analyze data from multiple sources, including IoT, to identify sales opportunities and predict conversion likelihood. This AI-driven approach helped Schneider Electric reduce its sales cycle time by 30%, ensuring that global sales teams focus on the most promising leads.

It is equally important to ensure active scanning of new customer data. The most sophisticated tools, such as Perplexity AI and TextCortex, are now crucial tools. Unlike conventional analytical resources, Perplexity and several other resources combine offline and online information to create a picture of the target audience’s behavioral activities and tendencies at the moment. This allows sales teams to access such data whenever there is a change in clientele’s needs or interests, making interaction with such clients more helpful and more focused. This function is most effective with highly developing industries or highly dynamic clients.

Tools of Generative AI, such as ChatGPT, CoPilot, or Google Bard, have also been of great use in ensuring that communication is effective and, above all, personalized. Enterprise sales teams can use these tools to automate the generation of personalized proposals, along with any follow-up and marketing copy that will appeal to their clients in no time. This level of automation ensures that interactions with clients remain relevant and personal, with no substantial input of manual effort, thereby easing the burden of responding to the increasing demands of customers in terms of their expectations for the degree of personalization and speed. The application of such tools is seen globally in many different fields, such as industry and finance, where there is increasing pressure to provide clients with tailored services and fast feedback.

Another common challenge is the degradation of knowledge, particularly during team transitions or onboarding new hires. AI-powered tools like Gong.io and Chorus capture and analyze sales conversations, while IBM Watson organizes and stores insights from past client interactions. These platforms reduce reliance on “tribal knowledge,” aid new hires in getting up to speed quickly, and ensure that best practices are documented and shared across the team. With teams often dispersed across different regions, these tools are crucial for maintaining consistency and knowledge continuity in global sales operations.

For example, Iron Mountain, a global leader in storage and information management, used Gong to reduce new-hire ramp time by 3 months and improve sales effectiveness. By leveraging Gong’s AI-driven insights, the company provided data-backed coaching and visibility into sales calls, helping new reps quickly identify the best practices and improve their performance. As a result, 60% of new reps hit their key metrics within five months, compared to just 9% before Gong, leading to a 148% improvement in performance. Gong’s call tracking and coaching features enabled more efficient onboarding and faster ramp-up, even in a fully remote environment.

As we have seen, therefore, both AI and Generative AI are transforming the IT sales landscape as they allow leaders to more efficiently manage their pipelines, enhance their customer interactions, and protect crucial information. They enable sales leaders to tackle the challenges of modern, fast-paced, global markets effectively. They can remain in touch with customers, be aware of the latest trends and strategies, and be prepared for expansion. As AI evolves further, it has been predicted that AI will complement sales functions with increasing significance, and it is likely to become an important success factor for IT sales leaders globally.

---

Reference:

---

Author:

Harsh Agarwal,
Senior Manager – Business Development

The lending industry has new opportunities due to a rise in efficient technology and new types of lenders. There has been rapid adoption of technology to streamline the overall process of getting a mortgage, personal and business loans, enhancing the consumer experience into a smoother and faster one and expanding consumer access to financing products. While many banks are working on providing a smoother loan application experience by digitising the lending workflow process and front-end platform. However, the digitisation of the industry still needs to be improved by leveraging modern technology and data effectively. Many banks still take 2 – 4 weeks to process the loan because of labour-intensive processes, the complexity of the technology landscape and the fragmented system.

Lenders using AI and ML modelling have seen improvements in loan assessments, default pattern identification, and accurate customer behaviour prediction. This helps banks to flag risky loans and make informed decisions to minimise losses.

Traditional lenders often struggle to see the E2E customer journey because data is dispersed between multiple channels and touchpoints. Thus, they lose the insights from all that data to drive a better customer experience.

Reshaping the lending Industry with Novel Approach and Modern Technology

• Non-bank lenders continue to grow popular –
  • Non-bank lenders have invested heavily in the digitisation of user interfaces that simplify application submission, processing and collaboration with customers through real-time communication using digital channels. They offer low-cost, high-value lending products while providing users with an easier path to obtaining loans.
  • According to Oracle’s Digital Demand in Retail Banking study of 5,200 consumers from 13 countries, over 40% of customers surveyed think non-banks can better assist them with personal money management and investment needs, and 30% of respondents who haven’t tried a non-bank platform said they’re open to trying one.
  • This means bad news for traditional banks that are still slow to transition and apply digitised tools to deliver differentiated lending services.
  • Neo banks operate entirely online and provide credit and lending services digitally. It leverages data models to understand customer needs and behaviours to attract new customers and retain existing customers.

• Optimizing Customer Experience
  • Based on the study conducted by McKinsey & Company, 60 per cent of customers say they are comfortable with a completely online application. Personalisation, reassurance, transparency, simplicity and speed are vital to attract and retain the customers.
    With information like demographic data, behavioural data, psychographic attributes, cash flow of customers, and alternative data sets – like social media data, and partner ecosystem data, the banks can construct meaningful customer insight and build products that serve customer needs.
  • Banks should prioritise getting things right first time, offering quick, precise, 24×7 status updates, pre-approval within 24 hours, and providing a single point of contact.
  • AI and machine learning empower lenders to provide highly personalised experiences to customers. Lenders must build advanced algorithms to collect customer data, analyse financial profiles, and suggest customised lending options. Furthermore, the platforms could leverage crowd wisdom to source the best rates, guaranteeing customers the most competitive offers. The integration of hyper-personalization with AI and machine learning has significantly improved the lending journey, delivering convenience, efficiency, and unmatched customer satisfaction.
  • An agile tech stack with seamless integrations, including access to lifestyle and contextual data, such as social media, to provide banks with a complete picture of prospects so that offers can be tailored for outstanding customer experience.

• Third-Party Technology Providers and Open Banking for NextGen Lending
  • Open banking helps create a value-driven, profitable lending journey that retains market share and margins.
  • The future banking practice demands opening customers’ entire financial footprint to trusted third parties, including mortgages, savings, pensions, insurance, and consumer credit data
  • By harnessing unconventional data sources, open banking performs a holistic assessment of customer creditworthiness
    It also helps with income verification, Know Your Customer (KYC) confirmation and customer onboarding
  • Third-party technology and data providers are leveraging open banking to support the banks. Their activities involve marketing lending products, gathering borrower information, and underwriting, closing, or funding a loan.
    The expansive list of services is available, including loan origination platform, workflow management, document extraction and management, income and asset verification, employment verification, title verification, appraisal management, e-closings, automated compliance, and decisions model.

• Cloud-based SAS solution – Improved time to market and customer experience
  • The digitisation of the Loan origination system (LOS) helps to enable self-servicing for the broker and the bank’s sales team, provide real-time collaboration, and increase transparency. Many Fintech and Product firm offer SAS solution on the cloud that helps the bank to implement the solution much quicker and faster
  • Cloud analytics services enable the correct set of tools to develop the data model and insight that would significantly help to keep the lender products competitive and help retain the customer longer
  • Cloud-based interoperable solutions enable lenders to benefit from multiple APIs and other technology that enhance the user experience and allow for new propositions to be brought to market swiftly and safely
  • Adoption of SaaS cloud-based solutions helps create a portal between the lender, borrower, and other mortgage stakeholders, offers immense potential to automate processes through self-servicing, improve opportunities and accuracy, and reduce costs and workloads.

• ESG: Driving Sustainability and Inclusion in Mortgage Services
  • ESG Integration: Organizations worldwide, including community financial institutions, are prioritising ESG considerations in their corporate agendas. This includes local banks focusing on mortgage lending to promote diversity and inclusion and improve the lives of their customers and communities.
  • Technology-driven Solutions: Banks are harnessing technology and advanced analytics models to incorporate ESG risk to enhance risk assessment accuracy and reduce funding costs. This enables them to issue mortgages at lower rates, reducing costs for both banks and borrowers.
  • Expanding Homeownership Opportunities: Affordable homeownership aligns with ESG goals, promoting sustainability and inclusion within mortgage services. Lower costs and improved risk assessment enable a more accessible housing market, fostering economic stability and improving quality of life.

Conclusion

The risk mitigation of lending and its volatile market can be controlled by leveraging data and innovative technology solutions. AI and ML data models improve fraud and risk management and proactively detect and reduce risk exposure.
Adopting SaaS and cloud computing offers flexibility, efficiency, security, increased collaboration, reduced costs, and improved time to market.

Banks can cut down 30 – 40% of operating costs through E2E automation and redefine customer journey by leveraging third-party services and open banking ecosystems. This advancement not only enhances the reliability and value of data but also enables banks to make better-informed decisions. Moreover, it also opens new avenues in the lending market, expanding its potential reach.

ESG factors are revolutionising the mortgage and business loan services industry. Cutting-edge technology solutions empower eco-friendly approaches, broaden access to homeownership, and foster financial inclusivity. This ultimately yields advantages for both financial institutions and borrowers alike.

---

Author

Kalpesh Mistry,
Senior Vice President