Learn why AI is essential for financial institutions’ future success and how Zennify is leading the way in AI-driven consulting.
Introduction:
Chris, could you start by introducing yourself and your role at Zennify and sharing a little about your background in the finance and technology sectors?
I joined Zennify in April 2023 as Chief Executive Officer. I’m a customer success and IT services veteran with over 15 years of experience in the Salesforce ecosystem and 30 years in technology.
Most recently, I was the Senior Vice President of Customer Success at Salesforce. I led the North American Success team responsible for ensuring the retention and growth of the $15B customer base. Before that, I was the COO of Model Metrics (acquired by Salesforce in 2011) and was a board advisor to Silverline and 7Summits, services firms within the Salesforce ecosystem. I was privileged to advise them on scaling and company growth.
We have a fantastic opportunity at Zennify to push boundaries and change the way consulting is done, using AI and tools to accelerate implementations and customer time to value. We strive to be the top boutique Salesforce and nCino consultancy for financial services firms. I’m proud to be here at Zennify and to continue upholding our reputation as one of the go-to partners for financial institutions that want to see accelerated outcomes.
Why financial institutions should ban AI at their own risk:
Chris, you’ve raised the idea that financial institutions should not ban AI at their own risk. Could you elaborate on why you believe AI is crucial for the financial sector’s future and what potential risks they face by not embracing it?
AI has and will continue to impact the breadth, depth, and quality of products and services offered by financial institutions. There are multiple use cases for AI – and a lot of them focus on increased efficiencies. For example, teams can use AI to better predict and assess loan risks, improve fraud detection, provide better and faster customer support through smarter personalization, and analyze data in unstructured ways – all while reducing costs. These are use cases that would have typically taken more time and have more room for errors. Understanding and implementing AI thoughtfully leads to sustainable business growth and staying ahead of your competitors.
We’ve heard that a lot of financial institutions are concerned with data security, which is one of the primary reasons for considering banning AI tools such as Chat GPT. We believe that organizations can solve this security challenge by working with providers like Zennify and Salesforce, who understand how to build strong data foundations, understand the current landscape, and can provide recommendations on whether to build your own models versus bringing in open market options.
When discussing the risks of not adopting AI, some argue that it could lead to a competitive disadvantage. Could you share your thoughts on this perspective and its implications for financial institutions?
We see two major risks. Financially, higher interest rates and slowing growth have pressured costs and margins. Leading FI’s are utilizing AI to drive back office efficiencies that will improve their competitiveness in the marketplace, those who fail to adopt will have to absorb the burden of higher cost structures.
Second, consumers are increasingly interacting with AI-driven fintechs where the user experience is seamless and efficient. Given increasing consumer experience expectations, those who do not tap into and incorporate AI risk losing customers. We saw this happen with deposits as consumers fled from traditional banks to fintechs where they could open high-yielding deposit accounts within minutes.
Due to AI, the four-day workweek might be more realistic than you think:
The idea of a four-day workweek becoming more realistic due to AI is intriguing. Could you explain how AI technologies might contribute to this shift and what benefits they could bring to both employees and employers in the financial sector?
The concept of a four-day workweek enabled by AI is gaining attention due to the potential for increased efficiency, productivity, and automation in various industries. We’re talking about the automation of repetitive tasks, enhanced data analysis and insights, customer service improvements, and remote work facilitation – use cases that increase employee productivity, improve customer experience and lead to better work-life integration and balance.
This improved work-life integration and balance frees employees from routine tasks, and opens up time for them to skill up and invest in professional development opportunities – as well as spend more time with their families and giving back to the community. Within the financial sector, institutions view deep customer relationships as a primary differentiator, and AI is going to give employees time back to focus on their customers and the local community.
From your experience, can you provide examples or case studies where AI has led to increased productivity or more efficient work schedules in financial organizations?
At Zennify, we use our in-house LLM called Arti – our internal ChatGPT to help with content creation and internal content discovery. This has increased productivity for our marketing team when creating assets like blog posts, and we’ve optimized our onboarding process by making it easier for new team members to find information. We’re excited to bring these use cases to our customers and have done so with our AI advisory and experimentation solutions.
We are seeing significant productivity gains in back-office operations, specifically for use cases that currently require repetitive labor-intensive tasks with complex compliance-driven workflows. Examples of this include business loan documentation and financial wealth advisory plans. FIs are utilizing AI and data-rich large language models to reduce the amount of work and time it takes to generate documentation and streamline the process workflows.
Digital agility in banks: best practices from customer deployments
Digital agility is a vital aspect of staying competitive in the finance sector. Could you share some best practices or success stories from customer deployments that highlight the importance of digital agility in banks?
One of our clients in the agtech lending space went through a digital transformation journey to impact their lending process. They leveraged Zennify’s expertise in the ecosystem and saw a 2000% increase in revenue, improvements in the customer & employee experience, and ROI on their tech investments.
They shared with us the following best practices: Getting internal commitment from stakeholders, be open to asking for help, and start with a clean slate.
Challenges in implementing AI in financial institutions:
Implementing AI in traditional financial institutions can be challenging. What are the main hurdles you see when it comes to adopting AI technologies in these organizations?
Here are some of the main hurdles I see:
- Legacy systems & infrastructure were not designed to accommodate modern AI technologies. Integrating AI into existing infrastructure can be complex and may require significant investments in system upgrades and compatibility enhancements.
- Data quality and accessibility. Good AI needs a strong data foundation: high quality, diverse, accessible, well-structured. Data is usually siloed, and has inconsistent formats. The lack of standardized data governance can hinder the training and performance of AI algorithms
- The financial services industry is highly regulated and AI is an ever-evolving field, so ensuring compliance with data protection, privacy, and financial regulations can be complex.
- Risk management & explainability: You need transparency and explainability in AI systems.
- Talent and skills gap. Deploying and developing AI systems requires expertise that may not be easily available
- Change management – as with any transformation journey, change management is crucial to align all stakeholders in the organization. This helps mitigate or address issues such as ROI concerns.
Innovation and best practices:
Chris, personally, what strategies have you found most effective in leading Zennify and helping financial institutions navigate the evolving landscape of AI and digital agility?
As with any digital transformation journey – especially with AI – all stakeholders and decision-makers need to lead by example. Leaders need to champion internal efforts to adopt AI and show that they are utilizing these tools and/or strategies. I leverage several GenAI tools daily and highlight the impact they have made on my productivity, encouraging others to follow suit.
It’s also imperative to facilitate an environment of innovation and experimentation. Encourage building internal use cases or experiments so that all teams across the organization can be part of this journey.
In the context of innovation and digital transformation, what trends or developments are you personally excited about and believe will have a significant impact on the industry?
All innovation and digital transformation need to result in a positive customer and business outcome, so I’m excited about these developments as they can create exponential revenue growth and meet customers where they are:
- AI-driven personalization helps enhance customer experiences, which can lead to more tailored investment options and banking solutions for individuals
- Open banking initiatives and APIs can foster collaboration between financial institutions and third-party service providers – enabling the development of more innovative financial products and services.
- Regulatory technology (regtech) is using AI and automation to streamline compliance processes, reducing costs and errors in adherence to complex financial regulations.
These trends are revolutionizing the financial industry by improving efficiency, enhancing security, and providing innovative services to customers. They’re shaping a more inclusive, tech-driven, and dynamic financial landscape that has the potential to reach a broader spectrum of individuals and businesses.
Final thoughts:
Could you share your personal vision for the future of AI in the financial sector and how it might reshape the industry in the coming years?
I haven’t mentioned ethical AI implementation, which I believe is crucial in the sustainable future of AI in the industry. There will be a growing focus on ethical AI implementation, and recognizing bias considerations to ensure transparency, accountability, and fairness in the algorithms and decision-making process. I also believe that AI will augment human decision-making capabilities. Good data foundations lead to useful AI outputs that provide comprehensive data analysis and insights – enabling quicker, more informed decision-making in investments, risk management, and customer service. Overall, the future of AI in the financial sector will revolutionize how financial services are delivered, making them more personalized, efficient, secure, and accessible. It will transform the industry into a more customer-centric, technologically advanced, and inclusive landscape.
Chris Conant
Chief Executive Officer at Zennify
Chris Conant, CEO of Zennify, is a seasoned technology services executive with more than 15 years in the Salesforce ecosystem. He believes that with strong teams and customers willing to embrace change, anything is possible.
