Journey to AI Maturity for Financial Services

Introduction to TAZI's Journey to AI Maturity

While many financial services organizations are looking to implement AI, at least in some parts of the organization, the process presents risks. The goals of AI — enhanced operational efficiencies and improved business KPIs — can be tricky to achieve if the organization's leaders don't first understand the current state: from data governance and integration to workforce readiness, cost, and cultural adoption. These leaders must also have a plan in place for how they want these to evolve. Other organizations get lost assessing the ecosystems of AI solutions and products — resulting in delayed AI initiatives, and anticipated business value lost.

In this whitepaper, TAZI breaks down how financial institutions should approach their AI implementation to build strong data foundations, implement ethical AI practices, and ensure compliance with industry regulations, all while fostering an innovation-driven culture that supports long-term success. Our process uses a self-guided methodology to help institutions understand their current capabilities, how they want these to evolve, what steps they need to take, and the benefits they'll realize at each step.

Knowing Where to Start is Key to Getting Results

With all its operational efficiencies and improved KPIs, more and more financial institutions are looking to implement AI. But once the implementation process begins, many organizations get stuck when they discover just how complex and taxing implementing AI can be. One of the biggest obstacles is knowing where to start.

A successful implementation of AI needs a clear understanding of your current data governance, processes and workforce readiness, and how you want it to evolve as you move to AI-enabled processes.

Before our customers start their journeys, TAZI emphasizes the importance of setting AI goals. We've developed a unique, proprietary approach that largely relies on a self-guided process. Organizations that apply our approach will clearly understand where they are today — and where they want to be tomorrow.

The Journey Shouldn't be Overwhelming

The AI ecosystem is rapidly evolving and is increasingly crowded. Organizations that start their AI journey by focusing on a single AI use case — like Voice of Customer or Customer Growth — will likely find themselves lost in a maze of products, vendors and services.

Each vendor offers tools designed to solve a specific problem, and time-consuming delays arise when organizations obsess over selecting the right vendor within a category. Part of the reason this is unproductive is that AI is evolving so quickly, and in unanticipated ways, that by the time a choice has been made, the rationale for that choice and requirements may no longer be valid. New AI solutions result from ongoing rapid foundational AI technologies development, so keeping up with each offering's details requires continuous and careful diligence.

It's important to stay focused on the bigger picture. Implementing AI is about improving performance and achieving business value. The most practical approach is to look at solution selection from a holistic perspective. For example, if the goal is to select an AI platform that supports multiple business lines, be careful not to select one that places tight boundaries around use case handling, data usage, or data options.

At this point in the journey, flexibility is key. Look for offerings that are configurable, that can work with many data sources, and that handle multiple use cases — those that expand your range of choices rather than limiting them.

Understanding the Starting Point is Key

Adopting AI is more than just implementing new technology — it requires a cultural transformation that redefines how teams think, work, and collaborate. Success depends not only on technical readiness but also on leadership commitment, workforce engagement, and the ability to adapt to a data-driven mindset.

TAZI's step-change approach to AI maturity is a tool that helps organizations determine where they are along the AI maturity lifecycle. The Y-axis represents organizational impact and the X-axis represents operational AI maturity — from siloed AI to connected AI and, finally, AI at scale.

Organizations won't always move from Step 1 to Step 2 to Step 3 and so on. They could skip one or more steps altogether. For example, with TAZI, organizations often skip directly to Step 3. Reaching Step 6 does not necessarily signify the end of the journey — AI is a dynamic environment in which change and technological advancement are constants. Organizations should continue to evolve, provided that advancement offers real business value and is consistent with the organization's goals.

Each Step Brings Incremental Value

Most importantly, each step of the journey brings immediate productivity gains. Gains might be measured in improved business unit efficiency, improved KPIs, etc. Some steps offer greater productivity gains than others — for example, there are greater gains in moving from Step 2 to Step 3 than from Step 1 to Step 2. Knowing at which step the organization resides provides a starting point in thinking about where it needs to go to support its business goals.

The Six Steps of AI Maturity

1. Add-On AI

Usually the starting point, utilizing added AI capabilities from existing vendors. Most organizations start here thinking it's the easiest way. However, organizational AI processes at Step 1 tend to be unstable and usually lack documentation, visibility, and standards — leading to compliance risks.

2. Specialized AI

As AI education matures, the value becomes more apparent and more AI tools designed for a specific pain point are available. Financial institutions are developing first AI standards such as AI governance. However, this approach means buying dozens of specialized AI solutions, with organizational AI processes continuing to rely on standards provided by vendors — which also leads to compliance risks and high costs.

3. Explainable AI, Single Department

At this step, organizations have an opportunity to see higher value due to multiple AI use cases being identified and handled. Standardization on a common AI platform improves operational efficiencies and cost. Organizations begin to define their own AI standards. AI explainability and readily available metrics provide the type and level of information necessary for assessing performance and reducing compliance risks.

4. Explainable AI, Multi-Department

This step adds significant value as AI use cases expand to multiple departments across the organization, utilizing the same AI platform. Additional efficiencies are gained with the same data utilized in multiple use cases. No additional tools need to be implemented, reducing IT overhead.

5. Integrated AI

At this step, the accountability of Data and IT at the enterprise level has increased. Data literacy continues to build as AI becomes integrated into the culture. Data/AI automation and governance are aligned with business needs. AI acceleration is now possible as clear AI processes, governance, and compliance requirements have been defined and automated.

6. Democratized AI

At this advanced step, AI is democratized in the organization — "AI for everyone." Business teams across the organization are confident to create and maintain their own AI solutions. AI fluency is achieved across teams, fostering trust in AI-driven decision-making and creating an agile organization that thrives in a digital future. The Executive team has evolved leadership, governance, and ethics models to guide the organization through the AI transformation while maintaining continued compliance.

When using the step-change approach, one question that often arises is how organizations can leapfrog several steps and accelerate their journey. Attempting such a leap without a technology partner that supports the journey means the organization's leaders must understand the challenges and acknowledge both the costs and the risks. Embracing technology without clearly understanding the value it provides to the entire organization is ill-advised. A successful journey to AI maturity requires a change in organizational skill — often an experienced partner supplementing the team can be an effective path.

Sample Customer Success Stories

TAZI Informs Smarter Retention Outreach Delivering $20M NPV

This financial services organization tried to create an AI solution on their own for 2 years, and they couldn't. With TAZI they were able to see the first value in just 10 days.

Before TAZI, this team was manually researching which customers may be leaving. With TAZI, they instead get predictive lists that they use to organize their outreach efforts. Now they can predict customer churn, identify their most profitable customers, and they have increased the efficiency of their outreach team by 300%.

The business saw $20M in NPV, and acceleration in adoption was achieved by integrating with Salesforce so the outreach team did not have to learn a new tool. This organization continued their journey from Step 3 to Step 4 by implementing additional TAZI solutions such as Voice of Customer and Targeted Marketing — and they continue to support new business units that identify AI use cases, all without hiring specialized teams.

• 10 days to value
• 3× efficiency gain
• $20M NPV
• Accelerated adoption via Salesforce integration

TPA Moves Quickly Along the AI Journey with a Lean Team

This $1.4B revenue financial services organization committed to automating all parts of their workflows utilizing AI to improve operational efficiency and business results.

They started their journey with a single solution in one department, and have quickly implemented multiple solutions in a couple of departments. This organization jump-started their AI journey at Step 3 and quickly moved to Step 4 by implementing 6 solutions across different departments with just one business intelligence analyst and a division CTO, with help from TAZI experts.

Now this organization is planning to move to Step 5 and expand the use of TAZI across additional business units, and globally — because with TAZI they have confirmed they do not need to hire expensive data science and IT experts to implement AI at scale in a secure and compliant way.

• 78 days to implement Fraud Solution
• 185 days to implement Payment Audit
• 228 days to implement Risk Audit
• 3 more solutions in pipeline

About the Authors

Zehra Cataltepe, PhD — CEO / Co-founder of TAZI AI, provider of a patented adaptive, explainable, responsible AI and Generative AI SaaS platform. TAZI's capabilities have been accoladed in more than 30 Gartner reports, including a Cool Vendor and Magic Quadrant CAIDS report. Zehra earned her M.S. and PhD in Computer Science from the California Institute of Technology, has experience in academia and industry which led to more than 100 AI papers and 14 issued patents. She has articles published in Forbes Technology Council and Nasdaq, and was honored as "Woman Entrepreneur of the Year" in 2024, 2020, and 2019 by Women in AI in Europe, Microsoft Turkey, and the Istanbul Chamber of Commerce.

Gordana Vuckovic (G) — Chief Commercial Officer at TAZI AI, enabling financial institutions to leverage advanced AI technologies for client retention, fraud prevention, and operational efficiency. An accomplished leader with over 25 years of experience in enterprise SaaS and technology, recognized for her strategic acumen, entrepreneurial mindset, and dedication to fostering innovation and growth. As a founding member of the MACH Alliance (Microservices, API-first, Cloud-native, and Headless), Gordana helped establish the Alliance as a key advocate for flexible and scalable enterprise technology. 2022 CRO of the Year and 2020 Women in Technology Award by Aragon Research.