Artificial Intelligence
10 Tips for Implementing AI in Your Organization
More and more financial institutions are investing in Artificial Intelligence. However, not all of these AI initiatives are as successful as hoped. Why is that? And what can you, as a manager or executive, do to make AI work within your organization? Frits Korthals Altes and Egbert Clevers offer 10 recommendations for AI Strategy Execution.
10 Recommendations for Implementing Your AI Strategy
More and more financial institutions are investing in artificial intelligence and developing their own departments, roles, and skills in this area. Yet not all of these AI initiatives are as successful as hoped. Why is that? And what can you, as a manager or executive, do to make AI work within your organization?
1. Determine your focus
There are countless successful applications of AI, including in the financial sector—from chatting with a customer service bot to handling suspicious transactions. This generic nature is also its Achilles’ heel: which applications truly contribute to the organization’s success?
A good AI strategy provides clarity on this. It outlines how AI strengthens the organization. This can be achieved through obvious applications, such as AI for optimizing customer interactions or simplifying the KYC process. More important, however, are the applications in which the organization is unique and stands out from its competitors. Consider the use of AI in credit or risk underwriting, risk management, or optimizing customer value.
Only when you can clearly identify the areas where you expect to generate the most added value can you manage and monitor investments, progress, and benefits. In other words, focus.
2. Motivate your employees
For many people, AI is still an abstract concept. As a result, many people view AI as something separate from themselves. Many people believe they play no role in AI. This kind of attitude stands in the way of the successful application of AI.
Change that mindset by getting people involved with AI. Start with a large-scale (online) hackathon, moonshot, or other event. It’s not so much about the outcome; we certainly aren’t going to solve the world’s problems after just one session. No, it’s about the process that people go through. It’s about them realizing that they can be part of Renewal and the adoption of new technology. That they are part of a bigger picture.
3. Place an AI department directly under the top management
AI affects everyone. But what belongs to everyone belongs to no one. Therefore, establish a central department or team with clear responsibilities, a corresponding budget, and a mandate. This department or team should be situated at the top of the organization, so that the importance of AI is clear to everyone and knowledge about algorithms and applications is centralized.
Of course, AI will play a role in all parts of the organization. Eventually, you’ll want to change this structure. At that point, AI will be a standard tool that departments and management teams are able and willing to use. Experts from the central team will then find their way to other parts of the organization. Until then, centralized leadership—provided by someone with sufficient seniority to truly drive the organization forward—is crucial to achieving successful implementations.
4. Put Data Governance on the Agenda
Data Governance: for most people, a terribly boring topic. Yet it is vital to the successful implementation of AI. After all, data is the raw material of AI. So you need to establish processes and assign responsibilities to ensure that the right data is collected and that its quality is guaranteed. Moreover, in many cases, this involves personal data, which means privacy and security must also be safeguarded. And if mistakes are made in this area, it suddenly becomes a hot topic.
There is already a great deal of experience with data governance both within (and outside) the sector that you can build upon. Often, organizations opt for a model with decentralized Data Stewards to secure knowledge about how data is created; these Data Stewards report to a central Data Governance Manager who has sufficient authority and influence to support them in their work to improve data quality.
5. Set IT priorities
Financial institutions have already invested heavily in digitization in recent years. However, that is still not enough. For AI to be successfully implemented, IT systems must collect data in a different way and make it available for analysis.
This requires not only adjustments to the system architecture and working methods, but also the introduction of new technology. This will undoubtedly (partly) coincide with ongoing digitization initiatives. Some of these will need to be adjusted or accelerated. But what exactly is the priority? The AI strategy mentioned above helps to keep us focused.
Learning Together from AI?
6. Invest in people and develop talent
Recruitment is, of course, an obvious step. A department that will be focusing on AI requires new skills. More and more graduates possess these skills, but demand still outstrips supply. Moreover, for this generation, money isn’t usually the only motivator. It pays to consider the social value of their work and the opportunities you offer this new generation.
In addition to investing in new hires, you also need to focus on your existing workforce. Investments are needed here as well to help employees become familiar with AI and to make them aware of the ways in which they can effectively apply AI in their work. Not everyone will be able to keep up with this change. After all, innovation comes from innovative people.
7. Become part of an ecosystem
AI is evolving rapidly; there’s too much happening in the world for a single central team to keep up with. If you become part of an ecosystem, it’s easier to stay informed about developments that are relevant to your industry.
This allows you to keep track of relevant developments in the field remotely and quickly mobilize expertise when company-specific opportunities arise. The exchange among like-minded professionals also has a positive effect on a team’s motivation and job satisfaction. And that’s a major advantage in a competitive job market.
8. Make data-driven decisions
Invest in those bright minds and that reliable data, but AI only becomes truly effective when you also use it for smarter and faster decision-making. Data-Driven Decision Making: it’s easier said than done. When you make decisions based on data, you can delegate decision-making authority to lower levels of the organization. Of course, this has a huge impact on your middle management. So expect resistance.
So set a good example yourself. Lead with data. Delegate decision-making authority to management so they can do the same.
9. Create multi-year budgetary flexibility
A lot needs to be done to get AI up and running. It’s inevitable that the path we’ve chosen will eventually be called into question. Of course, it makes sense to make adjustments along the way—no problem there. But be honest and outline realistic timelines and costs up front. After all, every adjustment takes time and money.
So be sure to focus on the benefits as well: set up a benefits management system. Make sure that the improvements brought about by AI are both qualified and quantified. This will provide a balanced picture and allow for targeted adjustments—based on data.
10. Ensure your first success
Implementing the AI strategy isn’t something that happens overnight, and benefits management helps you keep track of the advantages. Be sure to share early successes with your employees right away. You’ve gotten them very involved, so make sure to live up to those expectations. Demonstrate a concrete BREAKTHROUGH within three months —that will boost motivation.
The challenge is to constantly strike a balance between two priorities: short-term results and building structures over a somewhat longer period. You can’t have one without the other.
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10 Tips for Implementing Artificial Intelligence in Your Organization
More and more financial institutions are investing in artificial intelligence. However, not all of these AI initiatives are as successful as hoped. Why is that? And what can you, as
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