Artificial Intelligence (AI) and machine learning have seemingly limitless applications, but there is often a gap between our understanding of its benefits and our ability to leverage it effectively to get the desired results. A commonly asked question is not “What are the benefits of AI?” but in fact “How can I operationalize AI to meet my business goals?”
Many organizations are able to initially implement AI, but wonder why their initiative ultimately fails. The main contributing factor is the misconception that technology can solve the whole problem. It is vital to remember that technology (and AI) is an enabler, but not the entire solution. Defining the problem and identifying the goals to solve it, aligning these goals with the overall business strategy, and applying a holistic approach to technology adoption, are vital steps to reach the desired outcome.
The following article shares some of the ways technology solutions have been successfully implemented and the best practices for an effective holistic approach. It is important to note that while AI is the example in question, this method can be applied to any type of technology deployment.
A Holistic Approach
Successful use of AI requires a strategic path, one that is uniquely catered to your team’s needs and goals. However, the ultimate blueprint for successful AI adoption is dedication to a comprehensive shift in mindset.
A holistic approach for successful AI adoption starts with making sure that the business goals align with the overall strategy. From there, it is important to follow a comprehensive system that includes designing an optimal workflow, mapping out processes, engaging people with the right skillset, having the right infrastructure support and adequate budget resources, whilst also maintaining a strategy for people and change/stakeholder management. There must be a defined and supported team culture that is risk averse, flexible to changes at every level, and proactive, all the while remaining true to what makes your firm unique.
With this special approach, the five steps outlined below will become more natural. Your team will know to define the problem and identify the path to a solution whilst continually keeping the overall outcomes in mind. You will guide them to start with the bigger picture, and then look for small changes and constant improvements, keeping regulations in mind throughout the process. Your continual investment in your team of people will pay dividends; they are your most vital resource and will be the true marker of success throughout this process.
5 Steps to Success
1. Define the Problem + Find Optimal Application
Like any successful technology, AI is most effective when you are able to first the define problem that you are looking to solve. It is easy to overlook this crucial first step and underestimate the importance of problem definition, which is a challenge in itself. You will need a good idea of the scale and scope of your problem before jumping to AI to solve it: Ask yourself, “What are the key business objectives? What is it that we are looking to solve? How does it align with our overall mission and business strategy?”
Once you have these answers, you can map out a multi-step process of how to solve your problem, and AI can be integrated as one of these steps. Having a holistic view of the problem and the desired outcome while keeping the business strategy and stakeholder experience at the forefront of all planning is imperative to the success of any technology deployment.
AI adoption strategy must align with your team’s overall risk appetite, budget, outcome-orientation, and culture. It is important to have conversations among the team to discuss questions such as “Where and how do we want to use AI? What are the benefits or the outcomes we seek? What is our budget? What is our organizational and board culture around innovation?”
When the methodology is mapped out and you feel like you have a sense of your firm’s preparedness for AI, then it is time to determine the optimal application for AI in your organization. The following points are a great start:
• Simplify the process
AI is a great tool for automating repetitive manual tasks, like those involved in regulatory compliance or PII identification. For example, large banks are experiencing false positives in their compliance systems at alarmingly high rates, and AI has been used to analyze dozens of data elements to eliminate high numbers of false positives from these rules-based compliance systems.
• Make the process “smarter”
Improve your internal systems by learning more about the patterns involved in day-to-day risk mitigation and data management with AI. Financial institutions are being forced to comply with regulatory requirements that revolve around the management and analysis of big data. AI can increase the efficiency and lower the costs of compliance by automating processes that previously required manual work.
• Increase efficiency
By automating processes, you can reduce the resources spent on key workflows and reduce the overall time spent on deliverables. In a recent survey, nearly half of the financial institutions surveyed indicated that they have achieved 15% or more in cost savings from automating systems within the past two years.
Then, start the search for the right AI platform for your organization. The right platform will be a good match for your team’s skills and expertise and have the functionalities necessary to help you achieve your business goals.
2. Start Small & Learn from your Success
The initial foray into AI doesn’t have to be a large undertaking. Automation, segmentation, and behavioral analytics are just a few of the many strong candidates for practical, real-world applications of AI. Solutions like these are data intensive, with minimal regulatory incidence, and carry a relatively low risk of failure.
It’s helpful to focus on an agile approach, processing rapid proofs of concept (PoC) with clear outputs and benchmarks. With a strong PoC strategy, you can ensure that your concept and approach are validated through flexible development, and PoCs can be transformed into full implementation programs with ease. This also serves a dual purpose, allowing your team to conduct adequate due diligence. Neither human nor machine is perfect, for this reason, testing, quality control, and audit are key.
Learning from your success in this way can improve the credibility of the implemented AI solution within your organization as your team becomes more confident with the product. Overcoming fear and distrust is a larger task than many assume. It is important to take time to understand the AI software for its predictability and reliability, and to ensure that the necessary workflows are put into place and are comprehensive.
3. Organization Culture: A Scientific Mindset
Adopting and advancing AI requires an organization and its people to embrace a more scientific mindset. What does this mean? The team needs to be comfortable with a journey of trial and error to reach the final product. A scientist learns from failure, continuously testing and validating the feasibility of existing “elements” while introducing new patterns, behaviors or variables to examine the system as a whole. It is important to continuously observe your inputs, outputs, and outcomes for anomalies and ensure that you are working with the right product mix in the proper manner. This will allow you to fix issues that may arise, mitigating risks while enhancing the aspects of the system that are working well.
This mental shift is not solely for heads of business, but is relevant to all areas of the organization, including the board and functions such as Risk and Compliance, HR, and notably IT and Finance. Just as every scientist must invest in equipment, your team needs to invest in the proper hardware infrastructure to support AI. New hardware tools, particularly GPUs, provide powerful parallel processing and enable users to apply multiple processes to a single unit of data simultaneously. There is no way to get around the hardware cost of incorporating AI; it should be part of the conversation from day one. Consider performance, scalability, and maintenance, among other costs.
4. Keep the Regulations in Mind – Show your Homework & Protect Privacy
If you are planning to adopt AI, you can reasonably expect that the level of scrutiny from your supervisors will increase with its implementation. I like to use the analogy of homework when I give advice about regulation: expect a failing grade if your final exam is turned in with only the answers written. With increased diligence in regulation, management and regulators will demand to see the work behind the reporting of results to understand if a company is actually meeting its requirements. While AI reaches final outcomes, it does not explain the steps it took to get there. Be sure to document every step of the way as you implement an AI solution.
It is also important to consider privacy regulations. Almost every aspect of corporate regulatory compliance includes data that may be subject to privacy protections, especially if European data subjects are involved. Accordingly, AI systems for compliance will have to take into account whether certain data subject to privacy protections can be used to train the AI system.
5. There Is No “One Size Fits All” Solution – Human Expertise Is Always Necessary
Every product will have its flaws, and this is where investing in your team is crucial. Though there is no perfect solution, there can be the right combination of people, process, and technology to make your AI project successful.
Even in the age of AI, human involvement and expertise is still a vital contributor to a well-functioning corporate technology strategy. The most successful AI implementations will ensure that the holistic framework integrates human knowledge and experience where appropriate. At the same time, it is of the utmost importance to take care of your people during the onboarding of AI. It is important to fully understand the impact that such transformation will have on your team’s culture and talent strategy, and to put in place the necessary measures to address any adverse effects. Most firms will need additional technical specialists to help design, test and manage AI applications. Recruitment practices and channels will need to be updated, and retention, integration and succession strategies of career paths developed.
Successful Implementation of Artificial Intelligence
Success is never guaranteed when adopting technology, especially with sophisticated AI solutions, but organizations that adopt AI with the above steps in mind – or find a similar system that works for them – are able to significantly increase efficiency in time and cost, to automate workflows, and use information from valuable insights and predictions that AI provides. It means being able to effectively achieve your goals and results in strategic advantages to your business. When you have identified the problem, leveraged the right processes, engaged the right team of people and taken regulatory considerations into account, your team will be significantly more prepared for any challenges that may arise.
AI is a powerful tool but it is vital that it is applied as an enabler rather than a full solution. It can assist with business goals and help to solve strategic problems if you are outcome oriented and keep the overall business objective and stakeholder experience constantly at the forefront. Once you have adopted AI successfully in one area, it is easier to replicate it in another. Think of small incremental changes that will become micro-habits and create a system of constant improvements. Keep identifying problems, mapping out the processes, adding the right people, and applying AI to continue solving problems in your business.
Keep pushing until innovation and solution design are second nature: you will know your team is ready to examine and implement more bespoke AI solutions because you will no longer need a blueprint.
Holly Henry, Associate at Forensic Risk Alliance also contributed to this article.