7 Considerations for Maximizing ROI on AI/ML Investments

Motivated by multiple drivers, enterprises across nearly all industries are increasingly embracing artificial intelligence (AI) and machine learning (ML), enhancing efficiency, profitability, customer experience, and improving evidence-based decision making. Ever-increasing volumes of available data, both structured and unstructured, combined with ongoing innovations in the software and infrastructure space capable of handling large data volumes efficiently, is facilitating this adoption.

Implementation of AI technology and machine learning solutions can entail significant investments. Based on our experience spanning multiple industries, we have identified key considerations which can help any implementation of AI/ML be much more efficient, leading to a successful adoption (as compared to AI technology “sitting on the shelf”) and enhanced return on investment.

Business challenge identification: The first step towards a successful implementation of any artificial intelligence or machine learning solution is to identify business challenges the organization is trying to tackle via AI/ML, gaining buy-in from all key stakeholders. Being specific about the desired final outcome, prioritization of the use cases driven by business imperatives and quantifiable success criteria of an AI/ML implementation is helpful in creating the roadmap of how to get there.

Data availability: Enough historical data, relevant for the business challenge being tackled, must be available to build the AI/ML model. Organizations can run into situations where such data may not yet be available. In that case, the organization should develop and execute a plan to start collecting relevant data and focus on other business challenges which can be supported by available data science. They can also explore the possibility of leveraging third-party data.

Data preparation and feature engineering: This is one of the most important steps in the development of an effective AI model, worth emphasizing. In this step, in addition to the usual data cleansing, data integration, use of AI tools such as Natural Language Processing to incorporate structured data, judicious and creative feature engineering, creating the training and test data, etc., it is also important to consult with the business stakeholders and the legal team to ensure that the data/features being used in the model comply with any relevant regulatory frameworks and laws (e.g., Fair Lending). It is also important to incorporate “existing wisdom” in this step. For example, if the objective is to build a fraud detection model, prevalent fraud patterns already known to the organization’s investigation unit should be incorporated. In addition to enhancing the effectiveness of the model, this builds confidence for the end-users of the solution, thus facilitating adoption of the model.

Selection of an appropriate modeling approach: For any given business challenge, it is common to find that multiple AI and machine learning algorithms are applicable. Often, the simpler algorithm or model with fewer parameters may be a better choice (assuming the performance of different models is similar). A particularly important step in this process is to consider model explainability – is the selected model able to provide human-understandable, plain-English explanations and reasons behind its decisions? In certain regulated industries, reasons behind decisions made by an analyst or algorithm are a requirement. Many AI/ML algorithms are by nature “black-box,” in that the contributing factors for the model outcome are not clear. Model explainability packages such as LIME or SHAP can provide human-understandable explanations in such situations.

Strategy for operationalization: Having clarity around how the predictions and insights from AI/ML fit into daily operations is clearly needed for a successful implementation. How does the organization plan to use the model scores/insights? Where does the AI/ML model “sit” within the operational workflow? How will the model insights/score be consumed in the process? Is it going to completely replace some of the current manual processes, or will it be used to assist the analysts in their decision-making? Will the solution be implemented in the cloud or on-premise? How will the data flow into and out of the AI/ML solution when implemented? Is there a funded plan for procuring the necessary hardware and software? Having a well-defined roadmap that addresses such questions will go a long way in making sure that the solution gets operationalized and does not sit on the shelf.

Phased implementation approach: The human factor is one of the hurdles faced in any AI/ML implementation effort. People are often uncomfortable with sudden and dramatic changes to their existing processes. A phased implementation approach can help mitigate such concerns. We often suggest a pilot phase, in which the AI/ML solution runs in parallel with the existing process – so that relevant teams have an opportunity to compare the outcomes of the two and become comfortable with the new process.

Training, skilling and enablement: Of course, it is important to build teams with expertise in various areas of AI/ML space. Ensure that the relevant skills and resources to support the operation of the AI/ML solution are available. Any skills gaps should be bridged by either training the existing resources or bringing in new resources with appropriate skills.

Thinking through each of these recommendations and having a clear strategy from the beginning to address them will greatly enhance the chances of success and return on investment for any AI/ML implementation.

To learn more about our artificial intelligence services and emerging technologies practice, contact us.

Lucas Lau

Senior Director
Emerging Technology Solutions

Arun Tripathi

Director
Emerging Technology Solutions

Subscribe to Topics

Unifying and automating financial processes enables firms to reduce operational expenses and make smarter decisions. Join #ProtivitiTech and #Microsoft to see how #Dynamics365 can support compliance requirements and changing business environments. http://ow.ly/o7kR50Mu7ns

The #DevSecOps ecosystem is people, processes and technologies interwoven to manage the application lifecycle. It's a priority to implement practices in the DevSecOps toolchain by defining a secure #IAM program. Learn more in #TechnologyInsights: http://ow.ly/wSX650MFQSL

Project portfolio management takes a centralized approach to managing and aligning projects with company goals. Protiviti's Samir Datt shares in @TechTarget how it adds value to #projectmanagement. http://ow.ly/9BUU50MF133

#ProtivitiNews #ProtivitiTech

Protiviti's @KonstantHacker joined The @QRLedger Show to discuss the quantum threat. Watch the episode to learn when Konstantinos believes the quantum apocalypse will take place and how to prepare. http://ow.ly/8s7Q50MFSKI

#ProtivitiTech #QRL #quantum #quantumcomputing

CFOs are overhauling their technology budgets as inflation, slumping economic growth and other external forces jeopardize their earnings targets. Randy Armknecht shares more with CFO Dive. http://ow.ly/GtVg50MESoI

#ProtivitiNews #ProtivitiTech #CFODive #CFO

Load More