How do you prove the ROI of AI and deep learning projects?

Once you have clearly defined your objectives and success metrics, building an ROI model becomes a crucial step in leveraging AI-based segmentation and targeting, as well as AI-generated social media content. To create an effective ROI model, it's essential to quantify the expected benefits and costs associated with these AI-powered initiatives. One thought is to determine how AI will enhance your segmentation and targeting capabilities, leading to improved customer engagement, higher conversion rates, and increased revenue. Additionally, assess how AI-generated social media content can elevate brand presence, foster deeper connections with the audience, and potentially reduce marketing expenses.

Before setting objectives with respect to ROI, One has to take into consideration that Return on Investment can take place in different formats. Monetary ROI - This is the most common form of ROI, where both the returns and the investments are expressed in monetary terms. Non-monetary ROI: Also known as Social Return on Investment (SROI), this method attempts to measure the social, environmental, or ethical value generated by an investment.These returns are often intangible assets such as increased brand reputation, improved customer satisfaction, employee satisfaction, or decreased environmental footprint.

In my opinion, proving the ROI of AI and deep learning projects goes beyond setting clear objectives and metrics. It requires a nuanced understanding of how AI impacts different facets of your business. While cost efficiency or increased revenue are tangible indicators, I value intangible aspects such as improved decision-making, better customer experiences, and enhanced innovation.

Even before you pick AI as the technique of choice, you must frame the business problem and define what success will look like for your users. Paint a picture of the outcomes by identifying both short-term or "lead indicators" and long-term or "lag indicators." For example, a national retail bank wanted to solve the challenge of low customer spending, which happened to be below the industry average. The products team decided to track customer transactions and portfolio spread. These were the early or "lead indicators" that eventually drive customer spending, the final outcome or "lag indicator."

Just like a gardener who plants a seed today and expects a tree to yield fruits years later, AI projects are about planting seeds for future growth. For instance, an AI-powered chatbot may initially show ROI in reduced customer service costs. Still, its real value could emerge over time, as it gathers valuable data to inform product development, marketing strategies, and customer relationship management, ultimately leading to increased customer lifetime value. Hence, proving the ROI of AI and deep learning projects involves not only immediate financial measures but also strategic, long-term benefits.

An effective way to visualize this process is to think of it as a home renovation project. Before you start renovating, you have to assess the current state of your home (your organization), determine what improvements you want to make (AI or deep learning solutions), and figure out what resources you will need (data, technology, and personnel). Just like a home renovation project, you need to budget for all stages from design (development), to construction (deployment), and maintenance (ongoing support).

I believe that while calculating the cost of developing and maintaining AI and deep learning solutions is a critical aspect of proving ROI, it doesn't paint the whole picture. We also need to consider the value of the strategic opportunities these technologies unlock. Can they free up staff time for more value-added activities? Will they improve customer satisfaction and loyalty?

After quantifying outcomes, you must size up your total costs as well. AI needs investments, such as specialized hardware, software, multi-skilled teams, and change management initiatives. Additionally, there are costs to collect and label high volumes of data and subscription costs for ML model Application Programming Interfaces (APIs). Some of these could get unpredictable. Add all of them to arrive at your Total Cost of Ownership (TCO) and find when you can break even on the AI spend.

Estimating costs for AI projects involves upfront, ongoing, data, compliance, computational costs, and investments in skilled personnel and training. Upfront costs include hardware, software, implementation, and initial training. Ongoing costs consist of maintenance, upgrades, licensing, model refinement, and continuous training. Data expenses arise from acquiring, storing, cleaning, and processing data. Compliance costs are needed for adhering to regulations. Computational costs increase with project complexity. Costs for skilled personnel, such as data engineers, ML engineers, MLOps, etc., can vary depending on the scope and complexity of the project.

An AI strategist can assist in developing a comprehensive budget plan. After the initial solution deployment, AI systems often require minimal maintenance, making ad hoc, project-based outsourcing a cost-effective option. Efficient cost estimation and resource allocation are essential to avoid overspending and underinvesting. By making informed decisions about investments, organizations can sustainably utilize AI and deep learning technologies to achieve their business goals without compromising financial stability. Strategic budgeting ensures optimal utilization of resources and maximizes the benefits of AI implementation.

From my perspective, to indeed prove the ROI of AI and deep learning projects, it's critical to focus on more than just the quantifiable. Beyond the obvious benefits like revenue growth or cost reduction, the transformative power of these technologies lies in their ability to catalyze strategic benefits. This could include fostering a data-driven culture, enabling faster decision-making, or improving competitive positioning. I'm a proponent of a more nuanced ROI assessment that considers these strategic shifts.

Calculating your benefits begins with understanding what you’re are looking to improve and how you can actually measure it. The more concrete the measurement (KPI), the more tangible the benefits. And, hence, the clearer to communicate them to your stakeholders. Although it’s an important goal, “Happier employees” is harder to express in hard numbers than “Reduce customer churn by 5%”.

To calculate AI project benefits, define clear expectations and measure both tangible and intangible outcomes. Tangible benefits include increased revenue and cost savings, measured by sales growth rates or operational expense reduction. Intangible benefits like enhanced customer satisfaction and employee productivity, while subjective, can be assessed through NPS or productivity metrics. The ROI should account for benefits accrued over time and adapt to evolving AI technology and business objectives. The true value of AI extends beyond immediate returns, shaping strategic growth and competitive positioning long-term.

In the same Gartner survey, it was also clear that business and financial metrics were much more important than technical or implementation metrics. (somewhat obvious, but it was a good reinforcement).

Comparing alternatives is essential in evaluating AI and deep learning project ROI. It involves examining various solutions, including non-AI options, and assessing their costs, benefits, trade-offs, and risks. Consider implementation expenses, operational costs, time required, and potential risks for each option. For AI projects, don't forget the costs associated with data acquisition, processing, and model management. Measure each alternative's potential ROI against your objectives and factor in opportunity cost. This comparison helps ensure you choose the most cost-effective approach, thus maximizing your project's ROI.

Simply monitoring results and outcomes is only part of the puzzle in proving the ROI of AI and deep learning projects. I also believe in the need for a continuous improvement framework. This means iteratively refining our models based on our collected data, increasing their effectiveness over time. I also believe in maintaining transparency in reporting to all stakeholders, fostering a culture of trust and inclusiveness. In addition, proactively addressing issues, learning from failures, and celebrating successes will ensure that our AI solutions are efficient, reliable, and truly aligned with our broader organizational goals.

How you monitor is key, especially in the AI proof of concept or pilot stages when you’re making critical decisions about whether you want to scale the AI solution. A popular technique is A/B testing or Randomized Control Trials (RCT). This involves splitting the beta testers into two groups: a test group that uses the AI solution, and a control group that is left untouched to benchmark results for comparison. By measuring and comparing the results across the two groups, you can assess the impact of the AI solution’s performance more reliably.

If Netflix only measured its success by the number of times a recommended show was clicked on, they might miss out on significant issues like users not actually watching the show or stopping after a few episodes. Therefore, they continuously monitor an array of metrics such as user watch-time, user ratings, completion rate, and search queries to fine-tune their algorithms. Similarly, when you monitor your AI's performance, consider different perspectives for a complete picture of its health. This way, you can diagnose issues promptly, and ensure its efficiency, just like a doctor would adjust a patient's treatment plan based on monitored health indicators.

Successfully monitoring AI and deep learning projects' ROI demands clear objectives, rigorous tracking, and automation. Implement a system to gather and assess data linked to your goals, like cost reduction, service enhancement, or operational efficiency. Consistent tracking uncovers trends and areas for improvement. Factor in qualitative results like heightened customer satisfaction. Compare these measurements against your baseline to determine ROI. Leverage automated techniques for efficiency. Real-time ML dashboards help spot trends quickly, maintain model performance, alerting anomalies. These strategies combined effectively demonstrate your AI projects' ROI.

"Lead with a growth mindset, leveraging fail-fast approaches to fuel innovation in AI and automation. Embrace the power of smaller wins, allowing your team to learn and upskill within emerging technologies. When internal knowledge gaps emerge, seek external partners to complement expertise. Cultivate a culture of continuous learning, unlocking the true potential of AI and automation, and positioning your business for transformative success in an ever-evolving digital landscape."

To maintain a competitive advantage in AI and deep learning, continuous education and awareness of the latest trends are crucial. Taking courses and staying updated on AI developments will enhance your team's skills and knowledge. As new algorithms, techniques, and tools emerge, embracing them and integrating them strategically into your existing systems will enable your organization to adapt swiftly to changing requirements and opportunities and stay agile. Fostering a culture of continuous learning within your team promotes innovation and maximizes ROI. Commitment to learning empowers your organization to excel in the ever changing world of AI.

Learning and improving is an ongoing process in AI and deep learning projects. Harness the insights derived from monitoring your results to refine your models, techniques, and strategies. For instance, if anomalies are detected or performance metrics decline, investigate the causes and adjust your models or processes accordingly. Consider feedback from customers or employees as valuable input for enhancing your AI solutions. Regularly review your goals and metrics, tweaking them as your business evolves. Remember, AI is not a one-time solution but a journey of continuous learning and improvement. This iterative process not only optimizes your AI projects but also elevates their ROI over time.

Picture AI as a chess novice, beginning with basic knowledge but constantly refining strategies with every move made. Similarly, AI systems should learn from their data, turning raw experience into invaluable expertise.

Proving the ROI of AI and deep learning projects involves measuring cost savings, revenue generation, time savings, error reduction, customer retention, performance improvement, competitive advantage, and risk mitigation. By tracking these metrics, organizations can quantify the tangible and intangible benefits of their AI initiatives and justify further investments in artificial intelligence technologies.

Return On Investment could be evaluated from different angles such as revenue, operations, risk and competitive edge. But ROI has to be viewed from the context of business and AI strategy perspective. If a company is at the start of its AI journey and wants to experiment with AI , it may choose to go for low hunging fruit pilot projects. These projects are easy to accomplish with relatively low to medium ROI. But by doing so the company would have build its AI ability and AI talents via these projects, which will enable it to undertake higher ROI projects in the future.

Start by setting distinct company goals instead of fixating solely on technology. Ensure everyone agrees on success criteria before proceeding. Make sure AI addresses clear, company-aligned priorities, avoiding aimless implementation. Define how success will be measured; for instance, aim for a 12% reduction in customer call wait times in a contact center. Compare current wait times to the target to gauge AI's effectiveness. Evaluate AI technologies and solutions only after defining problems, setting objectives, and establishing a plan, including ethical guardrails. Remember the Japanese proverb: “Vision without action is a daydream. Action without vision is a nightmare.” AI decision is a tech, economic, social, ethical decision all at once

Return on Investment (ROI) can be a misleading number because it often fails to capture the full picture of the project's impact & value. AI & deep learning projects may yield intangible benefits like enhanced customer experience, brand reputation, employee satisfaction, all are challenging to quantify. ROI doesn't consider the long-term effects, such as increased market share or competitive advantage, that may take time to materialize. Focusing solely on ROI can discourage investment in transformative projects with high upfront costs but enormous potential for future growth & innovation. Evaluating AI projects should involve a broader assessment, encompassing qualitative factors & strategic implications beyond mere financial metrics.

As Generative AI becomes mainstream, ROI calculations must encompass a multi-dimensional view. Innovation, for instance, can be measured by the volume of new AI-generated outputs, like unique designs in fashion or new drug compounds in pharma. Employee satisfaction, often boosted by AI-reduced manual tasks, can be tracked via surveys or attrition rates. The AI's role in generating new business strategies is another factor to consider, even if its effects aren't directly financial. These aspects show that ROI for Generative AI extends beyond just financial metrics.

Adopting AI for the sake of claiming technological advancement without a well-defined purpose is not recommended. Not all companies may be ready for full-scale automation, and some might face financial constraints that hinder their ability to invest in AI projects. It is crucial to conduct a comprehensive cost-benefit analysis to determine the feasibility and potential benefits of integrating AI. Engaging an experienced AI or data strategist is recommended, as they possess a deep understanding of project management and the strategic considerations involved in implementing AI solutions to address specific business challenges. The strategist can ensure that projects are technically feasible and align with the organization's strategic vision.