The ROI of AI Investments

As an AI strategist at Atlas AI, I often encounter a burning question that haunts many executives: Do AI investments really pay off?

The rapid advancement of AI technologies is ushering in a massive technological shift. Last year marked a pivotal moment as over half of organizations began experimenting with generative AI (genAI) in their workflows. Despite this surge, studies reveal that many AI projects still struggle to achieve profitability, with returns often falling short of the typical cost of capital.

Yet, the power of high-quality data and mature AI capabilities is undeniable. Best-in-class companies report impressive return on investment (ROI) from their AI initiatives. Businesses are eagerly pouring resources into AI, venture capitalists are placing their bets, and employees are rapidly integrating AI tools into their daily tasks. The promise of unprecedented efficiency, enhanced customer experience, and a definitive competitive edge makes the allure of AI irresistible.

However, as investments soar and companies eagerly jump on the AI bandwagon, it is crucial to determine if these investments are truly justified. Is the frenzy worth the hype? Generative AI is indeed a paradigm-shifting technology with the potential to transform industries and revolutionize problem-solving. However, its value lies in its real-world application.

The Definition of ROI of AI

In simple terms, ROI is a financial ratio of gain or loss relative to its cost, measured as net return divided by the original capital cost. The higher the ratio, the greater the benefit.

The ROI of AI, however, encompasses the value gained, the experience delivered, or the profit realized from implementing AI technologies and solutions. Unlike traditional investments, AI investments may not yield immediate financial returns. Instead, benefits such as self-service, task automation, and predictive analytics contribute to long-term business success by enhancing employee productivity and decision-making.

According to Gartner, “Generative AI promises unprecedented productivity improvements and business transformation opportunities, but calculating the value of new investments in GenAI requires building a business case by simulating potential cost and value realization across a range of GenAI activities.” These activities typically include quick wins, differentiating use cases, and transformational initiatives.

ROI: Hard versus Soft Returns

Management consulting firm McKinsey & Company asserts that “Generative AI is poised to unleash the next wave of productivity,” estimating that it could add $2.6 trillion to $4.4 trillion annually. This would increase the impact of all artificial intelligence by 15 to 40 percent.

The ROI of AI investments yields both tangible and intangible benefits, termed hard versus soft returns. Hard returns are quantifiable in monetary terms and directly attributable to the investment, while soft returns are indirect contributors to business value and growth.

Hard Returns of AI Investments

At a fundamental level, the hard returns of AI are directly linked to profitability. These include metrics such as increased revenue, reduced costs, time savings, and risk mitigation.

By analyzing large datasets, automating repetitive tasks, and enabling self-service, AI investments can help enterprises unlock new revenue streams, optimize existing investments, reduce operational costs, identify potential risks, and make more informed business decisions. For instance, in healthcare, freeing up data scientists to focus on life-saving molecules can have a significant impact. In e-commerce, Mercari’s deployment of conversational AI led to a 74% reduction in ticket volume, allowing the company to scale operations and reduce technical debt.

Soft Returns of AI Investments

Soft returns of AI investments encompass broader benefits that accrue over time. These may not impact profitability or revenue in the near term but are vital for long-term success. Metrics include increased employee engagement, enhanced end-user productivity, improved customer experience, and greater enterprise innovation.

By reducing work friction and streamlining workflows, employee retention improves as the workforce becomes happier, more engaged, and productive. This, in turn, leads to happier customers, less churn, more innovation, and increased customer lifetime value. For example, Pinterest transformed its employee experience and achieved a 99% reduction in issue rates for its top communication campaign by leveraging an AI bot.

Challenges in Measuring the ROI of AI

Measuring the ROI of AI is challenging due to the lack of measurable key performance indicators (KPIs), holistic vision and strategy, data quality, and implementation costs. Research indicates that 82 to 93 percent of AI projects fail, highlighting the need for caution. As digital transformation initiatives continue to struggle, it’s essential to consider opportunity costs and ensure that investments will pay off before committing significant resources.

Common pitfalls include:

1. Lack of holistic strategy and goals

2. Lack of vision alignment across business and technology functions

3. Lack of monitoring success, outcomes, and measurable KPIs

These challenges arise from factors such as project complexity, upfront investments, unrealistic expectations, evolving business environments, conflicting priorities, and poor data quality. Additionally, survey-based approaches often lead to superficial understandings of effectiveness, lacking the depth of outcome-based evaluations.

According to PwC, organizations should guard against:

1. Discounting the uncertainty of benefits

2. Computing ROI based on a single point in time

3. Treating each AI project individually

It is imperative for enterprises to go beyond simple ROI calculations, consider uncertainties, estimate error rates, measure AI performance continuously, budget for maintenance, and evaluate their entire portfolio of AI projects.

Strategies to Optimize and Maximize AI Investments

A Microsoft-sponsored market study indicates a 3.5X return on AI investments, with 5% of organizations worldwide realizing as much as 8X. To maximize and optimize AI investments, consider the following strategies:

Six Core AI Capabilities

According to an IBM study, six core AI capabilities are needed to maximize AI investments:

1. Vision and strategy

2. Data and technology

3. AI operating model

4. Talent and skills

5. AI engineering and operations

6. Culture and adoption

Three Key Pillars for Successful AI Transformation

At Atlas AI, we’ve identified three key pillars for successful AI transformation:

1. Business outcomes

2. Technology

3. Operating model

Successful AI transformation occurs at the intersection of these pillars, with technical use cases linked to outcomes, value, and measurable KPIs. Platform capabilities must align with capacity needs, and delivery processes, operations, enablement, analytics, and change management need clear definition.

Moving Towards an Outcomes-Based Approach

Aligning AI investments to business outcomes lays the foundation for optimizing investments. The outcomes-based approach involves identifying business challenges, defining specific use cases, and establishing success criteria in collaboration between technology and business teams. Assessing the required effort, technology, people, processes, and data capabilities at the onset is crucial.

The Atlas AI Business Outcomes Framework

The three key elements of the Atlas AI Business Outcomes Framework are:

1. Improving operational efficiency

2. Enhancing experiences

3. Accelerating business transformation

This framework helps organizations identify important AI use cases and determine their value or ROI. The framework is grounded in three key business outcomes: improving operational efficiency, enhancing experiences, and accelerating business transformation.

The Promise of an AI Copilot

An AI copilot is a powerful tool or conversational interface that leverages large language models (LLMs) within an enterprise environment. It automates tasks, retrieves information across domains, and processes vast amounts of data. AI copilots predict user needs, define context, and provide relevant answers through integrations across systems, creating a unified platform for seamless communication and task management.

The Atlas AI Four-Tiered AI Copilot Framework

The Atlas AI four-tiered framework for AI copilots includes:

1. Tier-one copilot: Simple LLM integration using basic API calls

2. Tier-two copilot: Customized LLM integration grounded in domain-specific data

3. Tier-three copilot: Chaining multiple LLMs for sophisticated pipelines

4. Tier-four copilot: Enterprise-grade LLM system with advanced features

A tier-four AI copilot can make service desks more efficient, automate tasks, reduce manual processes, improve employee productivity, provide onboarding support, minimize churn, and enhance security.

To Build or Not to Build?

Gartner research suggests that by 2028, more than 50% of enterprises building AI models from scratch will abandon their efforts due to costs, complexity, and technical debt. Deciding whether to build or buy an AI copilot requires careful evaluation of goals and outcomes, considering factors such as cost, complexity, expertise, resources, time, infrastructure, control, and investment.

Atlas AI's Capabilities and Rapid Expansion in Legal with the AMLAW 100

At Atlas AI, we are at the forefront of revolutionizing the legal industry with our cutting-edge generative AI solutions. Our platform offers unparalleled capabilities that enhance productivity, efficiency, and accuracy for legal professionals. Here’s how Atlas AI is making waves in the legal sector, particularly among the AMLAW 100:

• Private Chat GPT Replacement: Secure, AI-driven communication tools tailored to the legal profession.

• Document Drafting: Advanced AI capabilities for drafting and analyzing legal documents with precision and speed.

• Instant Trained Assistants for Any Use Case: Deploy AI assistants trained on specific legal tasks to streamline operations and improve efficiency.

Visit our website at Atlas AI to learn more about our capabilities and see why leading law firms trust us to drive their digital transformation.

Thank you for being a valued subscriber. If you have any questions or feedback, please don't hesitate to reach out.

The Atlas AI Team