4 AI Investment Areas That Drive Competitive Advantage

As the pace of AI innovation accelerates, focus on these four areas to maximize value.

Suvodip Chatterjee, Head of Data & AI Solutions, Apexon

March 20, 2023

4 Min Read
acceleration speed motion on night road
Aleksey Odintsov via Alamy Stock

In the “everything AI” era, organizations face difficult choices around which AI to focus on first. Amidst the hype, AI offers real, seismic gains. Its impact is already being felt in every industry and every job role, no matter the size of the company. This effect is expecting to expand -- a recent PwC report suggests AI could contribute a whopping $15.7 trillion to the global economy by 2030. Furthermore, with promised productivity gains from AI in the region of 25-40%, today’s shrewd AI investments will propel tomorrow’s digital transformation and achieve meaningful advantage.

Organizations that succeed in maximizing AI’s value are those that, in pursuing its strategic benefits, address the full range of technical, ethical, and systemic challenges it presents the modern enterprise.

Focus #1: Trust is Critical to AI Acceptance

Hot ticket, or hot potato? The ethical issues surrounding AI can quickly inflame debate. AI bias is an inevitable, unintended consequence when programs use historic data that doesn’t represent the future outcomes we wish to project. There are many examples already: the Microsoft Twitter bot that spewed out hate speech, Amazon’s hiring algorithm fail, or the persistent issues surrounding facial recognition in visual AI systems. When algorithms make mistakes, we pay the price in ways that are quantifiable (a plummeting stock price, a privacy data breach, increased production costs) and in ways that are less obvious (missed opportunities or the reputational consequences of a biased algorithm).

To combat the misgivings around AI, companies need to operate a framework of policies and tools that assure trust. It’s a concept that the analyst firm Gartner calls AI TRiSM -- AI trust, risk, and security management. By 2026, it anticipates organizations that implement AI TRisM will realize a 50% improvement in AI adoption, attainment towards business goals, and user acceptance.

Although regulators are quickly catching up with AI advances, organizations need to demonstrate pro-activity and implement a rigorous framework that governs their AI practices to establish a sense of trust and transparency with users.

Focus #2: Self-Service Software Engineering

AI is accelerating the delivery of digital initiatives thanks to an emerging approach to software development and delivery called platform engineering that relies on self-service automated workflows.

Platform engineering improves the developer experience, simplifying tool and platform maintenance, minimizing workflow bottlenecks, speeding up the SDLC, and enabling developers to create valuable software with minimal overheads. Whether you consider platform engineering as the natural evolution of DevOps or a new era altogether in digital I&O, providing reusable components and tools has become very important in the increasingly complex world of software delivery. In fact, Gartner expects four in five software engineering organizations will establish platform teams as internal providers to meet the demand for digital products and advance digital transformation.

Focus #3: Adaptive AI for Business Agility

Unlike traditional AI, adaptive AI models are self-learning. Not only does this drive faster outputs and better business outcomes, but adaptive AI systems also help businesses achieve agility at scale. By deploying a sequence-driven data ingestion method, adaptive AI systems can learn on the go, adjusting predictions as new information becomes available.

A key advantage of these types of AI is their longer-term ROI. A short-term negative, at least initially, is the resource-intensive phase required to reengineer an enterprise’s infrastructure in preparation for this next stage in the AI journey. The benefits outweigh the challenges, however, making this a high-value investment focus that can be applied to a wider range of business scenarios, from improving customer-facing interactions to the next frontier in cybersecurity defense.

Focus #4: Sustainable IT for Smarter Business Operations

C-suite execs continue to face pressure from government regulations, investors, regulators, and employees to advance their environmental, social, and governance (ESG) goals for the good of the enterprise and the plant. AI has a pivotal role to play in a whole range of sustainable technology initiatives. AI, automation, and advanced analytics are already streamlining existing business processes and sustainable design principles in software development to ensure that ESG goals are baked into the process. For example, embedded IoT systems that use AI to reduce carbon footprint.

Design-led, AI-powered sustainability gives businesses a deeper understanding of how their ESG goals can be achieved, without compromising safety, or other key performance indicators. Sustainable IT’s benefits are not only good for the planet, but also for business. They include cost savings, increased output, and the soft benefits associated with acting in a socially responsible manner, which attracts environmentally conscious talent and customers.

Investing Wisely in AI Innovations

The potential benefits that AI technologies bring can be game-changing, and already their impact is being felt across all industries. However, to fully realize its potential, organizations must take a multi-disciplinary approach that addresses its technical, ethical, and cultural challenges. Prioritizing these principles can help organizations achieve goals such as digital transformation, competitive advantage, and business agility, while also promoting transparency, trust, and social responsibility.

About the Author(s)

Suvodip Chatterjee

Head of Data & AI Solutions, Apexon

Suvodip Chatterjee is Head, Data and AI Solutions at Apexon, a global digital engineering professional services company. Suvodip is an AI/ML and digital transformation practitioner with 20 years of extensive experience leading technology initiatives, enabling organizational innovation and growth through AI. He spearheads the advanced analytics and AI/ML solutions, innovation, and services within Apexon. With expertise covering a rare blend of domain, technology, and business strategy, he has provided analytics solutions in the areas of machine learning and statistical modeling, business analysis, and reporting and visualization.

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