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Scaling AI Programs Means Playing the Long Game — and the Short Game

Across departments and functions, identifying the right short-term and long-term artificial intelligence use cases creates an ROI advantage.

Prasad Shyam

March 6, 2023

5 Min Read
finger pushing a digital button
Gorodenkoff via Adobe Stock

Business and IT leaders alike know AI’s biggest hurdle is scalability. Across industries, organizations aren’t always positioned to start AI programs off on the right foot without clear results to showcase and earn the business buy-in needed to progress.

This age-old issue is heightened by another common mistake enterprises make -- failing to simultaneously produce pilots with short-term wins while also scaling these AI projects beyond first phases and minimum viable products (MVPs). It may seem like a Catch-22. Tech teams can’t get business sign-off for long-term AI programs without providing ROI, but to showcase any wins, they’ll still need business sign-off for short-term pilots and MVPs. Fortunately for business and IT leaders, there’s a way to play the long and short game.

Short-term AI wins will require total cost of ownership reductions, data quality improvements, and strategic sequencing of use cases to generate self-funded models. And long-term AI wins will necessitate scaling data platforms, developing and deploying MLOps strategies, and garnering sponsorship across the C-suite -- all while simultaneously implementing short-term uses cases for quick wins.

To understand how companies can have one foot in the present and one foot in the future, here are three business areas in which leaders are seeing exciting AI program developments across short-term and long-term uses cases:

1. Intelligent Industry

For nearly a decade, the market has been captivated by Industry 4.0, and leaders across sectors have accelerated their digital transformation strategies to prioritize this age of intelligent, connected business. Among the digital tools at the heart of the “fourth industrial revolution” are AI solutions that enable predictive, automated, and agile capabilities.

In the last several years, enterprises that have launched both short- and long-term AI uses cases have not only stayed at the cutting edge of Industry 4.0, but they’ve also created considerable ROI and produced benefits across customer engagement, business operations, research and development, manufacturing, and more. Take for example, the AI program run by a world-renowned, multinational hospitality and resort brand. This company has spent years cultivating short-term AI uses cases, such as developing and deploying facility-specific AI models to increase safety of guests visiting the resorts -- and has also planned for long-term projects, including restructuring, and revamping their IT infrastructure as well as installing IoT devices at all campuses. Collectively, these efforts have enabled the organization to deploy universal and generalizable AI models that can cover their various resorts around the world with minimal training and reconfiguration. None of this could have been done without aligning business and IT priorities and mapping out a strategy with short- and long-term tactics in mind.

2. Sales and Marketing

Although the sales and marketing domain may have traditionally fallen under business teams’ scope, enterprises are increasingly realizing the significant role their IT colleagues can play in enhancing sales and marketing efforts and, in turn, increasing profit margins.

AI capabilities offer sales and marketing teams a digital “crystal ball” by vast, and in some cases, untapped sets of data. These data-driven AI solutions can be used to create advanced, omnichannel customer experience strategies, enhance efficiencies across sales processes, and drive more meaningful consumer personalization efforts to reduce customer churn. More specifically, by enabling greater customer insight and smarter, faster decision making, AI also helps to make marketing automation more intelligent, generate and optimize relevant content, minimize data-related errors, improve data security, reduce costs. All of which contributes to improved short-term and long-term ROI.

For companies looking to see returns on their sales and marketing efforts in the next year and beyond, they can introduce a variety of AI pilots, such as sales forecasting, content generation, and dynamic pricing solutions. Some of the world’s largest companies in the retail and CPG space are arguably leading the charge in leveraging AI capabilities across their sales and marketing programming. For example, a renowned multinational e-commerce enterprise and an American athletic apparel corporation both utilize AI to enable personalize shopping experiences, recommending products based on search history and previous orders.

Although marketing decisions will still be made by human beings, it’s clear that AI has become an invaluable tool to enhance the sales and marketing mix and realize ROI in the short and long term.

3. Sustainability

With consumers and governing bodies across the world pressing the private sector to reduce carbon emissions, it comes as no surprise that today’s corporate leaders are prioritizing their sustainability efforts -- and enlisting digital capabilities to meet their emissions commitments. AI solutions, when implemented properly, can help enterprises fast-track their sustainability efforts.

In the short term, insights from carbon emissions data can help organizations measure, track, and report their environmental footprint. This data can also help companies collaborate with their stakeholder ecosystem to enable supplier assessments and compliance. Carbon emission insights can also help drive intelligent, environmentally friendly operations, such as optimizing logistics by utilizing AI solutions to decarbonize supply chains

In the long term, organizations are looking to develop AI solutions using emissions data to drive process improvements and sustainable product designs, focusing on material selection and the projected impact of the product’s carbon footprint over the entire product lifecycle. If businesses don’t prioritize sustainable, intelligent operations, they could not only face potential regulatory fines or taxes, but also considerably tarnish their brand reputation among an increasingly environmentally conscious population.

AI’s potential in the business world is seemingly endless, but without buy-in from both business and IT leadership, many enterprises will continue failing to fully scale their programs. By mapping out a strategic framework that simultaneously prioritizes short- and long-term use cases, organizations can fire on all cylinders and create an ROI advantage.

About the Author(s)

Prasad Shyam

Vice President, Insights & Data, Capgemini Americas

Prasad Shyam leads Capgemini’s Insights & Data practice for Manufacturing, Automotive, Life Sciences, Oil and Gas, and Energy and Utilities industries in North America. As the industry leader, Prasad is responsible for Go-to-Market Strategies, developing and delivering analytics and data-management solution offerings for multiple industries.

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