Artificial intelligence (AI) adds significant value across various use cases, including retail, banking, and e-commerce platforms. However, several studies indicate that, despite the potential for substantial returns, many businesses are hesitant to invest in AI.
A McKinsey study released in December 2023 found that generative AI (GenAI) could add the equivalent of $2.6 trillion to $4.4 trillion annually in economic value across 63 use cases. That same year, GenAI began gaining traction in the banking sector, showing promising early results.
McKinsey identified the most significant value potential in risk and legal (US$385 billion), corporate banking ($321 billion), and retail banking ($306 billion), highlighting how GenAI could reshape these high-stakes areas of the financial services industry.
A survey by e-commerce software firm Uptain found that no technology has a greater impact on the business world than AI. Its “E-Report 9: AI in E-Commerce” indicates relatively high awareness and acceptance of AI in e-commerce. Many online store operators still do not utilize AI, despite its growing prominence in the retail industry.
A third report by revenue lifecycle management (RLM) firm Conga revealed that despite growing confidence in AI’s potential, many companies continue to delay adoption. It queried more than 600 business decision-makers across the U.S. and U.K., revealing a significant gap between AI optimism and real-world adoption.
The studies highlight key barriers to the widespread adoption of AI. They cited outdated technology as a barrier to adoption and pointed to a steep learning curve in integrating AI features and new software solutions.
AI Awareness Grows, But Adoption Still Lags
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Julian Craemer, CEO and founder of Uptain, observed that most online shops have only realized a fraction of AI’s potential.
“The most common obstacles we observe in practice are a lack of expertise, complex setups, and fear of losing control. For AI to establish itself in e-commerce in the long term, user-friendly tools are needed that can be easily integrated and give users full control at all times,” he said.
Anuj Kapur, CEO of CloudBees, agrees. His company provides a development, security, and operations (DevSecOps) platform and tools.
“Enterprises are not equipped to incorporate AI due to widespread reliance on legacy systems. With continuous improvement, enterprises are embracing lower-cost, higher-efficiency AI development by empowering developers amid escalating global competition to develop enterprise-level AI,” he told the E-Commerce Times.
E-Commerce AI Still Underused by Online Stores
Uptain’s research found that new AI tools continue to emerge, and adoption is increasing. While 71% have used AI for their online store at least once, more than a quarter (29%) have never used it.
According to the report, respondents mentioned content creation as the most frequent use of AI, followed by marketing. However, AI remains underutilized in other promising areas, such as data analysis, forecasting, pricing, fraud prevention, and product recommendations.
Uptain’s data shows that 30% of respondents use AI for content creation, 18% for marketing, 14% for data analysis and forecasting, 13% for customer support, 6% for product recommendations, and just 3% for pricing and fraud prevention.
The survey also found that many online stores use AI only sporadically. While 55% report using AI weekly or more often, only 12% use it daily, and just 8% apply it multiple times per day.
Meanwhile, 45% of stores use AI only monthly, annually, or not at all, suggesting that many retailers are still far from integrating AI as a core strategy.
Unlocking Revenue Efficiency With AI Tools
The research examined the increasing pressure on businesses to do more with less. Nearly 97% of executives expect AI to maximize revenue within the next two years.
It found that the most significant consequences of mismanaging revenue processes are major financial setbacks like higher operational costs (50%), missed revenue opportunities (43%), and revenue leakage (35%). Because revenue management directly impacts profitability, sustainability, and long-term growth, effective revenue management is critical to creating a financially sound business that can withstand market fluctuations.
Survey respondents revealed that the top challenges organizations face in identifying and capturing all possible revenue opportunities include time-consuming manual tasks (45%), difficulty integrating data across systems (34%), compliance and security risks (32%), and errors in data or forecasting (28%). Not using AI impedes the ability to unlock greater revenue.
Recognizing AI’s value is not the issue — using its features is. Eighty-seven percent of professionals surveyed are confident in AI’s reliability to improve business performance.
The problem, according to the Conga report, is that only one in four organizations currently applies AI to key business processes, including revenue management, despite recognizing its potential.
Noel Goggin, CEO and Culture Leader at Conga, said, “To stay competitive, especially in tumultuous economic times, organizations must take a proactive approach — identifying inefficiencies, automating workflows, and optimizing revenue operations — to realize their full potential and drive consistent growth.”
Challenges Slowing Enterprise AI Adoption
These same factors also impact the processes software developers employ. Often, DevOps teams fail to leverage AI for development, resulting in weaker code security.
CloudBees’ Kapur noted that global enterprises face fundamentally different challenges from cloud-native startups when integrating AI into their development practices.
“Most enterprises are very willing to experiment with AI, and in many cases, there’s a top-down push to do so, but concerns around governance and data privacy can slow down the process,” he said.
He noted that most large enterprise customers are still in the early stages of their DevOps journey, and only a small percentage of their applications fully adhere to DevOps practices.
These companies have complex technology environments, utilizing various tools and a combination of on-premises and cloud systems. Due to their size and the associated risks, Kapur suggested using AI in very specific, narrowly defined areas.
“This helps reduce risk, limits the amount of data AI can access, and makes it easier to track whether the AI is helping. It’s better than spreading AI too broadly, too soon without clear results,” he recommended.
AI’s Role in Improving Developer Productivity
According to Kapur, productivity is the most valuable resource for large-scale software engineering teams, yet developer burnout remains a persistent challenge.
“AI can greatly help in testing and QA. Debugging flaky tests and managing bloated test suites cost developers productive hours. This is, however, an essential step in catching bugs early before they cause issues in production,” he said.
The reality is that large enterprises manage multiple pipelines across diverse engines and platforms, combining cloud and on-premises environments — a mix that introduces added complexity and security risks, Kapur noted.
“AI-augmented testing changes that.”
AI Strategy Tips for E-Commerce and Enterprise
Kapur provided a roadmap for companies embarking on their AI journey. It starts with viewing AI as an accelerator.
“Its focus and implementation need to be grounded in strategy and impact that ultimately drives business goals of increasing productivity, customer experience, and revenue growth without compromising security and trust,” he explained.
Then, identify the right partners to help deliver on this strategy. Also, focus on open and flexible platforms.
“Avoid vendor lock-in so you can evolve your strategy,” he advised.
