Robert Kugel's Analyst Perspectives

Artificial intelligence-enabled business applications have advanced considerably over the past year as software providers have added a steady stream of capabilities. This includes customer facing, financial, supply chain and workforce software. ISG Research asserts that by 2027, almost all providers of business applications will use some form of generative AI to enhance capabilities and functionality to remain competitive.

However, many enterprises have existing on-premises applications that, in most cases, will not get AI-enablement from the software provider. Those customers should be evaluating if, when and how they will tap into the benefits that AI and GenAI can provide to improve operational and financial performance. They have three choices: do nothing, AI-enable existing software or migrate to a cloud-based AI-enabled application.

There’s no end to the potential use cases for AI and GenAI, which has led to the perception that these are more hype than reality. On the contrary, there is a long and lengthening list of existing and cost-effective ways business application providers are already using the technology, including:

• Agents for task automation
• Accelerating forecasting and planning while reducing bias
• Automating analytics production to enable more time for analysis and thought
• Providing task supervision to spot data and information input errors
• Adding recommendations to facilitate decision-making
• Automating commentary to enhance reporting
• Transcription and summarization
• Knowledge management
• Worker skills assessment
• Dynamic route optimization
• Creating tailored offers and pricing

Nonetheless, at this stage of evolution in AI and GenAI, the “do nothing” option can be attractive for those with on-premises software. Since the ability to realize grandiose GenAI use cases can seem way out in the future, and the above list of capabilities can appear trivial, many perceive the chatter about AI as hype with few proof points. With a perception of limited or no benefit, not taking any action can appear attractive and may be the right choice. Doing so poses no chance of errors of commission, and in that respect, it appears less risky, posing no threat of disrupting operations and requiring no incremental financial commitment. Most importantly, doing nothing may have a better cost/benefit ratio.

At the same time, doing nothing imposes an opportunity cost because AI and GenAI enablement can improve an organization’s competitiveness and productivity as well as reduce costs throughout an enterprise. Almost always, it takes time for organizations to absorb new technology, so earlier adopters can exploit the benefit of having addressed the people and process challenges sooner and gain a competitive advantage, greater profitability, access to talent and other benefits. This was the case with personal computers in the 1980s and the internet in the 1990s. Waiting too long to start means risking having to play catch-up. Weighing the pros and cons of standing pat versus taking action with respect to AI and GenAI should be an ongoing exercise because the technology is evolving rapidly, and competitive business conditions are continually transforming.

There are two basic alternatives to doing nothing: AI-enabling the legacy on-premises software or migrating to an AI-enabled cloud application. Choosing between the two may not be straightforward, and the best choice for an enterprise depends on facts and circumstances.

AI-enabling on-premises software is preferable where there is some combination of incurring less disruption to operations, faster time to value, lower risk of failure and lower total cost of ownership relative to migrating to the cloud. At the same time, because such AI-enablement is a custom project, there are risks. ISG Research’s 2024 AI Study identified several that apply, including a lack of AI experience and skills, a lack of a holistic company-wide vision, which can make it difficult to create a business case for such a project, the challenge of keeping up with AI technology and the resulting impact on AI performance and quality. Nonetheless, the reasons for taking this approach can be compelling.

Choosing to migrate to AI-enabled cloud software may be the better choice if an enterprise can secure more capabilities sooner. This is possible because the application already has significant AI-enabled capabilities, and the provider’s pace of development can be faster than an internal effort. Enterprises can achieve faster time to value by migrating to an application in the cloud because the software is off-the-shelf rather than a project. And because the software can be evaluated before investing, there is lower project risk. Moreover, after implementation, the provider handles the maintenance of the application, reducing the burden on the IT department. However, there can be downsides to this approach if the migration significantly disrupts internal operations or poses significant change-management challenges. The TCO to value ratio also may be unfavorable compared to AI-enabling an existing application or doing nothing.

With respect to the benefits side of the equation, ISG Research recently quantified attitudes around spend in our Market Lens AI Study, which asked participants how much more they would be willing to pay for AI capabilities for various purposes. The research provides insight into how enterprises currently expect to gain value from AI and, therefore, the propensity to pay for AI capability in applications.

The research finds the greatest inclination to spend is in sales performance management, which I interpret to mean that the participants see this area as having the highest potential to generate profit through gains in sales productivity and, therefore, increase revenue. The next five include supply chain management (to cut costs), treasury and risk management (for more accurate cash-flow forecasts, to reduce the risk of fraud and credit losses as well as cut the cost of regulatory compliance), IT service management (to cut costs), analytics and business intelligence (to gain productivity) as well as procurement (cut costs). In other words, enterprises are willing to pay for productivity gains that clearly generate revenue and easily achieve cost savings or reduce risk. Those offerings that promote productivity without a direct connection to increased revenue or cost savings, or where the cost savings are perceived to be limited or difficult to achieve, will not make the cut until investments become economically attractive.

As AI and GenAI continue to evolve from the bright, shiny object status to a practical business tool, executives are finding ways to apply the technology. Organizations with on-premises applications must have a strategy and process for assessing how to take advantage of AI technology for that application domain. Applying a knowledgeable, disciplined cost-benefit analysis for each of the three options on an ongoing basis is essential.

Regards,

Robert Kugel