U.S. businesses want to capitalize on emerging technologies like machine learning and artificial intelligence, which have the power to revolutionize how companies operate. But the legacy technology tools businesses rely on may hinder or even prevent them from doing so.
Those are the findings of a recent commissioned study conducted by Forrester Consulting on behalf of Enova Decisions, in which 100 business leaders were asked about their views and plans for these emerging technologies.
The study points to a critical preparedness gap. Between 78 and 90 percent of respondents expect to use AI, machine learning, real-time event processing and constraint-based programming within the next two years. However, 42 percent of business leaders say their current decisioning software is incapable of integrating with today’s emerging decision automation technologies. The study concluded that top-cited misgivings with these legacy tools, such as high cost and complexity, are bound to worsen as companies rapidly adopt emerging technologies.
To overcome these challenges, company leaders are evaluating platforms with real-time decision-to-action cycles as a solution. In fact, 81 percent of respondents say such a platform would be valuable to their digital transformation goals.
“Firms need solutions capable of integrating with existing technologies and automating across the customer lifecycle,” the study stated. “Decisioning platforms that work in real time to optimize decision-to-action cycles are of high value because they will alleviate integration challenges.”
Adoption of emerging tools is already underway and is expected to increase rapidly, with 31 percent of firms currently using AI, 41 percent using machine learning, 47 percent using constraint-based programming and 48 percent using real-time event processing to help make operational decisions. By 2020 those numbers will jump to 78 percent, 81 percent, 86 percent and 90 percent, respectively.
“Legacy technology doesn’t have to be a roadblock for U.S. businesses looking to fully reap the benefits of automated decisioning, machine learning and AI,” said Sean Naismith, head of analytics services at Enova Decisions. “Business leaders can leverage digital decisioning platforms that enable real-time predictive analytics to deliver results now while ensuring seamless integration with technologies of the future.”
Other findings from the study include:
- Respondents note that the automation of operational decisions is important to executing on digital strategy (77 percent); when asked what business benefits they’ve experienced as a result of automating operational decision making, 50 percent of business leaders cite a more seamless customer experience, 46 percent say operational cost efficiency and 43 percent confirm improved business performance and key performance indicators.
- Only 22 percent of decision makers are very satisfied with the key tools they rely on to automate decisions today.
- Business decision makers believe AI is the emerging technology that will see the greatest expansion in two years (47 percent), followed by real time event processing (42 percent), machine learning (40 percent) and constraint-based computing (39 percent).
- The share of decisions that are automated will increase markedly in two years. Today, about one-third of respondents say they have the majority of their operational decisions fully or partially automated. In two years, that group will double.
- Businesses currently rely on four key technologies to automate decisions: business process management (81 percent), business rules (75 percent), statistical analysis tools (75 percent) and data warehouses (74 percent).
- The top four challenges encountered in automating operational decision making include the inability to integrate with current systems/platforms (42 percent), cost (42 percent), lack of consistency across channels, systems, and processes (42 percent) and technical complexity (36 percent).
- The top objectives for firms’ use of software to automate operational decision making are to improve the customer experience; optimize operations for improved speed, efficiency and agility of business execution; improve interactions with customers through mobile apps and other digital channels; and increase business performance and improve key performance indicators.
A report with the full study findings and methodology can be found on the Enova Decisions website.