Fewer than 10% of organizations have scaled intelligent automation investments enterprise-wide beyond pilots and POCs

Enterprises are struggling to go beyond POCs and pilots to make progress on this journey to enterprise-wide digitization using AI, analytics, and automation—the HFS Triple-A Trifecta. Huge upfront technology costs and a lack of service availability for organizational change management and value sustenance are difficult hurdles on the journey.

Exhibit 1: Enterprises are managing IA initiatives with a process or functional lens, but an enterprise-wide approach to IA is rare

Source: HFS Research, 2018

Sample: 590 business leaders including 100 C-level executives

Many AI tech solutions vendors are trying to sell the technology, while the enterprise buyers are ready to pay only for the outcomes. There is already enough expensive shelf-ware, without demonstrated outcomes and value realization, sitting in many organizations’ tech inventories. In such scenarios, purchase decisions were likely made either in a rush to beat peer to display new “in-fashion” tech or there simply weren’t enough people who knew how to utilize the expensive black boxes. Clients have learned their lessons and don’t want to fall in that “non-performing assets” trap again with expensive AI technology stacks. They are challenging the technology vendors with a “show me the money” approach to take the leap of faith across the daunting demand-supply gap of business-relevant AI.

High upfront pricing for technology and a lack of service availability are key hurdles in the path to integrated automation

Technology vendors are often trying to cash in on the high enthusiasm regarding complex AI algorithms by using phrases like “deep learning” and “neural computing” to craft compelling marketing proposition. Good business leaders don’t pay for complexity; they pay for simplification.  

Several end-user AI leaders shared their concerns and challenges regarding these demand-supply gaps:

• There are high upfront technology costs, but enterprises can only realize value long-term, large-scale implementations. CXOs and business leaders are skeptical of investing in new technologies that have a high risk in conjunction with their high potential, especially because of the enormous challenges in getting people to switch to new ways of working. Many AI-solution vendors tend to over-emphasize algorithmic marvels but grossly underestimate the cultural challenges faced by the enterprise AI and business leaders.

The two negotiating sides have different realities. Suppliers talk of technology reality, and buyers emphasize business and ground reality. Their languages are different, so it becomes nearly impossible to justify pricing models, whether input-based or output-based.

• The maturity of associated service offerings is still nascent in people and process aspects of organizational change management.

Customer leaders need an abstracted view of the underlying complexities of technologies. They care about how the complex algorithmic engines make their lives easier, and that’s all. They are ready to pay the vendors and service partners premium prices for their deep expertise to abstract the technology and shield them from the internal complexities. But, as many customer leaders shared, the maturity of these service offerings in most cases has not yet reached a state where it can meet their expectations.

Good enterprise AI leaders don’t buy AI solutions based on technology—they focus on outcomes and realized value

A prime difference between AI solutions and other software solutions is that the success of AI solutions is contextually intertwined with specific problem contexts within an enterprise, whereas most software sold with enterprise licenses performs a set of generic or mostly standardized tasks or functions. Without clear proofpoints of tangible impact and value realized from AI solutions, it will be hard for the organization to scale.

Several enterprise AI leaders shared their success stories with a “show value first, and then we pay” approach. After going through the initial hurdles for a few years, these leaders have collected enough proof that “this stuff actually works for us.” They are looking at building business cases for strategic investments with partners in a gainsharing model based on adoption and outcomes to expand IA solutions platforms, in all areas of business including use cases in IT operations, HR, and finance.

The Bottom Line: “First show me value, and then ask for the money” is the best practice for enterprise leaders to negotiate with AI technology vendors

Enterprise AI leaders must challenge technology and service providers to showcase realized outcomes from early project successes with specific, measurable goals on automating certain processes. Then, you the enterprise AI leaders must use quantified success proof-points to the momentum for scaling and further .