Enterprises – evaluate IT service partners as viable options for contextual AI solutions

HCL differentiates in three areas for its in-house artificial intelligence products: partnership ecosystem, solutions roadmap, and pricing model

Service providers are on the front lines of emerging technology trends. They help their clients determine the use cases, requirements, and how to best harness the power of emerging tech. In cases where customers need solutions broader than discrete artificial intelligence (AI) tools, many providers have developed their internal intellectual property in the form of broad platforms or frameworks complete with end-to-end, vertical-specific, and functional processes and pre-trained models. This combination creates solution capabilities not available as off-the-shelf products, and it enables excellence in service delivery.

HFS recently published a Market Analysis report on enterprise AI products from 17 leading service providers in which we analyzed the strengths and development opportunities of each provider’s in-house AI product portfolio. Our analysis compared the capabilities of their AI toolsets to the HFS Enterprise AI product features and functions assessment model outlined in Exhibit 1. As a part of the analysis, we also spoke with various enterprise clients across industries to understand their requirements and experiences using AI tools from their service providers partners. In this PoV, we examine how HCL is rising to the challenge of supporting clients with contextual solutions incorporating its AI products portfolio.

Exhibit 1: HFS Enterprise AI products features and functions assessment model

Source: HFS Research 2020

HCL demonstrated a strong portfolio of enterprise AI products

In this report, we examined the role that service providers play in the evolving AI landscape from an applied technology perspective. We have identified two major components of the enterprise AI products value chain: applied AI platform solutions and frameworks, and use cases. These two components are closely connected; the AI solutions increase the effectiveness of the use cases across industries, and the use cases give developers more insight into data generation, algorithm accuracy, user experience, and other aspects.

HCL developed several AI products, including DRYiCE LUCY, DRYiCE iAutomate, DRYiCE iControl, and EXACTO, and deployed these solutions over 10+ industries. Some of its major verticals are banking and financial services, manufacturing, retail, and life sciences. HCL follows three main principles—ecosystem-led approach, IP and solution-inspired mindset, and Mode 1-2-3 alignment—for its AI strategy to develop its solutions portfolio. Exhibit 2 highlights some of its AI solutions.

Exhibit 2: The majority of the solutions are aligned horizontally and span multiple domains

Source: HFS Research 2020

Clients preferred HCL for its partnership ecosystem, investment commitment, and technology innovations

We received feedback from several HCL client references who are in senior leadership positions located in Europe, North America, and the Asia Pacific regions. The clients were very satisfied with HCL’s AI product portfolio’s advanced data capabilities, which include sparse and noisy data handling, data integration, mixed data handling, minimal data handling, and data approximation. Clients expect HCL to have more robust capabilities in the integration, security, and governance areas – such as explainable AI, integrated “single pane of glass” view, governance, security and authentication, integrated management of data storage, cloud services, and APIs.

Customer reference scores for HCL’s in-house AI product portfolio were above average compared to other service providers for its solutions maturity, pricing, innovation initiatives, and ecosystem presence as described in Exhibit 3.

Exhibit 3: HCL has a developed a mature pricing strategy and robust partner ecosystem for its in-house enterprise AI products portfolio

Source: HFS Market Analysis of enterprise AI products from leading service providers 2020

Enterprise AI customers are very happy with their providers’ flexibility in pricing models. Good relationships with stakeholders, product maturity, and flexible delivery models are the key factors in ensuring ongoing satisfaction. But they are least satisfied with their providers’ image processing capabilities. As enterprises generate more unstructured data across industries, several prominent use cases demand information extraction from image processing. For example, image processing techniques are used for quality measurement in manufacturing. HCL’s clients are quite impressed with the pricing model of HCL and image processing techniques of its solutions. The difference between scores from HCL’s clients and the average across all companies’ clients is greatest in three categories: continuous improvement of the AI solution, partnership ecosystem for best-of-breed capabilities, and flexibility in the pricing model. This illustrates HCL’s focus on its in-house enterprise AI products’ technical aspects and business outcomes.

HCL’s partnership with leading educational institutions (Stanford University, MIT, and Berkley University) helps them gain experience with cutting-edge AI concepts before taking those concepts to the market. Also, HCL acquired several companies (Alpha Insight, Actian, and Datawave, for example) in this space, and it invests a significant amount in its AI R&D to help it create a deep AI product portfolio.

The Bottom Line: HCL exemplifies the trend in service provider development of contextual AI products. Enterprises need to consider service providers as a valid option for tech as well as services.

HCL’s in-house AI products are technically mature, but it needs to focus on integration, security, and governance capabilities and domain-specific offerings. HCL’s investments, partnership ecosystem, pricing, and ambition provide resources for it to reengineer and realign its product portfolio to be more effective in the market. HCL needs to ensure that they focus on business context – not just the technical needs in order to best meet enterprise requirements.