Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector - part two

Previous Center authors Kevin Desouza and Gregory Dawson and I recently wrote a paper on Artificial Intelligence and the public sector that was published in Business Horizons, a Journal of the Kelley School of Business, Indiana University.  This article will appear on our blog in a three-part series to include background information on AI and cognitive computing; designing, developing, and deploying cognitive computing systems, and harnessing new technologies.  This blog is the second in the three-part series.

Designing cognitive computing systems

Low hanging fruit vs. grand challenge

With a low-hanging-fruit initiative, the organiza- tion might look at a problem for which automation through the deployment of a CCS can alleviate mundane work and increase efficiencies. In a grand challenge, the organization can look at how CCSs open opportunities for business model transformation and disruptive innovation. Indeed, the use of CCSs can play a key role in developing innovation through the judicious application of analytics (Kakatkar, Bilgram, & Fuller, 2020).

Organizations with immature information systems capability often benefit from tackling low hanging fruit firstda wise strategy for all types of systems development but even more appropriate for a CCS. This allows them the opportunity to gain experience working through the various phases of system development and deployment. They can build on existing data assets and system capabil- ities while extending their current knowledge base and expertise. However, organizations that have deep in-house expertise in information system development and/or have sufficient IT resources can successfully engage in a bold challenge. Here, the organization can leverage the collective wis- dom to identify key opportunities for CCS design that align with internal or environmental disrup- tions. As part of this initiative, the organization can launch an educational campaign to increase the workforce’s familiarity with cognitive systems techniques and applications. This does not mean that the organization should only look internally for necessary resources. All organizations, but particularly public-sector organizations, have a rich ecosystem of commercial partners that can contribute to the effort.

Technologically sophisticated organizations ready to approach a bold challenge are the exception rather than the rule. While these advanced firms infuse innovation into their corporate DNA, most organizations lack the maturity necessary to undertake a major cognitive computing effort without first testing the waters and improving with easier, lower-risk projects. Finding small success with these low hanging fruit will help the organization to mature and build some credibility before tacking a bold challenge.

Ensure necessary capabilities

At the start, organizations must take a close look at data, technological, organizational, and envi- ronmental elements to determine if any CCS implementation is feasible and, if so, which type will most likely yield a successful result. Indeed, care must be taken to see that the proposed solution does benefit the organization since poorly planned or conceived CCSs can hinder rather than help an organization’s value chain (Canhoto & Clear, 2020).

Data

The organization must determine whether the data are available, accessible, and analyzable in order to take advantage of CCS algorithms. These issues need to be considered carefully if the or- ganization is tackling an accessible opportunity. While there will be costs associated with readying data due to cleaning and connecting (i.e., integrating), they should not be exorbitant. However, if the data are not available, it could be a sizable and costly effort to find, validate, and incorporate the data into the CCS scheme. Absent available data, the effort may be classified as a grand challenge.

In the case of grand challenges, ideas should be solicited regarding what data to analyze, what data sources need to be procured and integrated, and what major trials the organization will confront as data is readied. Within the public sector, processes have evolved to deal with properly communicating the data risks of these grand challenges before the effort begins. Unfortunately, the public sector has perhaps overengineered this process, which could explain why they take so much time.

If a private-sector organization goes after a grand challenge, it needs to develop and implement a repeatable process for properly understanding the advantages and disadvantages of collecting such data. In addition to the simple technical aspects of collecting, cleansing, and deploying the data, legal and ethical implications must be considered. The stakes with these pro- jects are high, and it is often helpful to collaborate with outside experts to steer the data procurement process although they should have no direct or indirect role in the implementation to avoid conflicting goals.

Technology

On the technological front, the organization must have a good handle on its current IT assets both from an infrastructure and capability perspective. Regarding infrastructure, does the organization have the necessary IT applications and allied technical resources to undertake a CCS development effort? If not, can the organization foster existing or new partnerships to access the necessary technical resources? Governments are particularly apt at identifying skilled partners to support the work. A private-sector organization can also do more to leverage other potential partners who can provide the missing resources and, ideally, share in the risk and the reward of the initiative. Some of the public agencies we studied use a risk-versus-value determination for aligning with partners. Risk can be high or low and value can be high or low (see below table), and the particular result can drive the partnership decision.

In the case of government, the most salient domain is risk; the natural tendency is to try to minimize risk rather than maximize value. In contrast, private-sector organizations are more likely to prioritize value over risk and that can alter their approach. The organization needs to take a careful look at what expertise/knowledge should be developed internally versus what can be sourced.

As we will discuss later, CCSs require training from human experts and should be subject to audits; organizations should carefully consider how they continue to develop technical capabilities to (1) retain the expertise to design the next-generation of CCSs, and (2) investigate, fix, and learn from CCS deployments. If too much work is outsourced, the organization faces costly support for future deployments, and this may be particularly acute in private sector organizations more focused on the value versus risk.

Organization

The organizational front looks internally to understand the organization’s current strengths and weaknesses. For organizations new to CCSs, the organizational front is considerably more challenging. Organizations often have trouble assessing their own capabilities in a candid way.

This organizational front problem is even more acute in government due to the long tenure of most government managers and their frequent lack of comparative outside experience. Without an external frame of reference, an appropriate assessment is more challenging. Agencies have made some progress in this area through data sharing, which helps all levels of government face similar challenges and limitations.

In contrast, very few people in the private sector have spent their careers with just one firm, and most managers have a frame of reference against which to evaluate their current employer. If the organizational assessment reveals a lack of maturity, the organization can simply buy/hire or rent/contract with firms or individuals with the necessary skills.

Environment

The environment front looks at any efforts underway at other companies in the same or adjacent industries. The goal of the environment scan also differs for public-sector versus private-sector organizations due to the nature of the domains. A realization that other government entities use CCSs allows the public sector to request necessary information and then apply that to their problem. However, in addition to commercial companies being unwilling to share their confidential information, competition can drive CCS adoption in order to avoid being boxed out of the market by a rival firm already progressing strongly in this area.

A government can perform an environment scan more easily since most civilian government applications are transparent. Thus, public-sector organizations can share this information to develop a clear sense of environmental status. Not surprisingly, the private sector has a bigger hurdle with this since most organizations will not freely share their competitive plans with others in or adjacent to their industry. Thus, the private sector needs to do a great deal more speculating on the maturity of efforts in other companies.