Why are companies struggling with Intelligent Automation?
Intelligent Automation, the combination of artificial intelligence and process automation, is posed to make a significant impact on business. Pick your favorite analyst or research report, and you’ll find that most executives agree that it has tremendous potential. Yet, the same reports indicate that companies are struggling to use these technologies. As we’ve talked with companies, we’ve heard time and again about companies with a lack of strategy, guidance, and the skills needed to identify the opportunities. In the most aggressive companies, those with a dozen projects or more, over 90% claim to have a moderate to substantial benefit.
The root of the problem seems to be a significant disconnect between the implementation teams (data scientists, IT, product managers) and senior management. Data scientist are ingenious in how to use machine learning and deep learning (neural networks) while at the same time miss the business context, and a strategic approach to finding solutions that drive business goals. Senior management personnel thoroughly understand the business but have no idea how artificial intelligence technologies can be utilized to solve their business problems. Senior managers often have unrealistic expectations regarding what can be achieved using neural networks leading to projects with little chance of success.
Building models is only part of the challenge. It starts with the data management infrastructure. It ends with a production (operational) solution to integrate and deploy in a repeatable manner that can be supported. A strategic approach is needed that identifies solutions, facilitates model development, and operate within the business.
Much of the work consists of tactical experiments that focus on model building. Like many enterprise projects, collaboration is required to help the business, data science, and technology in a focused manner. They are generally consultants, C-Level, or senior technology people who have reached a senior level in companies. They are those problem solvers that clarify the business opportunities and translate those into technological solutions. It is those partners who have the technical acumen to understand how AI can be utilized as well as the proper business context to apply to the data.
How do these people bridge this gap?
- It starts with specialists that invest time and effort to become thoroughly versed in AI solutions and how to apply them. They work towards an understanding of the types and volumes of data needed for AI and the data required to support these models.
- Next, these specialists evaluate the core goals, objectives, and processes of the business to find the opportunities that match AI solutions. Often these are where humans interact with technology in some type of decision process. This can result in a road-map for success using intelligent automation. A solid strategy considers the business opportunity, the processes, the people, the data, and the technology.
- Once the projects have been prioritized and financing has been secured for the initial efforts, these specialists can lead the initial efforts. These leaders play the connectors across groups, and keep the focus on delivery and results.
- Finally, Intelligent Automation is not a static solution. Like any business asset, it can be dynamic. It changes with the business environment. One of the biggest challenges is integration and another is running successfully once operational. A lifecycle management process is required to maintain its value. Establishment of governance and monitoring keep the solution relevant and true. Unlike traditional IT, AI models must be audited and reassessed continually as the environment changes.
A narrow strategy can be established to attack the problems with the highest likelihood of success and with the strongest return on investment and a good chance for providing a competitive edge. It is critical that appropriate cost/benefits expectations be set during the establishment of the road-map. A broad strategy could be more extensive where the organization invests in innovation to create a competitive product or service. However, a good strategy is key to focus efforts, minimize cost, and drive results.