Organizations face significant obstacles in development and implementation of AI technologies. Recent research suggests a four-step framework for integrating AI technologies that can help companies to achieve their objectives and implement useful business-process enhancements.
The Challenges of AI
There are several factors that can throw AI initiatives off-track, ranging from technology issues to not being able to find talented people with the necessary skills.
- Difficulty of integrating AI with existing processes and systems
- Expense of technology
- Expense of expertise
- Lack of senior level understanding of AI technologies and the business benefits
- Scarcity of people with expertise in AI technology
- AI technology is immature
Step 1. Understand the AI Technologies
It’s essential to understand what AI technologies perform what types of tasks, and the strengths and limitations of each method.
Rule-based expert systems and robotic process automation do their work transparently, but neither approach is capable of self-learning and self-improving.
Deep learning, on the other hand, is great at learning from large volumes of data, but it’s almost impossible to understand the underlying models that are being created by the learning process. This so called “black box” issue can be problematic in highly regulated industries such as financial services, in which regulators need to know why decisions are made in a certain way.
It’s easy for organizations to waste considerable amounts of time and money pursuing the wrong technology for the job at hand. So make sure that you have a good understanding of the different AI technologies, and which one might best address your specific needs. Use ongoing research and education to build this knowledge and understanding within your IT department or innovation group.
Importantly, companies must leverage the capabilities of key employees such as data scientists, who have the big-data skills required to learn these technologies. A vital ingredient to success is to have people that are willing to learn. Some will be flexible and leap at the new opportunity, while others will want to resist change.
If you don’t have data science or analytics capabilities in-house, you’ll probably have to build a team or innovation group of external service providers to plug the gap in the short term. If you expect to be implementing longer-term AI projects, you will need to start to recruit and develop expert in-house talent. Either way, having the right capabilities is essential to progress.
Given the scarcity of AI technology talent, most organizations should aim to establish a shared pool of resources – perhaps in a centralized function such as IT or strategy – that can provide the necessary expertise to high-priority projects throughout the organization. As knowledge grows throughout the organization, it may make sense to dedicate groups to particular business functions or units, but even then a central coordinating function can be useful in managing projects and business needs.
Step 2. Create a Portfolio of AI Projects
The next step is to carefully consider which parts of the business could benefit most from AI applications and then develop a prioritized list of potential projects.
Identify potential AI application opportunities.
Typically, most immediate benefit can be achieved in the parts of the company where insight derived from data analysis would have most impact but for some reason is not currently available. Some challenges are as follows:
- Bottlenecks. In some cases, the lack of insight is caused by a bottleneck in the flow of information; knowledge exists in the organization, but it is not distributed or used efficiently. Knowledge tends to get siloed within departments, or stored across different systems, in different formats and with different access methods.
- Scaling challenges. In other cases, knowledge exists, but the process for using it takes too long or is expensive to scale.
- Inadequate firepower. Finally, a company may collect more data than its existing human or computer firepower can adequately analyze and apply. For example, a company may have massive amounts of data on consumers’ digital behavior but lack insight about what it means or how it can be strategically applied.
Determine AI uses cases.
The second area of assessment evaluates the use cases in which cognitive applications would generate substantial value and contribute to business success. Start by asking key questions such as: How critical to your overall strategy is addressing the targeted problem? How difficult would it be to implement the proposed AI solution, both technically and organizationally? Would the benefits from launching the application be worth the effort? Next, prioritize the use cases according to which offer the most short- and long-term value, and which might ultimately be integrated into a broader platform or suite of AI capabilities to create competitive advantage.
Select the AI technology.
The third area to assess is whether the AI tools being considered have the capability required for the use cases that have been identified.
Chatbots and intelligent agents, for example, may cause frustration as most of them can’t yet match human problem solving beyond simple scripted cases (though they are improving rapidly). Other technologies, like robotic process automation that can streamline simple processes such as invoicing, may in fact slow down more-complex production systems. And while deep learning visual recognition systems can recognize images in photos and videos, they require lots of labeled data and may be unable to make sense of a complex visual field.
In time, cognitive technologies will transform how companies do business. Today, however, it’s wiser to take incremental steps with the currently available technology while planning for transformational change in the not-too-distant future.
Step 3. Launch Pilots
Because the gap between current and desired AI capabilities is not always obvious, companies should create pilot projects for AI applications before rolling them out across the entire enterprise.
Proof-of-concept pilots are particularly suited to initiatives that have high potential business value or allow the organization to test different technologies at the same time. Take special care to avoid “injections” of projects by senior executives who have been influenced by technology vendors. Injected projects often fail, which can significantly set back the organization’s AI program.
If you plan to launch several pilots, consider creating a AI center of excellence or similar structure to manage them. This approach helps build the needed technology skills and capabilities within the organization, while also helping to move small pilots into broader applications that will have a greater impact.
A redesign of workflows will be necessary to ensure that humans and machines augment each other’s strengths and compensate for weaknesses. Focus on the division of labor between humans and AI. In some AI projects, 80% of decisions will be made by machines and 20% will be made by humans; others will have the opposite ratio.
By automating established workflows, companies can quickly implement projects and achieve ROI but may forgo the opportunity to take full advantage of AI capabilities and substantively improve the process.
Workflow redesign efforts often benefit from applying design-thinking principles: understanding customer or end-user needs, involving employees whose work will be restructured, treating designs as experimental “first drafts”, considering multiple alternatives, and explicitly considering cognitive technology capabilities in the design process. Most AI projects are also suited to iterative, agile approaches to development.
Step 4. Scale Up
Many organizations have successfully launched AI pilots, but they haven’t had as much success rolling them out organization-wide. To achieve their goals, companies need detailed plans for scaling up, which requires collaboration between technology experts and owners of the business process being automated. Because AI technologies typically support individual tasks rather than entire processes, scale-up almost always requires integration with existing systems and processes. Indeed, research found that such integration was the greatest challenge faced in AI initiatives.
Companies should begin the scaling-up process by considering whether the required integration is even possible or feasible. If the application depends on special technology that is difficult to source, for example, that will limit scale-up. Make sure your business process owners discuss scaling considerations with the IT organization before or during the pilot phase.