AI technologies are quickly maturing as a viable means of enabling and supporting essential business functions. But creating business value from artificial intelligence requires a thoughtful approach that balances people, processes and technology.
AI comes in many forms: machine learning, deep learning, predictive analytics, natural language processing, computer vision and automation. Companies must start with a solid foundation and realistic view to determine the competitive advantages an AI implementation can bring to their business strategy and planning.
According to John Carey, managing director at business management consultancy AArete, "artificial intelligence encompasses many things. And there's a lot of hyperbole and, in some cases, exaggeration about how intelligent it really is."
What advantages can businesses gain from adopting AI?
Recent cutting-edge developments in generative AI, such as ChatGPT and Dall-E image generation tools, have demonstrated the significant effect of AI systems on the corporate world. According to a Rackspace Technology 2023 survey, AI and machine learning are vital to business strategies. Out of the 1,400-plus IT decision-makers surveyed, 69% consider AI/ML a top priority, marking a 15% rise from the previous year. Some of the many benefits that businesses can gain by adopting AI include the following:
- Improved accuracy and efficiency in decision-making processes.
- Increased automation and resulting productivity in business operations.
- Enhanced customer service experience through personalized recommendations and interactions through chatbots and intelligent agents.
- Enhanced data analysis and insights to inform business strategies.
- Improved risk management and fraud detection.
- Cost savings as a result of process automation and optimization.
- Enhanced competitiveness and differentiation in the marketplace.
- Advanced innovation and the ability to create new products and services.
- Scalability and efficient management of large amounts of data.
- An opportunity to venture into new markets with unique AI options.
AI implementation prerequisites
The successful implementation of AI in business can be challenging. But a detailed understanding of certain factors and conditions prior to execution can considerably enhance the result:
- Labeling data. Data labeling is a crucial step in the pre-processing pipeline for machine learning and model training. It entails organizing the data in a way that gives it context and significance. Businesses should assess whether they have a data-driven culture within their operations and evaluate whether they have access to enough data to support the deployment of AI/ML efforts.
- Strong data pipeline. To ensure that data is combined from all the different sources for rapid data analysis and business insights, organizations should strive to build a solid data pipeline. A strong data pipeline also offers reliable data quality.
- The right AI model. The success of any AI implementation can be seriously hampered by the choice of AI model a business uses. A large volume of data combined with an inadequate AI model could produce a large amount of training data, which can present challenges for the AI project. Therefore, selecting the right AI model is imperative before implementing an AI strategy.
10 steps to AI implementation
Early implementation of AI isn't necessarily a perfect science and might need to be experimental at first -- beginning with a hypothesis, followed by testing and measuring results. Early ideas will likely be flawed, so an exploratory approach to deploying AI that's taken incrementally is likely to produce better results than a big bang approach.
The following 10 steps can help organizations ensure a successful AI implementation in the enterprise:
1. Build data fluency
Practical conversations about AI require a basic understanding of how data powers the entire process. "Data fluency is a real and challenging barrier -- more than tools or technology combined," said Penny Wand, technology director at IT consultancy West Monroe. Results from the "Forrester Wave: Specialized Insights Service Providers, Q2 2020" showed that 90% of data and analytics decision-makers surveyed saw increased use of data insights as a business priority, yet 91% admitted that using those insights was a challenge for their organizations.
Forrester Research further reported that the gap between recognizing the importance of insights and actually applying them is largely due to a lack of the advanced analytics skills necessary to drive business outcomes. "Executive understanding and support," Wand noted, "will be required to understand this maturation process and drive sustained change."
2. Define your primary business drivers for AI
"To successfully implement AI, it's critical to learn what others are doing inside and outside your industry to spark interest and inspire action," Wand explained. When devising an AI implementation, identify top use cases, and assess their value and feasibility. In addition, consider your influencers and who should become champions of the project, identify external data sources, determine how you might monetize your data externally, and create a backlog to ensure the project's momentum is maintained.
3. Identify areas of opportunity
Focus on business areas with high variability and significant payoff, said Suketu Gandhi, a partner at digital transformation consultancy Kearney. Teams comprising business stakeholders who have technology and data expertise should use metrics to measure the effect of an AI implementation on the organization and its people.
4. Evaluate your internal capabilities
Once use cases are identified and prioritized, business teams need to map out how these applications align with their company's existing technology and human resources. Education and training can help bridge the technical skills gap internally while corporate partners can facilitate on-the-job training. Meanwhile, outside expertise could accelerate promising AI applications.
5. Identify suitable candidates
It's important to narrow a broad opportunity to a practical AI deployment -- for example, invoice matching, IoT-based facial recognition, predictive maintenance on legacy systems, or customer buying habits. "Be experimental," Carey said, "and include as many people [in the process] as you can."
6. Pilot an AI project
To turn a candidate for AI implementation into an actual project, Gandhi believes a team of AI, data and business process experts is needed to gather data, develop AI algorithms, deploy scientifically controlled releases, and measure influence and risk.
7. Establish a baseline understanding
The successes and failures of early AI projects can help increase understanding across the entire company. "Ensure you keep the humans in the loop to build trust and engage your business and process experts with your data scientists," Wand said. Recognize that the path to AI starts with understanding the data and good old-fashioned rearview mirror reporting to establish a baseline of understanding. Once a baseline is established, it's easier to see how the actual AI deployment proves or disproves the initial hypothesis.
8. Scale incrementally
The overall process of creating momentum for an AI deployment begins with achieving small victories, Carey reasoned. Incremental wins can build confidence across the organization and inspire more stakeholders to pursue similar AI implementation experiments from a stronger, more established baseline. "Adjust algorithms and business processes for scaled release," Gandhi suggested. "Embed [them] into normal business and technical operations."
9. Bring overall AI capabilities to maturity
As AI projects scale, business teams need to improve the overall lifecycle of AI development, testing and deployment. To ensure sustained success, Wand offers three core practices for maturing overall project capabilities:
- Build a modern data platform that streamlines how to collect, store and structure data for reporting and analytical insights based on data source value and desired key performance indicators for businesses.
- Develop an organizational design that establishes business priorities and supports agile development of data governance and modern data platforms to drive business goals and decision-making.
- Create and build the overall management, ownership, processes and technology necessary to manage critical data elements focused on customers, suppliers and members.
10. Continuously improve AI models and processes
Once the overall system is in place, business teams need to identify opportunities for continuous improvement in AI models and processes. AI models can degrade over time or in response to rapid changes caused by disruptions such as the COVID-19 pandemic. Teams also need to monitor feedback and resistance to an AI deployment from employees, customers and partners.
Common AI implementation mistakes
Businesses that neglect to take those steps when deploying AI risk committing various mistakes:
- Adopting too many tools simultaneously.
- Unclear business objectives.
- Ignoring privacy and security concerns that come with AI.
- Not collaborating with the right partners.
- Not involving the stakeholders and the affected employees in the decision-making process.
- Over-relying on the black box models of AI.
- Not performing enough testing and validation.
Coexisting with machines
Penny WandTechnology director, West Monroe
During each step of the AI implementation process, problems will arise. "The harder challenges are the human ones, which has always been the case with technology," Wand said.
A steering committee vested in the outcome and representing the firm's primary functional areas should be established, she added. Instituting organizational change management techniques to encourage data literacy and trust among stakeholders can go a long way toward overcoming human challenges.
"AI capability can only mature as fast as your overall data management maturity," Wand advised, "so create and execute a roadmap to move these capabilities in parallel."