10 Steps To Achieve AI Implementation In Your Business

Rapidly maturing as a viable means of enabling and supporting essential business functions, AI technologies are gaining widespread acceptance. To generate business value from artificial intelligence, however, requires a strategic approach that strikes a balance between people, processes, and technology.

AI is available in a variety of forms, including machine learning, deep learning, predictive analytics, natural language processing, computer vision, and automation. To determine the competitive advantages that an AI implementation can bring to their business strategy and planning, companies must first establish a solid foundation and adopt a realistic perspective.

John Carey, managing director of the business management consulting firm AArete, stated, “Artificial intelligence encompasses many things, and there is a great deal of exaggeration and in some cases hyperbole about how intelligent it truly is.”

Early implementation of AI is not necessarily a perfect science and may require initial experimentation — beginning with a hypothesis, followed by testing, and concluding with the evaluation of results. An exploratory, incremental approach to deploying AI is more likely to yield positive outcomes than a “big bang” mentality, as initial concepts are likely to be flawed. To avoid failure, these 10 steps can help ensure a successful AI implementation in your enterprise.

1. Acquire Data Acuity

Practical AI discussions require a fundamental understanding of how data drives the entire process. “Data fluency is a real and difficult barrier — more so than tools and technology combined,” said Penny Wand, technology director at IT consulting firm West Monroe. In a report titled 2020, Forrester Research found that 90 percent of data and analytics decision-makers surveyed view increased use of data insights as a business priority, while 91 percent acknowledged that utilising these insights is a challenge for their organisations. According to Forrester, the gap between recognising the importance of insights and applying them is largely attributable to a lack of advanced analytics skills required to drive business outcomes. “Executive understanding and support will be required to comprehend this maturation process and effect lasting change,” Wand stated.

2. Define Your Primary Business Drivers For AI

“In order to successfully implement AI, it is crucial to understand what others are doing within and outside of your industry in order to generate interest and motivate action,” Wand explained. Identify top use cases and evaluate their value and feasibility when developing an AI implementation. In addition, consider your influencers and who should become project champions, identify external data sources, determine how you could externally monetize your own data, and create a backlog to maintain the momentum of the project.

3. Determine Areas Of Potential

Suketu Gandhi, a partner at Kearney and an expert in digital transformation, recommended focusing on business areas with high variability and significant payoff. Teams composed of business stakeholders with expertise in technology and data should employ metrics to assess the impact of an AI implementation on the organisation and its people.

4. Evaluate Your Internal Capabilities

After use cases have been identified and prioritised, business teams must map out how these applications align with the organization’s existing technology and personnel. Internally, education and training may help close the gap in technical skills, while corporate partners can facilitate on-the-job training. In the interim, external expertise could speed up promising AI applications.

5. Identify Suitable Candidates

It is crucial to narrow a broad opportunity down to a practical AI deployment, such as invoice matching, IoT-based facial recognition, predictive maintenance on legacy systems, or customer purchasing habits. “Be experimental,” Carey advised, “and involve as many people as possible.”

6. Pilot An AI Project

Gandhi believes a team of AI, data, and business process experts is required to collect data, develop algorithms, deploy scientifically controlled releases, and measure impact and risk in order to transform a candidate for AI implementation into an actual project.

7. Establish A Baseline Of Comprehension

The successes and failures of early AI projects can increase the company’s overall comprehension. “Ensure that humans are kept in the loop in order to build trust, and involve your business and process experts with your data scientists,” Wand advised. Recognize that the path to AI begins with data comprehension and traditional rearview mirror reporting to establish a baseline of comprehension. Once a baseline is established, it is easier to determine whether the actual deployment of AI supports or refutes the initial hypothesis.

8. Scale Incrementally

Carey reasoned that the overall process of generating momentum for an AI deployment begins with achieving small victories. Incremental victories can help build confidence throughout the organisation and motivate more stakeholders to pursue similar AI implementation experiments from a stronger, more established starting point. Gandhi suggested modifying algorithms and business processes for phased release. “Incorporate [them] into standard business and technical procedures.”

9. Bring AI Capabilities To Full Maturity

As AI projects scale, business teams must enhance the development, testing, and deployment phases of the AI lifecycle. Wand offers three core practises for maturing overall project capabilities in order to ensure sustained success:

• Construct a modern data platform that streamlines the collection, storage, and organisation of data for reporting and analytical insights based on the value of the data source and the businesses’ desired key performance indicators.
• Develop an organisational structure that establishes business priorities and facilitates the agile development of data governance and contemporary data platforms in order to drive business objectives and decision-making.
• Create and implement the management, ownership, processes, and technology required to manage vital customer, supplier, and member data elements.

10. Continuously Improve AI Model And Process Implementations

Once the overall system has been implemented, business teams must identify opportunities for continuous AI model and process improvement. Models of artificial intelligence can degrade over time or in response to abrupt changes caused by disruptions such as the COVID-19 pandemic. Teams must also track employee, customer, and partner responses and resistance to an AI deployment.

Existing Alongside Machines

Problems will arise at each stage of the AI implementation process. As has always been the case with technology, Wand stated, “the most difficult obstacles to overcome are human ones.” She added that a steering committee with vested interest in the outcome and primary functional areas of the organisation should be established. The implementation of organisational change management techniques to promote data literacy and trust among stakeholders can go a long way toward overcoming “human” obstacles.

“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.”