Artificial Intelligence and the Pandemic
Artificial Intelligence (AI) defines the 21st century, impacting and driving transformation across every business and industry. With the new norms thrust upon us due to the pandemic, businesses and industries have had to find ways to transform overnight. Previously, it may have taken several years for an organization to make an incremental change or improvement.
In 2020, these same organizations had to pivot within days to weeks during the pandemic's onset to continue staying relevant and in business. These unexpected disruptions forced companies to look to AI to accelerate their migration to digital and experience-based business models.
Forward-thinking leaders are adapting to digital and experience-based models sought by their internal and external stakeholders. Organizations are investing in AI adoption to create customized experiences, targeted products, and intelligent business processes. AI cannot be excluded from today’s business discussions because it is no longer hype. It is here to stay. For organizations actively investing and incorporating it as part of their business strategy, the rewards are evident.
A Business Perspective on AI
AI offers improved approaches to examining various data sets to optimize plans and processes. These improvements can be used to manage sales, marketing, employees, and systems, to name a few. AI features can provide the premise for a higher automation level to enhance productivity for processes and the workforce.
Every use case needs to be properly explored to ensure AI efforts and associated applications achieve their desired business results. Each AI application needs various tools and algorithms to achieve efficiency gains and successfully automate business processes. Automation is the only way an AI application will give you the vital information you depend upon for accurate and timely decision-making.
The disruption is real. AI is sweeping across the business world, but the trail is long and winding. As such, the true test for an organization is to quickly devise a strategy for creating value from AI investments and identifying the landmines and risk management strategies.
Stepping Stones to AI Success
First, no general-purpose AI application exists. Each organization’s AI journey is different. Choosing the right next-level AI application is a fundamental step towards propelling the organization forward on its AI maturity curve. So, how can we achieve this? These seven stepping stones will enable you to make well-informed plans for AI adoption:
- Get Familiar with AI
AI is a continuous process of combining tools, techniques, and domain knowledge to solve business problems and streamline processes to deliver personalized experiences with efficiency and scale. As such, the primary step for progressing AI in a company is to get familiar with it by acquiring the right skills.
Briefly, AI is:
- The art of applying machines to make intelligent, human-like decisions relying on evidence.
- Blending decisions with actions and data to automate human tasks and streamline end-to-end processes.
- Using machines to detect unique human attributes like standard dialects, speech patterns, and pictures.
- Simulating human insight by analyzing and acting on machine- and application-produced data.
- Engage With AI Experts
To determine the most effective strategy for using AI in your business, use task analysis. The goal is to ensure that AI will reduce process complexity, minimize time to execute the process, and improve the end-user experience. Consequently, partnering with domain and AI talent is critical to prioritize AI business objectives, investments, and obstacles. The best partners are the ones who have embarked on implementing AI for their functional processes.
These partners should be experienced in visioning, project planning, program management, process re-engineering, training, communications, end-user support, adoption monitoring, and ensuring the post-go-live success of AI applications. As a result, they are unmatched in comparison to break through perceived organizational, economic, and technological barriers that threaten to slow down AI progress. Leading companies often seek professional assistance for strategy, program management, and implementation of AI initiatives. All leaders should consider it to ensure a successful outcome.
- Identify What Problems You Want AI to Solve
In addition to the use case, understand what the previous challenges in solving this problem were to grasp the obstacles the organization may have to overcome in using AI.
Investigate various ways your organization wants to use AI to improve your overall business. Be open to new thoughts and different approaches to solving long-running problems. AI implementations prompt companies to rethink the rationale for existing processes and see if they still hold relevance when combined with AI.
You may discover an AI-led process can eliminate redundant tasks. The key is to rethink the end-to-end process with the user experience in mind, leveraging AI's capabilities and how it can fit products and services to boost efficiency.
The use case should demonstrate business value where AI can help eliminate an existing problem, improve process throughput, or automate service delivery with demonstrable results.
- Build a Business Case to Successfully Deploy AI
- Think of areas such as predictive maintenance, targeted sales and marketing, logistics optimization, procurement and supply chain personalization, and unified customer service experiences as areas where AI can bring value.
- Pick the right problems to solve, keeping in mind the AI application's cost and efforts to the long-term efficiency and competitive advantage it can bring.
- Bring aboard critical decision-makers in the organization to build a consensus and a shared vision for the long-term role AI technologies will play in the organization.
- Experiment. Start with a small project to measure AI applicability to a specific use case to deliver results.
- After an area of focus has been identified, collect data for training the AI algorithm. More data translates to a more accurate AI training model. Consequently, have enough investment available for data gathering efforts from various hotspots to develop a useful AI model.
- There will also be a need to corroborate that the collected data is not only unbiased but also rich in quality to determine data relevance. AI governance should be considered as a practice to eliminate biases and inefficiencies within the process cycle.
To scale an AI pilot, consider:
- Incubating them in innovation labs or AI technology centers of excellence
- Leveraging the innovation network of technology and consulting partners
Once the organization has experimented with a small project, evaluate the outcomes with an objective mindset. What is the feasibility of AI deployment at a broader scale? Can the organization scale AI to add a financial value through improved efficiencies? What is the financial muscle and appetite for change within the organization? Are there enough C-suite champions and investments necessary to accomplish full-blown AI implementations to reap the benefits?
Often, the evaluation stage is a tug of war. IT enthusiasts that want to push an AI initiative demonstrating its ability and cutting-edge results may find themselves up against skeptical process owners who are yet to understand how AI works.
For this reason, transparency is vital. Equally, it’s critical to have business domain experts with C-suite support participate as key stakeholders in the project and governance processes. Functional leaders must feel comfortable about broader AI adoption while understanding successes and flaws to manage the risk.
It is AI experts’ responsibility to explain to business executives the ins and outs of AI. AI activities must be assessed and co-driven by both business and technical leaders to deliver functional, easy-to-deploy winning AI initiatives.
- Gain Employee Trust and Support by Easing Concerns
Research indicates at least 60% of companies speculate most of their workers worry about AI's impact on potential job cuts, making employees apprehensive about working with new technologies or AI applications. These uncertainties fuel resistance to change and create a significant impediment to AI implementations.
Clear this hurdle by involving employees and communicate progress throughout the journey. To ease their concerns:
- Provide training and programs to help employees have the data literacy to understand AI dashboards, reports, and analytics. This will increase their comfort level with AI technology.
- Communicate the positive effect AI will have on the employee and end-user experiences. For example, demonstrate how AI will reduce the mundane and repetitive tasks to free staff up to focus on more high-level activities.
These strategies will grow your workforce’s level of confidence with AI. Remember, both employees and AI applications are vital if we want to achieve the intended goals. Neither is dispensable.
- Work With Domain Experts That Understand AI to Prepare Enterprise Data and Skills
Another component of AI is building or having a team of specialists that have domain expertise, understand AI, and are willing to drive the change and adoption. Even so, the biggest challenge facing AI implementations today is the lack of experts with the talent and skills necessary to scale. This explains why we need to continue supporting AI enthusiast domain experts to experiment further and expand AI.
Each business will need the relevant data to train and test AI systems. Insufficient or irrelevant data is likely to compromise AI accuracy and relevancy. Accordingly, having the right data and skills within the organization are necessary to fully capture AI’s benefits.
To progress AI, keep these considerations in mind:
- Discover: Craft the vision for what you want AI to achieve.
- Devise: Develop or select the right AI solution to experiment with a small-scale project.
- Deploy and Sustain: Broaden the scope of AI for continuous transformation.
When it comes to digital pursuits in enterprises, the next revolutionary frontier is here with AI. While some organizations are still wobbling from previous technological disruptions, a new one is taking shape and will alter how we operate. AI will help improve or replace existing business processes, reduce errors, and broaden the level of automation for better business outcomes. Leading businesses that have already put AI into practice are poised to generate benefits through enhanced relationships, more efficiency, and higher revenues.
Given AI's high capacity to handle and interpret data, the technology offers a range of opportunities to innovate business processes by augmenting their operational frameworks. AI can substantially increase automation, predict business outcomes, revamp customer management, and prevent fraud.
With the explosive data growth today, increased computer processing power, and strengthened AI technology foundations, the time is now to make AI a competitive advantage for any organization.