Artificial Intelligence (AI) has come of age. Technology is now transforming industries and reshaping the business landscape big time. 

But very few companies realise their AI ambitions or expectations. In most companies, the gap between ambition and execution remains large. While 85% of business executives believe AI will deliver a competitive advantage for their companies, only 30% have an AI strategy. 

Artificial Intelligence is not a magic bullet. Success depends on strong commitment, robust preparation, and continuous learning. Also, decision-makers need a clear idea of what is and is not possible using AI.

Identify Clear Use Cases

Many enterprises go overboard with AI and pay the price. AI requires considerable investment in money, time, and energy. Applying AI to everything at once is a sure recipe for disaster. 

Successful AI implementation depends on identifying opportunities where AI can create tangible value. Shortlist business opportunities where AI implementation makes the best impact. Currently, the best use cases for AI include:

• Automating repetitive, time-consuming, and error-prone processes. One example is scheduling field service technicians. Field service scheduling requires grappling with dynamic and multiple variables. Automation converts a near-full-time activity to a task that takes just minutes.
• Enhancing customer experience by predicting market trends or personalising offerings.
• Improving the quality of enterprise decision-making. AI is especially helpful in uncovering hidden insights within data. But at the same time, entrusting AI to make decisions is dangerous. AI makes cold decisions without considering the social or emotional consequences.
• Generative AI applications for analysing marketing trends, creating content, coding, driving innovation, and more.

A clear vision and defined use cases keep the enterprise AI journey focused and ensure AI investments fuel the core enterprise needs. 

Gather the Right Tools

AI is resource-intensive. Enterprises must commit a sizable sum to create and upgrade their data infrastructure. Most of the investment is to make the enterprise infrastructure compatible with AI-based solutions. 

Just about all AI applications require huge computing resources. The success of AI initiatives depends on the ability of data systems to store, process, and analyse data in huge volumes. As such, topping the list of infrastructure requirements for AI is a scalable cloud ecosystem for AI workloads. Next are data lakes and edge processing capabilities. Many of the latest AI tools also need natural language processing tools. 

Developing AI solutions and models from the ground up is hard work and not viable for most small and medium enterprises. A better option is to leverage ready AI solutions and pre-trained models first. Developing in-house models, if needed, can take place at the next stage after the enterprise attains AI maturity. Such an approach minimises the development costs and also accelerates the AI journey.

The best AI approach is to start small, focusing on specific use cases with tangible goals. Launch pilot projects to learn, identify challenges, and adapt approaches before scaling up.

Fuel the AI Engine with Data

The efficacy of AI engines in enterprise settings depends on the availability and quality of data. Algorithms become intelligent only when trained on large company-specific data. Enterprises need to:

• Ensure data availability. AI is only as effective as the data available. AI algorithms require data in sufficient volumes. Lesser data volumes can distort the learning from projecting exceptions as major events.
• Integrate data from various sources seamlessly. Data fragmentation, silos, and outdated information hinder the AI algorithm’s learning ability. Even if an enterprise owns the data it needs, it may lie fragmented across multiple systems, rendering access difficult for the AI algorithms.
• Set up systems to gather relevant training data upfront and integrate insights from data collected over time to the original training data. An example is customer data. Customer preferences keep changing, and the original training data will become obsolete.
• Invest in data hygiene. Investment in data hygiene has to precede investment in core AI technologies. Set up systems to access clean, complete, and relevant data.
• Ensure bias-free data. Data sets that contain bias, leading to discriminatory outcomes, are a disaster for AI since the algorithms amplify and extrapolate such bias. Bias-free data depends on responsible data sourcing and rigorous testing. 
• Implement robust data governance. Clear-cut data accessibility rules ensure that the AI algorithms access the right and relevant data. It also pre-empts issues such as the algorithms accessing restricted or unauthorised data, which can lead to copyright infringement and expensive lawsuits.

Businesses become AI-ready when they develop a comprehensive data-collection process. Enterprises need well-developed systems to ensure data availability and analyze data while it is still current.

Ensure Access to Talent

AI tools are becoming increasingly accessible. But successful AI implementation requires access to talent, which is hard to come by. 

Building and maintaining AI models requires specialised skills. But, skill development has not kept pace with technological advances. Data analysis, machine learning, and programming skills are at a premium. Data scientists and engineers with such expertise are in short supply. 

There is also a short supply of domain experts or leaders with deep industry knowledge. 

AI success depends on such leaders who can collaborate with data scientists to roll out practical and workable solutions. Most costly AI misfires are due to ineffective tech and business collaboration. 

Businesses become AI-ready only when they can source the right talent. An alternative is to develop the competencies of internal staff.

Set up AI-Compatible Systems 

The clear action plan, infrastructure, and talent need backing up with AI-compatible systems.

A shared vision is critical for the success of AI initiatives. Enterprise AI success depends on the support and cooperation of rank-and-file employees. AI involves change and will invariably attract resistance to change. Preparing employees for AI integration and addressing potential concerns minimise resistance.

A related requirement is a culture of learning. Invest in familiarising and training employees for AI. Articulate in clear-cut terms how employees can benefit from the new technology. Make the staff competent in digital and analytical skills. Often, the resistance and hostility are due to unknown factors.

Successful AI implementations also require trust. Robust data security protocols and a culture of open communications build trust. But sustaining trust in the AI environment control over the algorithms. Many enterprises cannot explain how their AI models arrive at decisions. Clarifying the “how” in clear-cut terms builds confidence in AI recommendations.

An underestimated requirement is effective risk management and compliance protocols. It is common for AI models to introduce biases or errors with legal or regulatory implications. Regular audits of AI models and transparent decision-making processes mitigate such risks.

AI adoption is rising. But there is still a lot of ground to cover. Only one in five companies have incorporated AI into some processes, and only one in 20 has incorporated AI extensively. 

Conclusion

Enterprises must assess their data readiness upfront. They must define their AI vision, build suitable systems, and gather the right team before plunging into their AI journey. Solutions such as Informatica help enterprises define and articulate their AI vision. The AI-powered Intelligent Data Management Cloud, for instance, democratize data across the enterprise. Informatica’s range of solutions covers all bases and helps enterprises unlock the power of data to drive competitive advantage.