Technology is always in a state of flux. Each period has its new dominant technology, which drives business growth. The Internet, cloud, and mobility were all big new things that propelled growth in the past. Artificial Intelligence (AI) is the next big thing that drives business growth. In a recent EY survey, almost one out of four C-suite executives believe AI will have the largest positive impact on the progress of their enterprise over the next five years.

But AI adoption is not without its share of challenges. The primary obstacle is cost. 42% of C-suite executives consider budget limitations as the biggest obstacle standing in the way of AI-powered innovation.

The onus is on the CXO to spearhead AI-powered innovations. They have to draw up coherent, quantitative plans and convince the C-suite to loosen their purse strings for AI investments. Here is how CXO’s are living up to the task.

Taking Data-Based Predictions to the Next Level

The most popular use case of AI now is in data analysis. Business teams apply AI to analyze data and throw up suggestions. The AI-powered engines identify customers’ needs and preferences and enable personalized offerings.

Examples abound. Netflix customizes show listings for users. Twitter and Facebook customize the user’s feeds. Such intelligent recommendations throw up listings that sync with users’ preferences or amplify user taste.

The effectiveness of such processes depends on how well the business teams train the underlying algorithms. The quality of the recommendations depends on the ability of the algorithm to uncover patterns and predict desires. Done right, AI translates data into better customer experiences and more loyalty. It delivers a better bottom line for the business.

Today, most CXOs are busy training algorithms to improve recommendations. They have their task cut out to overcome data scarcity, or conversely, pick up the right data from the glut to train the algorithms. Successful CXOs:

1. Align AI objectives with the overall strategy of the enterprise.
2. Set up APIs, connectors, and other channels to collect data from all sources, including external sources.
3. Implement good data governance practices to ensure relevant, high-quality data for analytics.
4. Quantify the direct and indirect costs of the current data. For instance, they estimate the cost of accessing and cleansing data and compare it with the value of the desired outcomes.
5. Prioritize outcomes based on business impact, technical complexity, and other business considerations.
6. Establish clear implementation plans with timelines.

Only a few enterprises have the resources to train algorithms for extensive projects. CXOs overcome such limitations by starting with minor projects. They take an incremental approach. After training algorithms for small projects, they expand their scope to bigger things.

Focus on the Business Value

Successful CXOs do not focus on technology. They focus on business value. They seek to deliver business outcomes and use technology as an amplifier of the value offered to customers.

Until not too long ago, CXOs used AI to analyze the copious amounts of data at their disposal. They now go a step ahead and combine multiple algorithms with other technologies to unlock broader potential. Integration of AI with RPA, for instance, speeds up business processes. Integration of AI with NLP helps businesses analyze texts and decipher social trends fast.

CXOs use AI integrated with other technologies to:

• Understand their customers in-depth and enhance the customer experience. Using AI with internal company data does not deliver the correct answer in such situations. Reaching out to the customer requires a combination of external data on specific events, internal information about the products, and customers’ personal preferences. Equipped with these three datasets, CXOs train the AI algorithm to offer the best insight and deliver real value to the customer.
• Improve workflows to enhance workforce productivity and maximize efficiency. Applying AI to automate redundant tasks boosts efficiency. 
• Automate processes and overcome skill shortages. Automation spares human employees to concentrate on more critical tasks. The most popular use case is chatbots. The AI engine deciphers the customer’s query made in natural language and provides answers to user queries. AI-powered bots also take over tasks facing a shortage of human employees. 
• Manage infrastructure proactively. CXOs use AI to predict outages and ensure better service availability. Consider IoT sensors that emit data. AI systems track such data. When algorithms detect abnormal patterns, it triggers alerts that enable pre-emptive repairs.

Changing the Culture

AI growth requires a culture that supports innovation. CXOs have to support training in AI, Machine Learning, and related areas as the first step. But they cannot progress unless they also tackle the culture change. CXOs have to foster a culture conducive to innovation and manage resistance to change.

In enterprises with successful AI-powered projects running, CXOs become innovation champions. They:

• Integrate databases to end silos, promote transparency, and encourage open sharing of information. Innovation fails in an environment where the employee has to spend a major part of their time and effort to organize things.
• Forge effective connections across the board, and facilitate everyone to learn from each other. Successful CXOs enable reverse-mentoring for innovation. Reverse mentoring provides opportunities for seniors to learn the nuances of AI from junior upstarts.
• Encourage risk-taking and make sure failure does not cost the risk-taker career opportunities. Rather, offer incentives for accomplishments.
• Host innovation-focused events. Such events make innovation the accepted in-thing and spread awareness of how the initiatives benefit, and why it is not a threat.

Coping with COVID-19

According to a 2018 MIT Sloan Management Review study, 58% of companies believed AI will change their business models in a big way by 2023. But many companies did not progress beyond experiments, proof of concept, and prototypes. And most of these projects were about automating call centres and service desks.

The COVID-19 pandemic changed things. It made the future come suddenly. The shift to the digital-first mode in shopping habits and work forced enterprises to take up AI-based projects seriously. As a case in point, food producer Frito-Lay compressed its five-year plans for digital and data initiatives into six months and launched its e-commerce platform in just 30 days. The company deployed AI-powered analytics to predict shifts in demand and customer tastes, keep track of store openings, and so on, down to the zip code level.

During the pandemic and its aftermath, CXOs

• Scrambled to set up chatbots. Most chatbots have only a basic level of AI maturity. They redirect even the complex interaction with human employees. Still, chatbots helped enterprises cope with the increase in demand, many of whom were newcomers to the digital space. CXOs now strive to make chatbots more mature, and capable of handling more complex queries.
• Train algorithms to handle a wider use-case scenario and enormous volumes. Many companies deployed AI-powered automation to cope with demand surges. AI also helped businesses reduce workplace density and comply with social distancing mandates. 
• Use AI to resolve supply chain issues. AI-powered algorithms help enterprises predict and prepare for supply chain disruptions and delivery issues. These algorithms also helped in related issues such as fraud detection.

Here are some interesting innovation successes during the COVID-19 pandemic.

Many executives have a grand vision of AI, akin to science fiction robots. AI at the enterprise level is far less grandiose, but still offers tremendous value. Realistic CXOs put their head down and work diligently to make things better for their stakeholders. They will reap rich dividends in the coming years.