Here is one observation I have made: 2020 has shown has that businesses need to deploy AI in order to improve processes and become competitive in the market. As the COVID-19 pandemic hit, AI acceleration increased as consumers and enterprises adapted to this change.

Most notably, companies adopted AI more than ever as new challenges including social distancing and lock downs became widespread. The rise of the intelligent enterprise makes this a better time for AI implementation in organizations.

Companies that ignore or undermine AI solutions¹ risk falling behind in the market and with the customer experience becoming commonplace, enterprises should embrace this technology to continue surviving.

Unlike the 90s, where disruptions in the market were slow, the modern digital transformation requires businesses who understand AI, machine learning, natural language processing and many more technologies including cloud computing.

Many customers ask this question: How can they realize business value from artificial intelligence initiatives after proof of concept phase? Enterprises are excited at the potential of AI, and some even create a POC as a first step.

However, some are held back by lack of clarity on the return on investment. The same question is prevalent among data science teams² that create machine-learning models that are under-utilized by their organizations.

Overview

Artificial Intelligence is playing an important role in business. Every year, executives implement AI-based solutions across both products and processes. Nevertheless, do you know how they do it? The second question becomes: If you were to try the same, would you know how to achieve the best results?

Artificial intelligence³ is a team sport that requires strong collaboration between business analysts, data engineers, data scientists and #machinelearning engineers. It is hard to deny, AI is the future of business and the majority of companies will have to implement it to stay competitive.

The following are action steps successful companies use to scale AI:

1. Encouraging Early Stage Adoption

There is a common trend for companies to adopt AI when it is late and this leads to bad outcomes according to a recent Garner report among top AI executive in USA and Europe.

One of the biggest challenges among organizations is getting front-line employees to use AI insights⁴ in their daily decision-making. The same Gartner research shows the pervasiveness of this issue: just 36% of respondents from high-performing companies, and only eight percent of all others, say their front-line employees use AI insights in real time to enable daily decision-making.

To make progress here, companies will need to redesign #workflows so it is easy for employees to incorporate AI insights into their day-to-day activities. They will also need to empower front-line workers to make data-driven decisions, rather than having to seek their manager’s approval.

Leaders should also set in motion a broad set of change management activities to encourage and incentivize workers to use the new tools. For example, the CEO of a retail conglomerate should energize participation in their AI transformation by widely publicizing successes across the company and promoting top talent into new roles based on their efforts to develop new AI tools.

2. Data Culture and Organizational Practices

Unlike others who do not adopt AI, successful enterprises have a clear data strategy for AI and well-defined governance processes for key data-related decisions⁵.

One bank found that improving data management could generate up to $2 billion in annual value, generated from improved cross-selling due to better data, capital savings from reduced operational risks that arise from poor #dataquality, and costs saving from teams.

At the same time, companies are most successful when they have a strong, centralized governance program for data quality and data management.

This includes instituting policies about what data can be used; how and where different data sets should be stored; how data quality⁶ is monitored and maintained; how data is used and tracked; and how metadata, including data definitions and data lineage, are documented. Successful companies also have a clear data ownership structure, with business units owning business-relevant data and accountable for the quality of the data they generate.

3. Supporting AI Champions

Here is the truth: Empowering #datascientists with the right tools can enable them to seamlessly collaborate, fluidly move across the organization to fill talent gaps and better manage enterprise risk.

Seventy-six percent of respondents at high-performing companies say their organizations have standard AI toolsets, compared to only 18%. Experts need access to state-of-the-art workbenches and tools for managing, structuring and simulating data. They should also have repeatable methodologies and protocols for bringing new use cases to production.

Companies that do this well follow a structured playbook through every phase of #artificialintelligence development: identifying opportunities, validating the value at stake, evaluating feasibility and tracking model performance. AI high performers are nearly four times more likely than others to know how frequently their AI models need updating.

4. AI Coaching and Training Programs

One mistake most companies make when deploying AI is focusing too much on the technical aspects than looking at the cultural factor. While there are numerous learning programs on the market today, in-house AI training programs are emerging as a core element for the broad-based learning necessary and critical roles like translators.

Walmart is a good example here where the retail giant set up a comprehensive training program to prepare its employees for AI transformation. Reskilling programs come in handy as management can assist employees learn new skills and prepare them in the wake of #digitaltransformation⁷. Many companies today are adopting upskilling and reskilling in the quest to prepare their employees for technological transformation.

5. Collaborative Models and AI Implementation

AI high performers are almost three times more likely than others to have AI and business experts work together to solve business problems, proving that interdisciplinary teams with diverse perspectives are crucial in AI development.

These teams ensure that AI efforts reflect organizational priorities, address end user needs, and achieve value faster. In our research, cross-functional AI execution teams were typically embedded in the business unit for the duration of the project and include a project owner, translator, data scientists⁸, data engineers and business analysts.

At Amazon, the use of interdisciplinary teams enable AI experts gain a deeper understanding of how product buyers perform. With these insights, they have built a more effective model for recommending product placement in stores. Gross margins have increased between four and seven percent in product categories where the tool is applied.

You can Scale AI in your Organization Today

Organizations exploring AI and machine learning, are confronted with the question of how to realize the business potential of these powerful technologies. Integrating AI into any organization is serious work. It takes in-depth knowledge, time and a dedication to accuracy.

Moreover, to implement AI successfully, do not just follow the trends: instead, focus on how AI can add value to your particular business and determine where it is needed the most. Then, with the support and experience of a domain specialist, you can put your ideas to work and create long-term value using the demanding field that is artificial intelligence.

Works Cited

¹AI Solutions, ²Data Science Teams, ³Artificial intelligence, ⁴AI Insights, ⁵Data-Related Decisions, ⁶Data Quality, ⁷Digital Transformation, ⁸Data Scientists