Navigating AI Adoption: 6 Tips for Business Leaders


Today’s business environment is driven by rapid advancements in technology, perhaps none more so than artificial intelligence. AI yields remarkable benefits, from enhancing efficiency and streamlining operations to directly engaging customers to unlock growth opportunities and achieve a competitive edge, that can transform organizations.

However, before business leaders dive straight into AI adoption, it’s critical to pause and consider a range of factors. As experts in developing and delivering AI and machine learning solutions, we’re sharing six key considerations for business leaders interested in embracing AI within their organizations.

6 Questions Business Leaders Should Consider Before Adopting AI

  1. Do we have good questions and good data?
  2. Will adopting AI achieve the business value we want it to?
  3. Have we considered positive and negative impacts across all stakeholders?
  4. Where can automation take the load off?
  5. Are we willing to accept the risks of AI?
  6. Is our organization ready to develop, support, and use AI in a safe and responsible manner?

Do we have good questions and good data?

Data science is the foundation of AI. This means that to leverage the power of AI in an organization, it’s necessary to have a good question, high-quality data, and (hopefully) a discernable pattern.

Business leaders often have good questions, but their organization’s data quality is insufficient. Patterns tend to shift over time regardless of data quality.

To truly succeed in AI adoption, a business needs an interoperable, robust, skillfully designed data management architecture that provides timely access to a breadth of high-quality, well-governed, clean data. This enables scalable AI/ML solutions and presents the opportunity to derive accurate, meaningful insights and predictions.

Businesses will have numerous considerations around model monitoring, data collection, and data quality. It’s essential for leaders to build their organization’s capability around data management and AI concurrently.

Will adopting AI achieve the business value we want it to?

Achieving business value is a critical consideration when adopting AI in an organization.

Before embarking on AI implementation, business leaders should know the exact outcome they aim to achieve through adoption, such as:

  • Supply chain optimization through enhanced visibility and improved demand forecasting
  • Enhanced customer experiences through improved personalization and chatbots to reduce customer service response times 
  • Improved efficiency through automating key business processes, allowing employees to dedicate their time to higher-value tasks 
  • Increased conversion rate through more relevant customer interactions and recommendations at the point of sale 
  • Improved decision-making capabilities through forecasting market trends and customer needs with predictive analytics 

The desired outcomes that business leaders establish may also vary depending on the area they implement AI capabilities in. For instance, the tasks of today’s knowledge workers vary considerably from day to day. Offering these workers assistance through Copilots and Generative AI is a significant opportunity.

AI opportunities in the sales function of a business look different – the sales process itself doesn’t vary too notably but the parties engaged in that process have a wide array of needs.

In this case, businesses can achieve value by using AI to differentiate the experience their customer has and automate recommendations based on the data input. As consumers, we see a perfect example of this when an online retailer recommends a product based on our previous browsing and purchasing behavior. Similar experiences can be extended to corporate sales, enabling sales organizations to anticipate their customers’ needs.

Successfully embracing AI requires significant time, money, and effort. Business leaders should identify exactly what their organization’s new AI capabilities need to achieve and develop a thoughtful strategy to ensure their organization reaches that outcome and realizes the return on their investment.

Have we considered positive and negative impacts across all stakeholders?

Adopting AI can yield significant benefits; however, it also poses significant risks to businesses, employees, and customers. It’s critical for business leaders to have effective AI Governance in place to provide oversight and controls that recognize and thoroughly consider all possible positive and negative impacts upfront and continue to monitor and maintain AIs while in production.

One such impact results from training AI algorithms on biased data. An organization’s data is based on what it’s done in the past and how an individual has tagged it. If AI algorithms are trained with biased data, businesses run the risk of automating that bias and scaling unfair, discriminatory outcomes. Sometimes this bias is introduced to deployed AIs by malicious actors looking to “retrain” your AI

Take the example of a financial institution automating credit authorization. Certain minorities and disadvantaged groups have historically been unfairly treated in regard to credit authorization. If technical teams train AI algorithms on this data, they invertedly bias the outcomes and scale the negative impact on these stakeholders.

Stakeholder impact analysis is a critical element of a Stage-Gate review prior to deploying an AI implementation. It is the ethical and moral thing to do, but also reduces the potential for financial and reputational damage to the company.

When a business delivers a bad experience – whether it’s an employee experience or a customer experience – someone is going to talk about it.

Word-of-mouth marketing drives a stunning $6 trillion of annual consumer spending. If a business leader doesn’t conduct impact analysis and consider all possible impacts of AI stakeholders, they risk not only conducting business in an unethical way, but losing customers and revenue when news of their mistake inevitably spreads.

In addition to customer backlash, regulators monitor social media to identify potential compliance issues that may result in fines.

Where can automation take the load off?

The automation opportunities concomitant with AI adoption can increase efficiency, reduce costs, and enable teams to shift their time to more valuable work.

When considering embracing AI, assess which tasks, processes, and areas could benefit most from automation and taking the load off workers.

The most repetitive and time-intensive tasks, such as answering basic customer inquiries, checking patients in, and entering data, present excellent opportunities for automation. At the same time, be mindful of which higher-value tasks necessitate human empathy and expertise. Hybrid approaches such as “human-in-the-loop” should also be considered where AI assists your people to be more effective and productive.

A thorough assessment and strategic planning ensures an organization achieves a balance of efficiency through automation while still realizing the value of human touch. As with any investment, leaders should understand the potential for future cost savings relative to the cost and identify those projects with the highest expected return on investment (ROI).

Are we willing to accept the risks of AI?

Artificial intelligence is not, and will never be, perfect. Prior to adopting AI, business leaders must assess the risks that accompany the use of AI in their organization and include the estimated probability and financial impacts in any ROI analysis.

In a noteworthy example of this, a federal judge fined two lawyers after they used ChatGPT to create documentation submitted in an aviation injury claim. As a result of using the generative AI platform, six of the legal cases listed to support their written arguments did not exist and were entirely fictitious.

This year has shown us that AI is exceptionally powerful, but also extremely talented at committing fraud and plagiarism. Fraudulent generative AI situations can have a high blowback, in some industries more than others (such as the legal example above).

Business leaders should consider, “If I put AI in there, how bad could it get?” They should also ask themselves, “When AI is imperfect, is that an acceptable risk?”

If an organization is not willing to accept the risk of AI and the very real possibility of it being wrong, they should either not proceed with adoption or should adopt hybrid “human-in-the-loop” models where your expert resources are aligned to deliver the best outcomes and check the AI’s assumptions.

Is our organization ready to develop, support, and use AI in a safe and responsible manner?

AI is a complex technology that is maturing rapidly and has the potential to significantly impact business, both positively and negatively. Businesses are clearly struggling to keep pace.

In this environment, it is important to pause and reflect on your own business’s readiness to adopt AI, not only from an operational and technological standpoint but also to be responsible for the outcomes for all affected stakeholders.

This means assessing the impacts on your customers, partners, employees, and financial stakeholders regarding their safety, privacy, security, and well-being (financial and otherwise) and ensuring that your AI is fair, transparent, reliable, and inclusive for these individuals and groups.

Your business is accountable for the results of the AI, like any product or service you provide, and you must be ready to take on that responsibility.

Leverage the Full Value of Your Organization’s Data

AI empowers your organization to maximize the value of its data, automate tedious processes, and increase efficiency. These capabilities can set your organization apart as an innovative leader in the industry.

AIM Consulting‘s expertise in machine learning and predictive and prescriptive analytics expertise helped numerous organizations transform their processes and achieve their most ambitious goals. Let’s build the future together.

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