Revisiting Cultural Norms May be Key to Adopting New Technologies
February 10, 2023 – As technology develops, business must develop with it. Companies that want to keep up with society must find ways to effectively integrate new technologies that will benefit them and allow them to continue to grow. AI technology is relatively new to both business and the technology field. Successful adoption of artificial intelligence and machine learning technologies beyond bleeding-edge tech firms is unique and may not have as much to do with the technology itself as one might believe, says a new report from ZRG Partners.
In addition to the obvious technological prowess required, companies must examine—and possibly change—their cultural norms. “In order to fully take advantage of AI, a company must address the changes they need to make in management styles,” said authors Lisa Hooker and Rahul Kapur. “Management needs to be aware that they cannot micromanage the process. Trust is essential in AI integration. Company leaders need to trust the system, and their teams.”
This ties into another struggle which is that AI integration does not yield immediate results. “AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks and relies heavily on deep learning and natural language processing,” said the report. “Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns. Advanced algorithms are being developed and combined in new ways to analyze more data faster and on many levels.”
This intelligent processing is key to identifying and predicting scenarios/outputs and takes time. Realistic expectations must be set and communicated to avoid the making premature adjustments, which can be detrimental. “AI systems have a give-and-take relationship with the people who operate them,” said ZRG Partners. “Having only one team dictate necessary changes for integration will stunt the growth of the AI at the core of the implementation.”
Trial and Error
For a company to best prepare for full integration, says the study, management needs to allow room for trial and error. The teams setting up AI systems will make mistakes, as will the system itself. “This is part of the process, and an important part. Every company is different, and so are their customers and employees,” said ZRG Partners. “AI works with these differences to create a comprehensive understanding of everyone involved in a company’s process. This is messy work and takes time and corrections. We expect tech to automatically adapt to our needs, and AI is not as simple as that.”
AI adoption cannot grow out of only one progressive department. For AI to be integrated successfully, all departments need to be involved. “AI’s purpose is to recognize patterns in human behavior and build an understanding of the consumer and/or company’s method,” said the report. “Because of this, diversity is critical to a properly functioning AI system, and a siloed approach will not provide that. Historically, this has been a major problem with AI effectiveness.”
Lisa Hooker, managing director and technology practice leader, is located in the Austin, TX office of ZRG Partners and is a member of the technology and board practices. She is also an active member of the firm’s private equity practice and the diversity, equity, and inclusion practice. She brings a career spanning more than 20 years of executive search consulting and has delivered board and leadership projects for Fortune 500, mid-cap, and SMEs as well as private equity, pre-IPO and venture-backed clients in the technology sector. Ms. Hooker serves as an advisor to the C-suite and boards of directors on topics including CEO succession, board efficiency, director onboarding, and leadership assessment and development.
The field itself lacks diversity; black researchers represent less than 12 percent of the U.S. AI workforce, and women make up less than 15 percent. “This makes it almost impossible for the system to be inclusive,” said ZRG Partners. “If the data scientists have inherent biases, the system will too. Nobody is implying that data scientists as a group are personally bigoted. That said, the occurrence problems like misclassification of non-white individuals through image recognition, or the radicalization of chatbots, or the failure of technologies to recognize darker skin tones must be addressed. There needs to be focused energy in developing diversity in a company’s AI system in order for it to actually serve its users.”
Adaptation and Evolution
Agile, responsive, and adaptive cultures are rare, yet they are key to a successful adoption of AI/ML, according to the report. Properly utilizing AI requires a change in thinking and practice on almost every level. “Management is arguably the most important level to adapt, as their managing style ripples out and guides their direct and indirect reports both,” said ZRG Partners. “It’s important for management to allow transparency, mistakes, and to focus on data-driven decisions.”
Cooperate to Integrate
A company must be prepared to make mistakes, which is why transparency is so important, says ZRG. The entire company must work together and trust both the system and themselves. According to some research, “only eight percent of firms engage in core practices that support widespread adoption. Most firms have run only ad hoc pilots or are applying AI in just a single business process.”
“This hesitation can stem from the fear of making those inevitable missteps,” said the report. “However, to not fully integrate across systems, departments, and culture is the best chance to actually lead to failure. Errors should not be a deterrent; rather, they are an indication to make an adjustment. It is vital a company trusts the data AI is providing, otherwise the system is useless.”
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A frequent problem management runs into during integration is their desire to make decisions based off their own knowledge base. “Management should exercise appropriate caution while still seeking to avoid internalized prejudices about human capacities,” said the report. “We are more comfortable believing that we can make a better ethical decision than a computer system. This is not necessarily the case. AI compiles such diverse data, from diverse groups (when applied properly), which provides greater objectivity and identifies underlying patterns of behavior.”
Artificial Intelligence and Company Culture
It is always difficult to generalize; thinking about adopting AI systems into your business certainly militates against generalities. The cultural preparations necessary for the successful adoption of AI will obviously vary depending on the specific company and industry, says ZRG Partners. There are, however, at least a few constants we can discern as well as some considerations for how AI can itself be part of the cultural assessment of any organization.
Rahul Kapur is a managing director at ZRG and is the global head of fintech. Based in New York, he has more than 30+ years of experience in fintech with a focus on banking, payments, lending, capital markets, and wealth management. His clients include top-tier financial institutions, enterprise financial technology providers, and emerging non-bank challengers.
“Think of how difficult it is to manage a change of email services from something like a Google-based system to a system like MS Outlook,” said the report. “Even something this seemingly simple cannot be accomplished without thoughtful and forward-looking change management practices. Adopting AI solutions will require even more forethought and will likely require greater cross-team collaboration in order to ensure an effective rollout. In the case of changing email servers, the onus falls almost solely on the IT department to ensure a smooth transition. In the adoption of AI solutions, the entire organization needs to be involved.”
Artificial Intelligence, Organic Talent
Unsurprisingly, AI/ML talent is in high demand, and that often leads to upward pressure on salary. IEEE has tracked software engineering salaries across the past few years. “2021 appears to have been a high-water mark across AI/ML and natural language processing salaries, but machine learning experts are still seeing approximately an 8.5 percent increase since 2018,” said ZRG Partners. “Natural language processing has seen approximately a 12.4 percent increase in the same timeframe, while artificial intelligence engineers are roughly flat, having peaked in 2021 along with the others. Of course, these numbers are related specifically to the engineers and not to their managers. It stands to reason, however, if the engineers who are overseen by a manager and or a C-suite executive have seen compensation increase, those managers and executives would at least have seen concomitant increases.”
It is clear that data remains a hot commodity. “Here we do not mean one’s personal data gathered by unscrupulous means from internet traffic,” said the authors. “Rather, we mean the use of data to orchestrate and operationalize the value of a company’s AI initiatives whether it comes from an unstructured or a traditional source. Managing that data certainly requires the right technology, but it also continues to require top talent as well.”
These trends are not limited to the software and technology industries, either. “The innovative disruptors are being targeted by traditionally non-innovative industries to ratchet up innovation themselves,” said the ZRG report. “Industries such as financial services or traditional manufacturing are all looking to disruptive technology companies for talent that can think outside of the traditional industry paradigms that have stifled innovation in the past.”
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Contributed by Scott A. Scanlon, Editor-in-Chief; Dale M. Zupsansky, Managing Editor; and Stephen Sawicki, Managing Editor – Hunt Scanlon Media