Feature
Companies can capitalize on artificial intelligence in many ways, but having the right strategy — as with any other corporate endeavor — is key to success. A good plan for applying AI internally will go far in helping an organization maximize the increased productivity and competitive advantages AI can offer.
Reworked spoke with several workforce leaders on the five ways organizations should address AI, from strategy and tools, to staffing, skills and budget.
1. AI Strategy
Jeremiah Stone, CTO at SnapLogic, said any company that deals with data and is not implementing a generative AI strategy to automate processes and improve productivity risks “being leapfrogged by the competition” sooner than they can imagine.
While there are valid concerns around AI security, governance and trust, Stone said companies should nevertheless assume that their competitors are already experimenting with generative AI solutions and realizing benefits.
“Start with pragmatic, narrow use cases,” he said, “but have a plan to build those out to different business units across the enterprise.”
Clare Hart, CEO of Williams Lea, takes a slightly more cautious approach, agreeing that while companies should experiment and be open to the early failures that will come as familiarity is built, leaders should also make time to consider developing a strategy that includes defining the scope and objectives of the AI implementation.
Olga Beregovaya, VP of AI and machine translation at Smartling, agrees that companies need to identify the objectives of an AI-driven initiative for their strategy — and whether there are other means of achieving the same objectives within the existing ecosystem, making sure that AI is indeed essential for the task.
Ryan Elmore, head of data science at West Monroe, said a comprehensive AI strategy should include governance, guardrails, approach, success pillars, opportunity identification, case for change and ethical guidelines, because, he said, relying solely on tooling, people and skills will not be sufficient.
The foundation of AI strategy should be rooted in monetization and business value, with the primary goal of implementing AI solutions being to enhance productivity, increase earnings, reduce errors and boost creativity, Elmore said.
So, instead of a company treating AI as a one-off project, the technology should be woven into the fabric of the organization's long-term vision, said Raghu Ravinutala, co-founder and CEO of Yellow.ai. A corporate AI strategy should outline how the technology will drive innovation, improve efficiency and create value, as well as include elements on risk assessment, ethical adoption, compliance and governance, change management and the scope of future scalability.
Iliya Rybchin, partner at Elixirr, stressed the importance of taking a holistic approach. Companies with numerous strategies for every technology, business unit and function run the risk of misalignment in the organization and creating confusion about the ultimate goals for each business unit and function, he said.
Related Article: How to Identify the Right Workplace Processes to Automate
2. AI Tools
Generative AI tools can empower employees to be creative in their roles in addition to offering productivity and efficiency gains. But in Stone’s view, those gains have yet to be fully realized because the tools are so new that they, for now, land themselves more to experimentation by non-technical users.
But, he said, it’s through this experimentation that companies will unlock opportunities and learn to better manage risks.
While Hart agrees that companies should experiment with the technology, she said they should also invest in and leverage the tools that suit their business model’s specific needs. “Not every business needs an AI chatbot, but most should understand the impacts of a large language model,” she said.
Using the right AI tools for specific roles needed by the business can enhance efficiency, data analysis and decision-making.
Ravinutala agrees companies shouldn’t simply adopt AI tools because they are trending. Rather, they should choose AI tools that align with their strategy. He said companies can look into pre-built AI solutions for common tasks, such as data analytics and NLP. If they have a more unique business challenge, they can look for customized AI solutions.
But from a more technical perspective, Beregovaya said companies should make sure, when selecting AI tools, that there are no static AI models hard coded into them. Otherwise, they may not be able to capitalize on the evolution of the technology’s capabilities. Tool design needs to be modular to allow for adding or updating to the next best thing, she said.
AI itself may not provide a complete end-to-end solution for a holistic use case, as the full solution architecture is often “more process-driven than tool-driven,” said Elmore. Understanding the foundational steps of AI, ML and data science — and the iterative science behind them — is crucial for reducing errors and increasing accuracy in generative AI.
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3. AI Staffing
Stone said data engineers are often the best positioned to excel with generative AI work, due to the transferability of their skill sets to the new domain. Business leaders are also critical members of working teams to provide context and success criteria.
There’s already a shift in the market toward talent with AI fluency, said Hart. The roles reflect the “marriage between people and technology,” as companies increasingly seek applicants who can accelerate their adoption of AI capabilities.
For instance, Lea said, in the legal sector, the introduction of AI tools will reveal significant capitalization opportunities over the next two years, as firms work to integrate AI-fluent employees and analyze and adjust their hiring needs to compensate for the roles AI is augmenting.
Beregovaya said companies may want to consider having a team of data scientists responsible either for R&D or vetting external applications, an ML ops team responsible for deployment, a representative from the legal team responsible for reviewing AI provider agreements, as well as strong representation from the IT team for infosec clearance. There also needs to be an executive who’s accountable for the overall AI initiative implementation.
She said it is also crucial to put in place guardrails on access levels to AI applications and have a governing body that controls the centralization of AI efforts.
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AI projects have the potential to enhance collaboration by involving multidisciplinary teams and integrating new data sources, resulting in a “flywheel effect that uncovers additional value drivers,” said Elmore. There may eventually be a tool that supports project staffing by using staff availability and skill sets to determine the optimal team composition for specific business problems. However, he said, data accuracy and availability of up-to-date skills matrices could pose challenges to such a tool.
Ravinutala said because of the level of complexity involved with using generative AI capabilities today, companies would be wise to consult and hire AI experts, such as ML engineers, data scientists and AI researchers, who possess the technical skills to oversee AI initiatives. Establishing cross-functional AI teams with both data professionals and topic experts is also essential.
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4. AI Skills
The technology is in its infancy, and the skills needed to fully capture its capabilities are still relatively unknown. Stone said for that reason, companies need to consider upskilling employees across the various facets of AI and paying close attention to those already using generative AI tools to monitor developing opportunities and best practices.
Rather than banning shadow AI, he said, companies should learn how it’s being used in their organization, vet which portions can be implemented and roll out safe and sanctioned hands-on experimentation and learning.
“It’s critical for every organization to ensure its employees have the education and support required to succeed with generative AI," Stone said.
Fostering a culture of continuous learning and development will acclimate employees to AI, helping boost their knowledge to become more skilled with AI tools, said Hart. “Upskilling the workforce will be a critical, ongoing necessity to stay competitive as the true potential of AI is revealed.”
Ravinutala agreed that it is imperative for companies to invest in AI training and cultivate in-house proficiency in ML, data analysis and AI ethics. He said organizations need to develop a culture of creativity and experimentation to support the growth of AI skills.
There are several key skills necessary for the successful implementation of an AI-driven initiative, Beregovaya said: data science; deployment engineering; prompt engineering; and informed and trained end users who understand the benefits of AI and how to use AI capabilities as a co-pilot to perform their tasks more efficiently.
Elmore noted that in computer science, algorithms, data structures and software engineering principles are essential for designing, analyzing and implementing AI models. Data science, he said, plays a vital role in understanding data collection, cleaning, preprocessing and analysis, as well as evaluating model performance.
Industry-specific knowledge is also critical for training AI models, selecting relevant data and interpreting and using their outputs in sectors, such as health care, finance and entertainment.
The human aspect of AI, Elmore said, is often overlooked, particularly in the case of LLMs that are trained on human-generated text. Understanding human behavior, including psychology, sociology and neuroscience can enhance the ability to extract outcomes from AI.
Due to the multi-faceted nature of AI, there are many different workforce skills that are necessary — such as NLP and an advanced understanding of neural networks, LLMs and even computer vision, said Katie Owston, VP and market specialist of security operations, threat intelligence and information security at Glocomms.
However, Owston said, one of the most under-appreciated AI skills are soft skills, which allow engineers and scientists to communicate with non-technical stakeholders to “fully realize their visions.”
Related Article: AI Certifications Can Help, But Don't Stop There
5. AI Budget
Many companies are trying to adapt to a turbulent environment by using AI in specific, practical ways to improve productivity. CTOs and CIOs, Stone said, have an opportunity to not only be creative in how they do more with less, but also where they place their investments.
Companies need to set aside capital specifically for AI investment, whether that be for tool adoption or training employees about existing tools, said Hart. There needs to be a material commitment to accelerate the understanding, adoption and deployment of AI tools across the business.
AI implementation, she said, will also require committed investments in data management, technology infrastructure and workforce training.
Since external AI applications are often expensive, budgeting should be based on ROI, and decision-makers should be confident, with the help of a technical vetting team, that they have a clear understanding of the benefits, said Beregovaya.
There’s also the buy-vs.-build decision: deploying AI via API might seem like a trivial task, she said, but in reality, the internal personnel overhead can quickly outweigh the benefits of controlling the tech stack.
Ravinutala said companies can’t forget that beyond allocating resources for the initial implementation of AI, they must also consider budgets for ongoing maintenance, upgrades and scaling. In his view, the best approach is to rank AI investments according to ROI and strategic significance and be prepared for unforeseen challenges and expenses that may arise during the phased adoption.
AI should be viewed as a strategic and ongoing initiative rather than a one-time experiment, he said. And integration within the workplace, when done right and forethought, can support a company’s ability to innovate, increase productivity and remain competitive.
