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Savannah Josey
Nick Chuva Plagge

Partnership of the Future: Near-Term Strategic Planning for AI Integration

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How businesses can design to maximize both man and machine: Number one in a series

People, Process, and the Promise of AI

Terms like machine learning, large language models (LLMs), generative artificial intelligence (AI), and big data have existed as part of the lexicon for years, but are now having an undeniable moment in the sun. The technologies they refer to are transforming industries, business models, and job roles at a faster pace than almost any technology that came before them. The notion that a surge of productivity might be attainable with plug-and-play solutions promising minimal business disruption presents an enormously attractive proposition for C-Suite executives anxious to set their organizations apart from competitors. Business leaders are being forced to respond to the major technological shifts that these technologies represent.

This nascent era of artificial intelligence presents both immense opportunities and significant challenges for enterprises. As businesses of all sizes ramp up AI investments, the Stanford AI Index Report found that corporate investment in AI increased by over 120% between 2020 and 2023, with particular emphasis on generative AI, natural language processing, and machine learning applications.[i] Many of those businesses are beginning to realize just how potentially difficult it is to put AI to work correctly.

For employees, there’s a range of sentiments regarding the adoption of AI and similar tools: fear, excitement, distrust, even curiosity. Questions abound around how AI will impact job descriptions, workforce planning, and the quality, individuality, and purpose of work. As employers move to respond, it’s important to navigate the landscape mindfully, setting a strong foundation to build an enduring strategy that can nimbly adjust with evolving technology and retain the trust of workers whose roles may shift, but whose presence is still critical to a thriving organization.

AI and people are often framed as competitors, as seen on a wide scale by the way that the increasing functionality of LLMs is measured; just look at how ChatGPT, Claude, and Gemini judge their newest releases against both competing LLMs and the capabilities of humans.

This can spark fear in workers who view their humanity as a vulnerability and who feel unable to measure up to the technical abilities of generative AI that progress at a pace that feels impossible to match. But this juxtaposition of man and machine largely misses the mark: what workers and employers should align around is the strength in the partnership between evolving AI tools and people. Jobs may change, but certain skillsets, especially in the near term, are unlikely to be fully replaced. Indeed, little is known about the unintended consequences of what happens when AI or similar automation works to diminish the unique value that only human employees can provide to a business. When company mandates and initiatives are framed around shared collaboration that enhances productivity and maximizes time for humans to provide nuanced, sophisticated contributions – ones that can’t be fully outsourced to technology – the byproduct is, more often than not, enhanced processes, increased productivity, and happy employees.

The balance between workforce augmentation via AI and automation, and the rational ability for humans to innovate in spontaneous and creative ways, is fragile, and there are a lot of ways to get it wrong. AI for the sake of AI isn’t enough, and certainly doesn’t help effect sustainable, foundational change. When we think of the right way to leverage the immense power of today’s newest technologies, we find that the companies that stand apart from the rest have built the following three things into their approach:

  1. Human centricity – We know automation and AI-driven tools are coming, but in the near- and medium-term, strategic resilience comes from thoughtfully nurturing human talent and recognizing when to pass the proverbial baton from man to machine, and vice-versa.
  2. Solid underlying process framework – It’s tempting to bolt on AI to an outdated manual process, but this application doesn’t solve pain points – it only creates new ones. The best implementations consider how to first enhance existing processes to ensure that automation is solving the right problems in the most streamlined and direct way.
  3. Strong, holistic governance measures – With so much we don’t yet know about the future, building adaptable guardrails and active risk mitigation systems is a critical step that will deeply impact the future of businesses, and ensure that both new opportunities and unforeseen challenges can be addressed quickly.

Building a cohesive AI strategy requires taking a holistic business approach, with thoughtful consideration toward how workers will be impacted in the near- and long-term. This article is part of a larger series that will explore what is a complex, multifaceted topic. Our goal here is to focus on the near- and medium-term implications of AI adoption; we’ll discuss more long-term facets in an upcoming piece.

Augmented Ambitions: Aligning AI and Abilities

In an increasing number of industries, training data – or the content fed to a large language model to “teach” it how to provide the most accurate responses – comes from top talent. It’s safe to say that the best AI models are built from (very large quantities of) high quality data. For example, the methodology behind an R&D process or the training process of a quality instructor can serve as valuable input for an LLM. But because this input enhances the quality of responses from AI, many experts feel threatened that their primacy or expertise is being quickly undermined.

This anxiety is not dissimilar to the reaction to the now ubiquitous modern calculator, an innovation that, when introduced, made many expert mathematicians feel that their then-heretofore critical role was all of the sudden made redundant. There was truth in the reality that the tedious task of completing calculations longhand or with a slide rule had become exponentially faster and that a skillset had been democratized in the process. That said, mathematicians did not become suddenly obsolete. To the contrary, being able to quickly and accurately perform complex calculations (or, for some of us, to figure out the tip on a dinner check) freed up this highly specialized group of knowledge workers to explore more complicated or esoteric mathematical and even scientific themes. The same can be said for AI.

JP Morgan Chase’s Contract Intelligence program is a more recent example of innovation that models this shift in the tech landscape. The company’s machine learning system utilizes the bank’s private cloud network to review commercial credit agreements, a process that previously took around 360,000 hours annually from the firm’s lawyers and loan officers. Across the more than 12,000 contracts processed annually, JP Morgan enjoyed an overall accuracy improvement from 60-70% to 90-95%, due mostly to the difference between human-only fatigue versus AI review with human oversight.[ii] This major optimization process unlocked productivity among workers who were redeployed to different, higher-skilled jobs that ranged from complex deal structuring, to relationship management, and even to AI oversight. The use of upskilling also allowed the legal staff to combine their legal expertise with timely technical knowledge.

CTRL+ ALT+ COLLABORATE: Skills for a Future-Ready Workforce

It’s certainly tempting, given the current economic climate of squeezed margins, potential trade wars, and general uncertainty, for employers to view AI as a panacea. And many initial fears about AI center around widespread human displacement, or the idea that machines will replace workers. However, research consistently shows that AI more often augments work than replaces it, particularly when organizations proactively upskill employees to use AI tools effectively. The results of a study in ScienceDirect underscore multiple additional findings that “AI and robotics can enhance professional capabilities,” and that the benefits of digitization include new job creation and better job quality.[iii]

Upskilling, or the process of helping workers bolster or modify existing skills to adapt to technological changes in the workplace, helps mitigate fear and resistance. Employees are more likely to engage with AI tools when they understand how these systems work and feel confident about their relevance.

The pace of technology isn’t slowing. Being innovative with human-centric learning and development is one way to empower workers to fluidly evolve alongside changing conditions. But identifying and building the skills of the future within the workforce is a complex task. Contrary to historical methods of large, one-size-fits-all classroom trainings or even virtual versions of the same universal content, skills-building for the future needs to be as dynamic and personalized as the people on the front lines who are expected to keep up with the pace of technological innovation.

Neuroscience, too, supports that building upon existing strengths, where the brain already has strong synaptic connections, is a more productive approach to talent development than asking workers to instead focus on addressing weaknesses, where they are likely to have weaker synaptic connections[iv]. Because 63% of employers consider employee skills gaps to be a main barrier to future-proofing operations, this notion of building upon existing strengths and addressing weaknesses with a mix of talent and technology should be enticing to business leaders. As technological skillsets advance, speed of information processing increases, and machines become more capable of effectively synthesizing big data and performing repetitive, standardized tasks, human skills are becoming more important – and more in demand: analytical thinking, cognitive skills, resilience, leadership, and collaboration are top skills that remain relevant for the future workforce[v]. Closing skills gaps that play to human strengths and minimizing weaknesses that technology can readily accommodate is the next frontier of learning and development.

Unlocking the full potential of workers today requires using tools to design personalized learning and development solutions that build upon each worker’s unique learning styles and contribute to a unified mission. Addressing skills gaps by working with neuroscience to maximize existing neuropathways, tailor the pace, depth, and type of content, and cater to an individual’s preferred learning style(s) to enhance understanding are among the proven benefits of personalized upskilling platforms.

To ensure the successful implementation of this type of training, organizations must align their upskilling strategies with both business goals and technological capabilities. To that end, we see the following as crucial to a successful upskilling program:

  1. Assessment of current workforce capabilities: Start by evaluating the existing skill set of your workforce. Identify gaps where employees lack the technical knowledge needed to work with AI tools or understand their implications, and work to address those challenges with clear strategic communication.
  2. Clear definition of role evolution and career pathways: As AI reshapes job roles, companies must clarify the evolution of these roles. What new responsibilities will employees have, and how will their career paths progress? Again, clear communication about these changes is vital for meaningful engagement.
  3. Personalized, modular learning paths: Not all employees need the same level of AI literacy. Develop modular training programs that allow employees to learn at their own pace. Personalized learning pathways should ensure that each employee gets the knowledge they need, whether it's a foundational understanding or more specialized expertise.
  4. Measurement and iteration: Collect data on how employees are progressing and how effective upskilling programs are. Use this data to iterate on training content, methodologies, and the overall AI adoption strategy. Continuous learning and adaptation are essential.

Measure Twice, Automate Once: Optimizing for the Intangible

Now more than ever, the most sophisticated businesses recognize that what has worked historically doesn’t guarantee future success. This naturally extends to operational processes.  Although increasingly powerful and adaptable, AI isn’t magic. Simply adding an AI component to an inefficient or cumbersome process isn’t – at least not yet – going to increase productivity. Many processes that companies are in a rush to digitize or automate would benefit from refinement first, and automation second. Before embarking on widespread adoption of automation tools, companies should think about future-proofing, and specifically about why a specific tool was selected, how it will enhance outcomes, and what specific problem it’s solving; otherwise, not only is enormous potential missed, but inherent flaws are likely to be amplified, and the process supposedly being fixed will likely be one identified for yet another overhaul during a subsequent, probably expensive, assessment.

On the other hand, businesses that invest in identifying the root cause and searching for simplification opportunities, inclusive of broader process enhancements, are well situated to have enduring, optimized processes once those processes are automated.

One example of the latter is Tesla, which, despite producing in 2018 the best-selling luxury electric car in the world, suffered from processes that weren’t organized in a way that reliably met market demand. Rather than go about automation with a blunt object (or a chainsaw) and digitize en masse for the sake of digitization, Tesla instead chose a more strategic and surgical long-term approach that focused on a human-centric business model overhaul. Meeting optimization occurred first, followed by an audit of vendors to determine which ones could meet targets for production speed. The company then updated its internal communications channels. It wasn’t until after these optimizations – enhancements to processes that impacted other businesses and employees across the enterprise – had been identified and targeted that automation was introduced into facility management. The result: new production records were achieved for their top-performing luxury model. While a specialized example, the impact is clear. The answer isn’t automation at all costs; it’s process optimization at all costs, and both humans and machines have a potentially important role to play as companies embark on the strategic exercise of refining and tailoring processes to meet changing workplace needs.

Governance in the Age of AI Can’t be an Afterthought

Designing effective governance systems with AI adoption in mind involves establishing the traditional ethical and transparency guardrails associated with data and technology and also building in flexibility to account for both rapid changes in technology and the ability to pounce on market opportunities. The best data and AI governance also helps employees navigate the technological landscape with clear expectations on the risks and rewards associated with both open source and proprietary AI tools, protect sensitive data and corporate IP, build trust and credibility around tech tools, and empower a company’s people to confidently embrace AI and related technologies to enhance productivity.

As corporations design these frameworks, it’s important to balance enthusiasm for innovation with measured, overarching safeguards. Governance models are evolving, but some of the same rules apply in the digital age as in the analog era; we still see that the best models contain consistent and transparent disclosure rules, strong monitoring and auditing, secure collaboration and data privacy, and shared accountability.

How Bizlove Approaches The Future Of Work

AI and related technologies are rapidly and profoundly changing the way we approach work. In this pivotal season of widespread transformation, it’s important to ensure that:

  • People are on board: Despite a lurch toward widespread augmentation, we still believe that an organization’s most valuable asset is its people, and we help clients design adoption that leverages the unique talents and abilities of their workforce. Workforce augmentation allows us to help clients save valuable time, money, and resources by future-proofing talent needs.
  • The right process infrastructure is in place: Successful AI adoption is a holistic exercise that requires putting a lens on how work is done and redesigning for the future. We believe that automation doesn’t exist in a vacuum. Solving for the root challenges when optimizing systems is what drives lasting impact.
  • Effective governance frameworks are in place: New technologies require thoughtful guardrails and success metrics; we help clients build governance and reporting structures for the long-term by blending historical best practices with our knowledge of what’s coming next.

Perhaps more so than at any other time in recent memory, future business success is directly and deeply impacted by decisions that leaders are making today. We believe that establishing clarity around the expectations and access surrounding AI, developing a culture of technological curiosity, comfort, and compliance, and bolstering intangible skills that are increasingly in demand due to shifting workforce skills gaps are among the most important actions that today’s leaders can take to successfully transition their businesses into this challenging, but exciting, new era.