AI Adoption: Shift your focus from tools and technology to solving problems

Inside companies and boardrooms across the country, executives are fixated on the potential of generative AI.

The focus is warranted: Investment is high, use cases are proliferating, and adoption is advancing before governance can catch up to manage risk. Executives are eager to harness the benefits of this technology — and avoid the pitfalls of implementing it too late.

We understand the urgency. No one wants to be left behind. But the truth is that strong data analytics and machine learning are underutilized across many companies, and proficiency in these operations is much more likely to add value to your company than generative AI alone. There are ample opportunities to drive real value with tried and tested machine learning technologies — in addition to being on the cutting edge with generative AI. 

But that value will not be realized if you go into AI adoption without a clear strategic vision.

Companies that jump into AI without clear goals are destined to fail

Here’s the hard truth: Companies that jump into AI without clear goals are destined to fail. 

It is tempting to rush toward implementing the latest generative AI craze to demonstrate that your company is on the pulse or to meet a leadership mandate. But businesses that pursue machine learning adoption without a strong strategic plan will find themselves saddled with shiny new toys that don’t serve their needs. A generative AI model that can instantly adapt your company’s imagery into the style of Pablo Picasso may be fun, but does it help your customers? Will it drive revenue

Instead, consider integrating more large-scale machine learning models into your workflows to produce greater value.

Machine Learning vs. Generative AI: Defining the Differences

So where do you start when standing up machine learning models in your company? First, you will need to articulate your goals. What problems are you trying to solve, and what insights from your existing data would be useful to drive value? This stage can be both an opportunity and a challenge. Because these models can be configured to serve a variety of use cases, rather than being pre-trained on a narrow directive, business leaders will need to think critically and prioritize what is practically achievable over what is theoretically possible. 

It can be instructive to consider how other companies are beginning to use these models to create value. We have already successfully run almost 20 use case ideation workshops to find and prioritize the most impactful ideas. The clients we have worked with have a broad range of business objectives, but there are some recurring themes in how these models can be useful. 

Key Applications of AI

• Cost/Risk reduction: By identifying patterns of unnecessary expenditures or determining ways to minimize risk
• Productivity increases: For your workforce by reducing rote tasks and automating inefficient workflows
• Customer/revenue growth: By uncovering predictors of your customers’ demands, understanding the true value of your customers, and delivering an approach to maximize value and customer satisfaction
• Customer engagement: By using your data to determine what forms of outreach and communication will most satisfy your customers

Whether your needs fit neatly into one of these use cases or are unique, the lesson remains the same: Start with the use case, not the tool. When AI and machine learning adoption fails, it often comes back to companies lacking this strategic vision.

With this vision in place, business leaders should turn to a crucial next step: assessing the data inputs necessary to build their machine learning model.

Reflecting (honestly) on current capabilities

The old saying that you are what you eat is a useful way to think about machine learning models: They are only as good as their inputs. 

A strong machine learning operation depends on having data to feed into it, and you can’t build an innovative AI operation without reflecting on your company’s current data collection, access, and analytics capacities.

Confirming your organization’s existing data literacy, quality, and infrastructure are important baseline metrics to understand before you can layer on machine learning. This process will include an overview of the tools already available to your organization, how they are currently capturing and managing data, and how they are being accessed and used. 

After completing this audit, you can determine where AI and machine learning can fill the gaps. In many cases, your company may already be doing a good job at collecting comprehensive data — and the ability of machine learning to ingest this data and produce new learnings is all that is missing. In other cases, you may find that a more substantial data collection process is necessary before machine learning can be adopted.

Once you have established your strategic aims and determined the data that will feed into your model, though, you cannot simply sit back and let the model generate output. Managing machine learning models is an ongoing process — not only in terms of technical maintenance, but in ensuring that these tools continue to be in service of your business, and not the other way around.

Generative AI has value — if used strategically

Building a machine learning model will be the most powerful way to drive value from your business’s data. However, once you have established a machine learning model to harness the power of your data, layering on generative AI can add additional value.

This value is typically derived from using a generative AI platform to help translate the model’s findings into plain language for end users. In the same way that an application like ChatGPT can take academically dense writing and distill it into a simple paragraph summary, so too can your proprietary generative AI models help you make sense of the data synthesis conducted by your machine learning model.

We have worked with clients that have successfully deployed this method. For example, in the health care industry, we’ve created machine learning models to establish clear processes for physician decision-making. The model ingests the organization’s data to create new insights into optimal decision-making processes for physicians, while the generative AI tool helps the physician end users clearly and quickly understand those processes.

This is a prime use case for generative AI, which can be built on the foundation of the machine learning model to answer any questions the physician might have.

It’s also a narrow task for the generative AI to complete — which is precisely where its value lies. Generative AI may not be the foundation of your organization’s AI infrastructure, but adding it back in at the end of the process will help you to use these platforms in a more strategic, targeted way than your competitors.

Governing and guiding AI to work for you

Sustained success with machine learning models requires a governance infrastructure and clear guardrails for how — and how not — to use AI. Insufficient governance and guardrails around these technologies are a common pitfall that will take intentional work to avoid.

A governance infrastructure will establish roles and responsibilities for leaders across your organization to oversee the operations of your machine learning capacities. This will ensure that the way your AI tools are being used is not opening you up to any operational vulnerabilities.

A successful governance model will incorporate the components in the flowchart below:

Incorporating Organizational Change Management (OCM) is crucial in the early stages as you formulate your strategy and identify key leaders. OCM offers a structured way to roll out new processes, tailored to your organization's specific needs. For generative AI, this typically involves C-suite training sessions, defining use cases and an implementation strategy, and setting up a communication and branding plan. Effective OCM can alleviate employee concerns about generative AI's impact on their roles by clearly communicating its intended use and demonstrating strong leadership vision and wins along the path to full transformation.

Guardrails must also be established on both the input and output sides of your machine learning model (this holds true for machine learning as well as generative AI). On the input side, leaders should ensure that data is being used to deliver insights that are genuinely valuable to your organization. It can be easy to fall into the trap of doing an excellent job of making decisions to satisfy the analysis produced by the model — without considering whether you are tracking against the right metrics.

On the output side, it is vital to understand that what the model produces is not the end of the road. The data insights produced by machine learning should work in concert with the people who know your business. The model may recognize many different patterns in your data, but determining which of those patterns is instructive toward your strategic priorities will still be a job for your team.

Ultimately, these technologies are powerful — but it’s essential to not mistake the powerful data insights they can generate for a tool that can independently own strategic decision-making. These tools should be used to help you make better decisions, not make those decisions for you.

AI will be part of your organization’s future

Make no mistake: AI will soon be fundamental to doing business. But in the race to adopt AI and machine learning, the smartest companies will recognize that generative AI alone will not be the solution. The excitement around these tools is a useful entry point to imagine what is possible with machine learning.

Interested in leveraging AI for your business in the right way? Contact us to get started.