Every workforce revolution is tied to human evolution. Just like heavy machines empowered manual labor and elevated many workers to skilled roles, AI will uplevel developers and programmers to collaborate with almost unlimited IT resources.

The knowledge gaps we’ve been trying to fill for years can be addressed. Anyone can write a piece of application without possessing programming knowledge. Anybody can become a creative storyteller without the power of language.

Now that we’ve had a year of runway with Generative AI, organizations everywhere are finding their bearings, and we’re seeing a lot of progress across industries. Companies of all kinds are finding ways to use AI to drive real-world business outcomes.

In the productivity space, tools like Microsoft Copilot and Google Duet write better, research quicker, and review and summarize documents more efficiently. In cybersecurity, AI is assisting SecOps and IT departments to see through the noise of web traffic to predict potential security threats.

Developers are benefiting, too, with tools like Code Companion or GitHub Copilot. These systems collaborate with developers to provide the foundational code for programmers to work on top of.

But what about digital transformation? Can this moment with Generative AI be that great equalizer, where companies who were once behind can leap ahead?

Yes and no. AI is the tip of the iceberg when it comes to transforming business processes. There is a lot of unseen work underneath—key areas that every company must address before they apply Generative AI to their digital transformation. Here are some principles to keep in mind.

Build a culture of data.

AI changes how an organization works with its data. Rather than using data to provide a snapshot of past events, AI can begin to understand patterns and identify potential roadblocks and opportunities that lie ahead.

However, the results attained from using Generative AI are only as good as the data it’s working with. If the organization has not invested in the health of its data estate, Generative AI may provide hallucinations based on faulty, incomplete or misaligned information.

The first step toward driving business outcomes with AI is to create complete, reliable and consistent data flows. Companies should look at how they can modernize their approach to data so they can turn it into an ongoing asset through any new AI solution.

Doing this effectively means creating a culture of data across teams, so they are not dependent on the IT department alone to source, store, archive and draw insights from information. Companies should think about how they can upskill employees to ensure everyone recognizes the value of proper data practices and can attach them to business objectives.  

Decide which cloud approach is right for you.

Data is IP; accordingly, many companies are hesitant to pen up for Generative AI tools over data IP issues. For these companies, their doubts can be cleared by architecting a solution that protects internal data while at the same time leveraging the powers of Generative AI.

There are ways to protect data in a public cloud environment by isolating it from the broader ecosystem. Under this model, the data remains within the organization’s cloud account, and the AI solution can dip into the broader ecosystem to provide accurate suggestions.

For some enterprise companies, they have huge amounts of data that can be monetized by creating domain-specific / domain-tuned LLMs. A large logistics company may have petabytes of data on the predictability of shipments they want to use for a new logistics service. An extensive healthcare system may have a massive data estate to analyze diagnoses or outcome trends, but it needs to balance research with protecting sensitive information. In these cases, the best approach may be walling sensitive data from the public domain entirely and then providing access to partners by creating ways of monetizing.

Rely on a growing partner ecosystem. that can help.

Working with a partner can ease this process. Many systems integrators and solution providers have years of experience in AI and know the pain of implementing it quickly with no plan. Companies who are struggling with where to start can tap into that experience to help drive their AI initiatives.

It’s important to select a partner who is invested and aware of the customer’s business objectives and capabilities. These types of partners not only drive initial positive results with digital transformation but also continuously implement innovative processes in IT departments, providing enhanced solutions to end users.

Use AI to accelerate transformation.

AI can accelerate an organization’s transformation journey, but it's not a shortcut or a silver bullet. A company reliant on mainframes will still lag behind competitors who started migrating or already migrated from mainframe to the cloud years ago.

Yet, Generative AI can help companies catch up by fast-tracking their modernization journey. For instance, an organization wants to migrate a legacy application running on a mainframe and convert it to a modern cloud-based application. In that case, it can use a code companion tool to help developers move workloads from COBOL to Java.

Another primary benefit of the recent Generative AI revolution is that projects that may have been unattainable for many organizations are now within reach. Where it previously took substantial time and resources to put an AI system in place, AI has become much more accessible, and it’s easier to get greater value from the investment.

Start small and build from there.

Organizations today look for faster ROI. AI can deliver quick wins with an initial pilot or prototype, creating larger value through iterations over time.

One example is customer support. Building a chatbot system that can provide a workflow to answer all the possible questions for a given customer base can take several months or even years.

Starting small, the company can work to identify the most frequently asked questions and address those first. Creating the first workflow for a password reset, for example, could demonstrate a quick and substantial reduction in support tickets that justifies further investment in the system. From there, you can move on to other maximum items that can be automated in this way.

This also minimizes the risk of the investment—if it is not creating the expected ROI, the company can pivot and go down a different path.

Lay the foundation to leap ahead.

AI can power endless possibilities to transform your business—much like the workforce evolution we’ve seen throughout history, elevating the workforce and accelerating how business gets done. The challenge, however, lies in creating a strong foundation, so you’re well-positioned to take the leap and succeed.