The Growing Role of AI in Digital Transformation

Artificial intelligence (AI) is easily the most important enabler – no, accelerant – of digital transformation.

We’ve watched these predictions play out in real-time – amid conditions no 2010s-era alarmist could possibly have imagined. The point is, coping with the challenges we anticipated, plus all of the post-COVID chaos we didn’t, is a lot for us humans to take on by ourselves.

And while it’s still true that the most important parts of digital transformation have very little to do with technology (i.e.: culture, strategy, well-designed workflows), the execution can’t happen without artificial intelligence (AI).

Here, we look at AI’s central (and growing) role in digital transformation, how it’s evolving, and why DX can’t happen without AI.

How Does AI Help Digital Transformation?

The simple answer is, AI supports digital transformation by augmenting human capabilities. It can analyze massive data sets and instantly serve up actionable insights humans wouldn’t be able to identify on their own – at least not quickly enough to make a difference.

It automates key processes so that front-facing employees can focus on creating value and building relationships. For example, chatbots and virtual assistants provide 24-7 support, help human agents handle support tickets, and give employees more time for meaningful work.

Overall, AI powers smarter, faster decision-making, boosts productivity, and allows orgs to create tailored products, content, and end-to-end experiences for super-specific customer segments and even individual end-users.

The real (and more complicated) answer to this question is, it depends. AI can be applied to countless use cases and used to achieve a near-infinite set of goals.

But only if you have a comprehensive game plan, a proven business case, good data, and the ability to use it.

The AI Strategy is Changing

In a recent CIO piece, HP experts say that the real benefits of AI begin with a shift in mindset – moving away from a “tactical” approach and toward a more holistic, “transformational” strategy.

AI and ML projects can fundamentally change everything about how a business runs, connects with customers and drives growth.

Unfortunately, however, too many orgs still invest in incremental improvements. And, as a result, orgs waste valuable resources on AI initiatives that not only fall short of big-picture goals but fail to generate any real value.

Business leaders really need to grasp the fact that automating simple processes and deploying a couple of bots won’t yield anything remotely “transformational.”

Accenture defines AI maturity as having both mastered a set of core capabilities and, crucially, understanding how to apply those capabilities to the right use cases, using the right combinations and the right data.

Basically, AI solutions don’t just “work” straight from the box, and companies should plan on tailoring their platforms based on the customer insights and real-time data they capture over time.

But, beyond that, tapping into the true power of AI hinges on the end-user’s ability to combine data sources and work with real-time insights in creative, new ways.

According to Forrester, if orgs truly hope to achieve tech-driven transformation, then they must embrace a holistic approach to AI transformation analysts call “connected intelligence.”

That means looking at AI as more than just a chatbot or language processor — and instead, working to build these connected ecosystems that span all channels, touchpoints, data models, services, and so on.

Those connections can then bridge silos, fill gaps, and facilitate cohesive experiences.

McKinsey experts agree, and say embedding AI across the entire organization (and all departments, locations, processes, and people within it) is the only way to unlock its full potential.

What’s more, Deloitte’s most recent State of AI in the Enterprise report, found that AI-driven businesses treat data as an asset. Leading orgs have the ability to scale human-centered AI across all business operations. This means that, rather than relying on recommendations from the IT or data science team, this group uses proven business cases and end-user feedback to inform the strategy.

AI Must Be Implemented with Care

Finally, it’s important to remember that anything involving an algorithm requires a lot of care (and caution).

One of our clients, Gatsby Chocolate initially reached out because they needed help streamlining their inventory management process and leveraging automation to boost business agility.

First, we implemented D365 Business Central — giving the Gatsby team the connected ecosystem they needed to begin their DX journey. This enabled them to continue processing orders if supply chain conditions changed, manage a growing network of outsourcing partners, and bring more products to market faster.

Over time, we helped Gatsby use the data captured via D365 BC to identify opportunities to automate processes. It’s important to mention that we implemented AI and automation one phase at a time — starting with an ERP implementation and strong manual processes, and gradually layering in more advanced capabilities.

Skipping steps or cutting corners can put your company (and customers) at-risk.

AI processes massive data sets and runs processes at a rapid pace — flooding your system with bad data and amplifying the impact of things like poor planning, sloppy implementation, or even just a handful of one-off reporting errors.

This impacts everything — project, resource, and financial management, sales performance, the ability to deliver on what was promised in client contracts or function as a trusted partner that gets results.

In some cases, errors can fall through the cracks and wreak havoc on real people’s lives.Data breaches and cyberattacks on IoT/OT linked to critical infrastructure are big ones. But so are overzealous algorithms.

As a recent example, Bank of America was fined $225M for mishandling pandemic relief funds when an automated fraud filter froze legitimate accounts. Customers were unable to access those benefits – and in several cases, were blocked from using customer support channels to resolve the problem.

Now, BofA is a giant bank, capable of moving past a financial blow of this magnitude, but it’ll likely face challenges winning back consumer trust/losing customers to competitors — which could lead to serious long-term losses (potentially a major decline).

Final Thoughts

Look, AI’s potential benefits and use cases are as expansive and unique as the organizations using this game-changing tech. So, the real (read: more complicated) answer to this question is, AI can be applied to countless use cases and used to achieve a near-infinite set of goals.

However, those use cases must support a proven need (aka – you have data to back up this decision). And – you’ll need to have the right strategy, infrastructure, and cultural foundation in place to support your AI initiatives.

Otherwise, you could end up amplifying existing problems and creating all sorts of expensive, damaging issues with the potential to take down your business – and, worst case scenario: cause serious harm to your customers, partners, and the general public.

From professional services firms and distributors to agribusiness companies and field services providers, Velosio helps clients tackle DX’s biggest challenges and create new value with AI, machine learning, and process automation.

Get rid of repetitive task, create creative content, and make data-driven decisions. Copilot is poised to deliver on the promise of technology. Understand Copilot’s capabilities and how they can best fit in your company’s workflow and goals. Contact us to learn more.

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