Dan Adika is its CEO WalkMedigital adoption leaders who enable every organization to realize the promise of their technology.
ChatGPT’s immediate appeal is obvious. An intuitive interface offers endless answers at the touch of a button. The genetic AI (GenAI) behind chat is not just an instant encyclopedia that delivers original results every time. It can allow us to think faster and plan more efficiently. Putting GenAI to work in a business context can go even further, helping to accelerate real-world workflows and improve productivity.
But using GenAI for work is more complicated than asking a question in ChatGPT: Actually, 91% Americans recently surveyed said they “failed to use AI effectively at work on the first try.” That’s because GenAI tools aren’t just general-purpose chatbots. they have artificial intelligence capabilities attached to the many applications that make up a digital workplace. This leaves end users with the daunting prospect of figuring out how to effectively use GenAI for their specific roles. There is a steep but predictable learning curve with any new technology, and GenAI is not immune to adoption barriers. Fortunately, we can navigate through them and chart the co-pilot’s success.
Enterprise Barriers And Paths Forward
McKinsey recently is described some challenges businesses face in adopting AI at scale, including data quality, employee mistrust, implementation costs, and the fact that technical capabilities have grown faster than leaders’ visions of how AI can transform work. But there is another set of practical problems rooted in digital reality that people who try to use artificial intelligence in their work face. Business leaders should understand these barriers and solve for them:
Digital Sprawl And The Digital Adoption Gap
For more than a decade one of the biggest problems of digital transformation has remained: the gap between the rate at which we introduce technologies into our organizations and the rate at which people adopt them. In most businesses, employees don’t use many of the tools they have access to, or don’t use them fully.
The latest versions of the same tools and apps now introduce GenAI capabilities. As each application evolves rapidly, the digital adoption gap that already exists widens exponentially. The new co-pilots must be used regularly by the vast majority of employees for companies to realize the value and significant productivity gains. Walking through the dozens of apps we use every day, each with its own pilot, reinforces app, process, and workflow silos and learning curves. Disjointed co-pilot experiences can create friction and limit the productivity gains they are designed to encourage.
The Path Forward: SaaS companies won’t stop building AI products. Enterprises can reduce sprawl and democratize the use of AI by adopting a co-pilot that works across multiple applications and workflows. A cross-application co-pilot, aware of an organization’s applications, unifies AI assistance to make it easy to understand and use.
Tools force us to pull what we need
Most co-pilots are “pull” models. They require us to seek out AI and figure out how to extract what we need from it to do our jobs. They force us to provide some of the context that AI needs to help us, rather than having all of that context pushed at us, ready to go, when and where we need it most. How much time is wasted trying to figure out how to effectively prompt the AI and what happens if the AI responds incorrectly? Do teams know when to challenge results? Recent IDC research revealed that 58% of CEOs believe their teams lack the skills to meet the needs of AI initiatives.
Additionally, if bad data is captured by AI through engagement, its impact can compound over time as multiple systems within an organization assimilate and rely on it. At best, businesses will waste time and resources.
The Path Forward: Workers won’t suddenly become instant engineers, with the developed skill sets needed to take full advantage of many AI tools and co-pilots. Businesses need to deliver human-centric GenAI—because all its value lies in people actually using it, and doing so in a way that moves the needle. This means that a co-pilot should understand the role of a person using the tool, its functions, the workflow involved and what they are likely to be trying to achieve at any given time. By understanding the entire context around the user, human-centric AI empowers workers rather than adding another layer of complexity to their day.
Visibility into what’s working (or not) is murky
Many co-pilots don’t offer the visibility businesses need about which teams are using which tools, how they’re being used, what’s working, and where you’re seeing successes and failures. We cannot realize the promise of GenAI if there is no visibility into how GenAI experiments are or are not performing — and why.
With a lack of visibility, so do security and compliance risks, as some employees use unauthorized AI applications, known as shadow AI, or accidentally misuse tools due to a lack of guidance.
The Path Forward: Businesses can only fix what they can see. A GenAI co-pilot that collects usage and behavioral data across applications can provide unbiased adoption analytics, improve its own functionality, strengthen organizational security, control costs, and provide hard proof of what works and what doesn’t. No.
Finding True North with a co-pilot
A GenAI co-pilot cannot perform all tasks and cannot replace human creativity, toughness and multi-level decision-making. However, it can accelerate a huge range of business workflows and processes if it has cross-app simplicity, understands context to improve usability, and enables visibility to reduce waste and enhance security.
The ultimate business goal with a co-pilot is that it can serve as a true and consistent help to every person in an organization, regardless of their technical abilities. This accessibility standard enables adoption. Contextual awareness is key because it shapes this human-centric focus, where the co-pilot can suggest relevant next steps tailored to each user in the workflow and then execute approved actions. By overcoming co-pilot adoption barriers, businesses will find their true north in measurable, visible productivity gains.
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