By Tim Beerman, CTO, Ensono Smarter, faster, better decision making starts by recognizing and removing the hidden-in-plain-sight constraints on your data’s full potential.
Given its broad democratization, ongoing proliferation and extraordinary pace of evolution, we’re likely to feel that overwhelm for a while. It was an unprecedented event in many ways. But as the saying goes, the more things change, the more they stay the same.
The advent of cloud computing back in the early 2000s was also a seismic technological shift, igniting a similar sense of excitement, urgency and anxiety among those who understood its potential. The host of good, bad and ugly actions organizations took in response offer valuable cautionary tales about how to proceed—and especially, how not to proceed—in the face of the profound and urgent-seeming disruption.
To paraphrase another adage, if we don’t learn from history, we’re doomed to repeat it. Here are three areas in which examining some of the steps (and missteps) taken in the early days of cloud adoption can help your organization maintain solid footing as you forge ahead in the AI age.
Any tool, no matter how cool or cutting-edge, is only valuable insofar as it solves a business problem or serves a business goal, ideally in a way that’s ultimately cost-effective.
People and culture: Strengthening the backboneof your business
The move to cloud computing wasn’t just about technology; it fundamentally altered how IT professionals approached their work. Training and workforce upskilling were crucial in transitioning to cloud-based models, as were change management strategies that recognized the human element in this technological shift which, like all such shifts, sparked anxiety around job security. More than just imparting new tactical skills, organizations needed to nurture within their IT teams a culture of comfort with continuous learning and innovation.
The major difference between cloud and generative AI–and it’s a huge one—is the latter’s extraordinarily democratized nature. While cloud computing also democratized IT in a way that had never happened before, the extent was limited. If you weren’t operating in the rarefied professional realm of IT, it probably wasn’t on your radar.
Generative AI is ubiquitous both as a general topic of conversation and contemplation outside of the enterprise, and as something that either has or eventually will touch just about every role within it. And the existential threat it presents is on a whole different scale, calling into question not only the future existence of specific jobs, but also the nature of human agency, ingenuity and creativity in general. Successfully navigating your people and culture through this new, uncertain and potentially frightening landscape requires an enterprise-wide commitment to:
The most immediate promise of generative AI is that it can help people work better and faster—which means the most immediate threat isn’t to jobs in general, but to people who aren’t using it in their job. Not leveraging this incredible tool is the equivalent of using a regular screwdriver when you’ve got a cordless one at hand. It’s going to slow you down, and you will be less productive. Communicating clearly to your teams about how AI can augment their jobs rather than replace them is crucial. Focus on how AI can enhance job performance and quality of life and unlock new career opportunities, and be prepared to make good on that promise.
The need for AI-specific roles, such as prompt engineers, is emerging rapidly. Upskilling for generative AI differs from cloud computing because it requires not only technical skills but also creative and critical thinking. Training programs must be designed to enhance these skills, focusing on how to leverage AI tools effectively for various professional needs. Beyond role-specific training, investing in more general learning opportunities such as workshops and knowledge-sharing sessions is also a must if you want to quell anxiety and foster a culture of curiosity and continuous learning.
Achieving broad adoption of any new technology is always a challenge, and generative AI is no different. More than anything, bridging this gap comes down to a combination of education and habit breaking. For instance, instead of defaulting to traditional search methods, our employees are encouraged to explore sanctioned AI tools for more efficient solutions. Effective training should include practical examples of AI success along with cautionary tales, so employees understand both the potential of these technologies and their limitations.
Integrating AI into the workplace goes beyond technical training. It involves developing a culture that understands and respects the ethical dimensions of AI. Employees should be encouraged to question and critically evaluate AI-generated outputs, balancing trust with healthy skepticism.
Business alignment and strategy: Keeping all eyes on the prize
When cloud computing burst on the scene, everybody in IT loved it, and everybody could find an easy way to use it. That led to a whole lot of siloed decisions, rogue credit card swipes, ghost machines and end-of-year budget shocks. The key takeaway was the importance of aligning technology investments with clear business objectives to ensure that any new technology would serve as a tool to enhance business capabilities, rather than a costly endeavor with uncertain returns.
Here again, what was true for cloud computing is exponentially so in the age of generative AI. There is, it seems, an AI-based app for everything, with more coming online all the time. But any tool, no matter how cool or cutting-edge, is only valuable insofar as it solves a business problem or serves a business goal, ideally in a way that’s ultimately cost-effective. To ensure you avoid costly, wasteful or simply unnecessary AI investments, focus on:
Identifying value-driven AI use cases – Run proof of concepts with generative AI to identify use cases that directly contribute to business value. Whether it’s reducing costs, generating new sales opportunities or improving client experiences, the goal should be to ensure that every AI initiative has a clear, measurable impact on the business.
Understanding the Total Cost of Ownership of AI – AI costs extend beyond the initial investment to include ongoing operational expenses. It’s crucial to evaluate whether AI tools make existing teams more efficient or less. For all the concern over AI taking jobs, right now there is, to my mind, a greater likelihood of AI-based tools surfacing new requirements and necessitating new staff, projects or both to meet them.
Funding and sustaining AI innovations – Strategic funding of AI initiatives is vital for long-term success. This involves setting clear objectives for each investment, such as pilot programs, and expanding only when a solid business case is established. Keeping pace with AI advancements means being selective in partnerships and investments.
Scalability and adaptability in AI implementations – While scalability is less of a concern due to the cloud-based nature of generative AI, adaptability remains crucial. The rapid evolution of AI technology demands a flexible approach that will allow you to shift gears as new opportunities and tools emerge.
Long-term strategic planning in a rapidly evolving AI landscape – Though it may seem counterintuitive, in this fast‑paced environment, long-term strategic planning is more important than ever. Companies must be prepared to course-correct and adapt their strategies as AI technology evolves. This involves continuous evaluation of AI initiatives against business goals and being open to exploring new partnerships and tools as they become available. (See also, “Recalibrating the portfolio approach for successful AI investment,” page 16.)
And remember: It all comes down to the data. A sound data strategy that ensures data is secure, accessible and high quality is foundational to leveraging AI effectively.
Generative AI technologies will increasingly rely on robust data ecosystems to deliver meaningful insights and solutions. (See also, “How to set your data initiatives up for success,”).
The transition to cloud computing notably elevated the significance of governance, risk and compliance (GRC) strategies. Then, the focus was on understanding the type and location of data, especially concerning sensitive information like Personally Identifiable Information (PII). Managing risks in cloud computing involved monitoring data usage and ensuring data security.
This understanding is equally crucial in the age of generative AI, as AI tools interact with an organization’s data. But the new complexities introduced by this technology require a nuanced approach in the following key areas:
Proactive risk management – Effective data governance ensures that data is not only used legally and ethically, but also managed and secured appropriately. It is critical to understand how AI tools are used within your organization and who has access to them. Establishing mechanisms to track and report data usage is essential, especially when AI makes data querying more accessible.
Compliance agility – Compliance in the context of generative AI is still evolving. As this technology becomes more integrated into everyday tools, organizations must be cautious in their commitments, especially regarding AI usage disclosures. Compliance frameworks like SOC, ISO and others will also need to adapt to these changes.
Ethical oversight – The ethical challenges introduced by generative AI are real, and rightfully concerning. Despite efforts by major AI developers to mitigate it, the underlying issue of human bias in the development process remains. It’s crucial to maintain a human-in-the-loop approach to ensure that all AI outputs align with organizational values and ethical standards.
Continual adaptation – As AI technology evolves, so must your governance, risk and compliance strategies. Organizations need to stay informed about the latest developments in AI and adjust their GRC frameworks accordingly.
The early cloud adopters who thrived were those who integrated new technology enthusiastically but also judiciously, taking the time to understand and accommodate its practical implications. With a page from their playbook, you can step into the AI world more thoughtfully, confidently and ready to leverage its awesome potential.
