ChatGPT was released to the public on 30 November 2022. Within five days it had one million users. Within two months it reached one hundred million. Those numbers matter less than what happened in the boardrooms of large enterprises in December: executives who had spent years sitting through AI strategy presentations about machine learning pipelines and training infrastructure had, for the first time, a direct personal experience of what the technology could do.<\/p>\n
The experience was not uniform. Some executives were impressed. Some were alarmed. Some were sceptical that the demos they saw on social media reflected genuine enterprise capability. All of them had opinions, and they were asking their technology leadership about those opinions within days of the launch.<\/p>\n
This represents a genuine shift in the AI conversation, and enterprise technology leaders need to understand what it means for their strategy and their board communication. ChatGPT did not change the technology. Large language models have been a focus of research investment since GPT-3’s release in 2020, and the enterprise AI landscape has been building momentum for years. What ChatGPT changed is the executive comprehension of and interest in the technology at a level of specificity that makes the AI conversation qualitatively different from what it was in October.<\/p>\n
The most immediately useful thing enterprise technology leaders can do in the weeks after ChatGPT’s launch is develop a clear articulation of where large language model capabilities create genuine near-term business value in their specific context, and where the most exciting use cases remain further away than the demo suggests.<\/p>\n
The capabilities that are immediately available and demonstrably useful include document summarisation, content drafting and editing assistance, code generation and review, customer support conversation assistance, and internal knowledge retrieval across structured documents. These use cases do not require fine-tuning a model on proprietary data. They work with general-purpose language models accessed through APIs, with appropriate prompt engineering and appropriate human review of outputs. The time to production for well-scoped versions of these use cases is measured in weeks to months, not years.<\/p>\n
The use cases that receive the most board enthusiasm but require substantially more investment include fully automated customer-facing interactions where errors have significant consequences, AI-assisted decision making in regulated domains where model behaviour must be explicable and auditable, and domain-specific applications where the model’s general knowledge is insufficient and the organisation’s proprietary data must be incorporated into the model’s knowledge. These are achievable, but they require the data infrastructure, governance framework, and risk management investment that the demo does not reveal.<\/p>\n
A clear articulation of which category a proposed AI use case falls into is the most valuable thing a technology leader can bring to the first post-ChatGPT board conversation. Boards that have seen the demo will ask what the organisation is doing about AI. The answer that serves them best is one that names specific near-term use cases with realistic timelines, names the investment required for the more complex use cases, and frames both in terms of the business outcomes they enable.<\/p>\n
The excitement generated by ChatGPT is useful only if it translates into AI investment that the organisation can absorb and put to productive use. Most large enterprises are not as ready to absorb AI investment as their enthusiasm for the technology suggests.<\/p>\n
The readiness assessment starts with data. Language model applications that go beyond generic content generation require access to the organisation’s data: customer interaction history, product documentation, internal knowledge bases, support tickets, code repositories. The quality, accessibility, and governance of this data determines what is possible. An organisation with fragmented, ungoverned, inaccessible data cannot quickly convert AI enthusiasm into AI value.<\/p>\n
The assessment continues with governance. AI systems deployed in enterprise environments require a governance framework that addresses who can access the system, what data can be processed through it, how outputs are reviewed before they affect business decisions, and what the liability model is when an AI system produces incorrect or harmful output. These questions do not have simple answers, and organisations that deploy AI systems before establishing governance frameworks will discover their inadequacy through incidents rather than through planning.<\/p>\n
The assessment includes skills. Building and operating AI applications requires a combination of skills that most enterprise technology teams do not currently have in depth: prompt engineering for language model applications, evaluation and testing of probabilistic systems, integration of AI models into production workflows, and the security and compliance expertise required to manage AI risk. Skills gaps do not prevent initial deployment, but they limit the organisation’s ability to operate AI systems reliably at scale.<\/p>\n
ChatGPT’s commercial success will accelerate the deployment of large language model technology across enterprises, including through shadow IT adoption where individual teams deploy AI tools without organisational awareness or governance. This creates a governance urgency that predates the organisation’s formal AI programme.<\/p>\n
The questions that require immediate attention are not complex in principle, though they require deliberate decisions to answer. What data can employees send to external AI services as part of their work? Customer data, financial data, personal data, and confidential business information all have regulatory and contractual implications when processed through a third-party AI service. Most ChatGPT usage happens through the public interface, which uses conversation data for training purposes in the absence of specific contractual arrangements. Employees using ChatGPT with confidential information may be sharing it without realising it.<\/p>\n
This is not an argument against using large language model tools. It is an argument for a clear AI use policy that employees understand before they start using these tools, not after an incident makes the gap visible.<\/p>\n
The board and senior executive team that is now asking about AI strategy deserves a structured response rather than either an enthusiasm match or a risk-only caution.<\/p>\n
The structured response has three components. Current capabilities and near-term deployment plans: what AI applications the organisation is currently piloting or has in production, what is planned for the next six to twelve months, and what business outcomes these applications are designed to deliver. AI readiness: an honest assessment of the data, governance, and skills investments required for the organisation to absorb AI investment productively. And risk management: the governance framework for AI use, the data privacy and security controls around AI systems, and the monitoring and oversight model that maintains appropriate human accountability for AI-assisted decisions.<\/p>\n
A board that understands all three components is equipped to govern AI investment. A board that only understands the capability and excitement dimension will approve investment that the organisation cannot absorb, into applications that the governance framework does not yet support. The technology leader’s job after ChatGPT is to ensure that the conversation produces the former rather than the latter.<\/p>\n
The moment for a structured enterprise AI conversation has arrived, driven by forces outside the technology team’s control. That is an opportunity. Use it well.<\/p>\n","protected":false},"excerpt":{"rendered":"
ChatGPT’s launch has made the capabilities and limitations of large language models viscerally real to executive leaders. The question is no longer whether AI will change enterprise operations. It’s how quickly and on what terms.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-79","post","type-post","status-publish","format-standard","hentry","category-executive-briefings"],"_links":{"self":[{"href":"https:\/\/baecke.io\/index.php?rest_route=\/wp\/v2\/posts\/79","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/baecke.io\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/baecke.io\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/baecke.io\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/baecke.io\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=79"}],"version-history":[{"count":0,"href":"https:\/\/baecke.io\/index.php?rest_route=\/wp\/v2\/posts\/79\/revisions"}],"wp:attachment":[{"href":"https:\/\/baecke.io\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=79"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/baecke.io\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=79"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/baecke.io\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=79"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}