For the last two years, the conversation around artificial intelligence has revolved around speed. How could it make a meeting wrap-up faster or automate a boring, time-wasting task? How could it cut hours from of busy work weeks? That initial wave has started to cool, and a stronger current has started to rise, the ability of artificial intelligence to improve the odds of success.
One truth stood out, the value that companies will realise through artificial intelligence will not be from speed, but from the ability to make more impactful decisions, which ultimately determine an organisation's success.
Beyond time saved
While it's true that the ultimate metric for AI's value will be time saved, the more meaningful metric is improved business outcomes.
Think about how executives define business outcomes, they don't measure how quickly a staff member completes a task or responds to an email, but rather if deals were closed, the company gained market share and acquired better clients or made more judicious investments. That's the metric that AI now needs to address, the ability to improve the odds of success.
Sales leaders are constantly grappling with the question of which opportunities to pursue, which accounts to prioritise and which tactics will realize greater win rates. Soon, AI will start to make these recommendations as these leaders are constantly focused on one factor, will this deal close?
This philosophy translates into marketing, product development, finance and every other function within an organisation, leaders are looking to understand if their actions are improving the odds of success. Time saved is a valuable metric, but the greater opportunity is using the time saved to focus on the interactions and decisions that improve business outcomes.
From tool to thought partner
It's rare that a company today is not using some combination of Open AI, Claude, Gemini or another large language model. As these fundamental models continue to evolve, the value proposition of just layering a user interface on top of a large language model will diminish.
What will differentiate companies is the creation of AI that operates like a colleague, rather than a tool, with the ability to incorporate a degree of context. While context is what differentiates humans from AI, as AI becomes more ingrained in business processes, it will start to collect the same context that a colleague would possess, which will then be applied to recommend or execute an action.
Rather than a tool that waits for direction, contextual AI will start to recognise the status of a use case, identify the appropriate action and even take ownership of some tasks, routing others to a colleague, requesting approvals or even automating communications and escalating to a human for more complex issues.
Imagine an AI system that understands historical win rates, the competitive landscape, the patterns of thousands of similar decisions and even how to aggregate data across a dozen different systems, rather than just a "nice front-end" for ChatGPT.
The case for a feedback loop
The typical business process contains four stages, strategy, development, execution and results. The results of a given initiative should then inform the strategy of the that function. Too often companies fail to capture those lessons-learned, as a campaign concludes, a proposal is submitted or a product is launched, the business starts the next initiative without understanding why any given action had the result that it did.
That's where AI can start to add value, by capturing the results of a given initiative, wins and losses, and applying those learnings to future strategies.
For leaders this means providing AI with the necessary context to inform its recommendations, that context begins with the institutional knowledge and experience that have been realised by the function or business. Equally important, every initiative must be evaluated to understand if, once executed, it drove the desired outcome, Did the company win the deal? Did the marketing campaign generate conversions? Did the investment realize a return? That data must be fed back into the system; to create a feedback loop, every learning can be applied to the future decision-making.
AI is no longer about just using more AI, or faster AI, but rather understanding how to apply AI to realise better business outcomes, which will in turn generate compounded value for an organisation.
Better outcomes, not faster ones
Business leaders are starting to understand that the value of AI will not be measured in how many tools adopt it or how much time it saves, valuable as that is in the short-term but the ability of AI to help an organisation realise better, more judicious decisions will prove critical to delivering improved business outcomes.
The companies that apply AI to learn from every outcome and realise their impact on future decisions will create lasting value that is difficult to replicate.