AI is transforming how tech and SaaS companies make, personalize, and scale their products. From streamlining workflows to powering global customer experiences, this new era of human-AI cooperation has given a new definition to what software can do. Here’s how leading platforms are putting AI to work, and what it means for the future of your business.

How human-AI cooperation is powering authentic engagement at scale

“AI is just a tool.” What started as a reassuring and pragmatic phrase utilized partly to dampen media hype has arguably become a platitude.

The argument about how artificial intelligence will alter the workforce is really similar to late ‘90s discussions about the internet. Actually, the debate about the long-term effect of tech is a modern-day parlour game. What really influences businesses is the functions of AI now and in the short to medium term.

AI as business multiplier in tech

An instructive place to start is to observe how some of the big tech platforms market themselves, to see just how essential AI now is to their propositions.

Despite the fact that it’s really normal to see AI front and center in these kinds of B2B tech marketing efforts, the current AI boom is exactly different from the previous one. In 2017, during the last AI summer, all kinds of SaaS businesses trumpeted their AI credentials, but a lot of the functionality on offer was some form of predictive analytics assisted by machine learning.

For instance, Marketing and sales software added a skein of intelligence to its automation functionality – helping with lead scoring, understanding propensity to buy, and deciding who might currently be in-market for a  particular product.

Besides, in customer service, there were automated chatbots which, if they went beyond frigid decision trees, would make use of NLP (natural language processing) tech of the time to make identification of a limited set of common customer requests, which enabled a human representative to check or automate a canned response if a certain confidence level was achieved.

These innovations were of great essence of course, but the tech of 2017 was only touching the surface of the possibilities of AI. Others at the forefront of NLP were doing product categorization or optimizing copy – see the development of CRM companies like Phrasee (now Jacquard), which helped to pioneer AI copywriting.

Fast forward to today and the landscape is very different. Movable Ink, a CRM company specializing in personalization of email and mobile messages, describes its newest SaaS platform, Da Vinci, launched in 2023, as “a unified suite of marketing AI models optimizing every customer experience for increased lifetime value.”

Generative AI as the keystone of institutional knowledge

AI is now at the center of personalization, automation and localization, helping companies (including SaaS businesses) scale. And consumers are also using AI-powered experiences every day in various ways.

It’s this advancement in LLMs and computer vision, beginning in the 2020s that has caused more and more use cases, as tech companies make the most of easy access to powerful models.

Absorbed in CRM, we can know how generative AI in particular is reshaping SaaS products. Salesforce provides solutions that can use customer data to “bring conversational AI to any workflow, user, department and industry”. That means both customers and employees can engage with an agent grounded in relevant data. Customer support teams can then begin to build a knowledge base using this technology, with summarized case resolutions.

Elsewhere, McKinsey specifically exemplifies how LLMs and RAG methodology (retrieval augmented generation) empower businesses to create a font of institutional knowledge. The consulting firm’s generative AI platform, named Lilli, was reportedly* trained on 100 years of McKinsey intellectual property. The firm stated that almost three quarters of staff are actively using the tool, with a resulting 30% time-saving on searching for and synthesizing knowledge.

The advantages of utilizing AI for institutional knowledge in this way are compound – the efficiency of individuals can be improved whilst ensuring greater consistency across a business too. At the Festival of Marketing in 2023, a famous woman explained how the business had determined to use generative AI when creating a member app portal on it.

The simple idea was to write marketing content for the portal in a way processed by AI. This approach would guard against a “disconnect” between teams, as customer service teams could then be guided on how to have communication with customers in a way having consistency with the company.

Equipped with both predictive and generative AI, businesses could also see benefits in adapting content for new audiences.

The AI-human double act

So, back to the assertion in the introduction, that AI is now more than a simple tool, we can know the truth that a lot of controversies around AI innovation stem ultimately from issues around its stewardship – what data it is trained on, how sustainable it is, where it is used, how it is flagged and so on.

The long-term efficiencies created by AI may be hard to estimate, but in the short-term at least AI is swiftly helping many tech and SaaS companies create better, more responsive, more nuanced products and experiences.