Contrary to the current AI hype cycle, AI isn’t actually new. AI is a decades-old technology with a long history of promises and setbacks for early adopter companies attempting to commercialize the technology at scale.
In 2023, however, there was a flurry of public-facing product releases that, in the aggregate, could be the breakthroughs the technology needs to finally achieve mainstream, world-changing adoption – at scale.
AI’s integration at the enterprise level has been swift in some sectors, slower in others, and not always precise, intentional or even visible to observers. In terms of adoption, McKinsey surveys indicate that one-third of survey respondents indicated their organization already uses generative AI (Gen AI) “in at least one business function,” and 25 percent say it’s “already on their boards’ agendas.”
That’s not to say everyone is convinced that this technology will be world-changing or business-optimizing.
Like all technology adoption curves, a large segment of brands are currently content to “dabble” in AI, running prototypes and evaluating outcomes, while their competitors assault “the bleeding edge” of AI innovation. Some of these brands will be “fast followers,” and some will adopt slowly, depending on “proven” case studies in their respective sectors.
So far, we’ve learned in practice that Gen AI has enormous potential, but there are many pitfalls to its successful implementation – moving from hype to truly transformative use at the enterprise level and at scale is not guaranteed.
For enterprises, we are forecasting that 2024 will be the year that Gen AI comes out of the lab and into real-world applications. Here are three rules for brands to follow to make their enterprise scale AI programs successful in 2024 and beyond.
#1: Pilot AI solutions and determine efficacy before introducing them.
C-suite decision-makers are navigating a rapidly expanding AI-product ecosystem, and identifying technologies that can be scaled at the enterprise level can be challenging – the ecosystem is filled with new, unproven apps that offer promise but have not been “stress tested” in a real world channel operating environment.
The first key to AI success lies in identifying those priority use cases where embedding AI in a customer-facing process makes sense, and that the new AI-driven process drives the desired metrics such as revenue, engagement intensity, efficiencies and other key drivers of business outcomes.
As suggested above, introducing and scaling new, unproven AI tech entails expanding the successful prototypes from a controlled environment to a widespread application across an enterprise’s customer-facing domains.
Brands that successfully convert to the status of AI-driven will realize that AI’s value proposition lies not in its ability to announce its presence to consumers, but in its ability to seamlessly, transparently augment the consumer experience, enhancing value without subtracting from the authenticity of the brand promise.
Transparency of AI-generated interactions, from the consumer’s perspective, will separate the truly AI-savvy brands from the pack. This assumes that AI adds value to, and changes the nature of interaction positively, and is not used to simply make bad processes more efficient.
The criteria for AI-driven process success will be that interactions will appear more intelligent, targeted, and timely and have a higher utility and “value exchange” for the consumer’s time investment.
Because AI is data-driven, it will, by definition, be at the core of the brand’s evolving competitive capabilities. Smart brands will seamlessly embed AI into their customer-facing processes in a way that is not blatantly apparent to competitors as to how it’s actually being used.
#2: Integrate AI transparently into products and services.
Brands must be sensitive to the fact that, after decades of expansive data collection and misuse, many consumers are wary of having a new data-hungry technology inserted into their brand experience. Consumers also want brands to prioritize data privacy over rapid technology deployment.
As a May 2023 consumer survey found, “Most Americans want companies themselves to limit the risks, even if they don’t trust them to do so.”
To cultivate consumer trust, brands must integrate AI transparently into their products and services, demonstrating a commitment to transparency and ethical implementation.
Specifically, brands should communicate:
Use cases and implementation methods
Personal information and data collected
Steps they are taking to ensure data privacy
Integration of these practices within the brand’s customer framework.
This assures consumers that while AI is a pervasive part of their experience with a brand, their personal information and privacy remain a priority at every touchpoint.
#3: Leverage accurate CX-focused Gen AI output at scale.
As brands transition AI from experimental phases in the lab to live production environments, the accuracy of AI-generated output becomes crucial for safeguarding the brand’s reputation and customer trust.
This can be especially challenging as brands strive to ensure that targeting outputs are relevant and precise, allowing them to deliver offers and opportunities that impact customer buying decisions without compromising confidentiality or privacy concerns.
To achieve this precision, brands must adopt best AI implementation best practices, including establishing continuous monitoring channels to “scrub” AI outputs, ensuring that all AI-generated content aligns with the brand’s value proposition and messaging strategy.
For GenAI to actually improve brand positioning, marketing messages must be timely, relevant, proactive, personalized, and offer consumers a valuable exchange for their engagement.
For example, an auto manufacturer uses comprehensive 360-degree view data on prospects or customers—including customer-supplied data and lifestyle information inferred from other sources – to recommend suitable automotive models and price ranges.
If AI algorithms fail to leverage this 360-degree view, whether the data is from first or third-party sources, the targeting – specifically, the right vehicle to suit their needs at the right price – may miss the mark, leading to inefficiencies, lower conversion rates, defection of previously loyal customers and potential damage to the brand.
Simply put, truly impactful AI technologies will recognize and integrate these data elements to maintain the effectiveness and relevance of every marketing offer, meaningfully moving the meter on customer engagement and retention.
2024: The Breakthrough Year
It seems 2024 will be the breakthrough year for AI at scale as it moves out of the lab and deeper into the mainstream, impacting more enterprise initiatives across channels, business units and product factories.
To maximize the technology’s positive impact and mitigate any potential drawbacks, brands should rigorously identify the most successful applications within their prototyping environments to strategically expand and scale their Gen AI footprint across the enterprise footprint.
Meanwhile, they must establish stringent guardrails that ensure the precision of targeting outputs and fortify consumer privacy in parallel, reinforcing the consumer’s perception of value in the brand’s offers and trust in the brand itself.
By embedding privacy-first principles across all Gen AI outputs, including text, audio, image or video, and by using generative AI as an integral part of the corporate culture, brands can navigate the complex interplay between innovation, consumer trust and market leadership.
Scaling AI while protecting privacy is a challenge and an opportunity that will, in our AI-driven future, increasingly separate successful brands from the pack by wowing their consumers, creating an emotional connection to the brand and driving revenue growth to new, previously unimaginable levels.