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Taking advantage of the opportunities that artificial intelligence brings isn’t just about installing new technology. It requires organizations to prioritize strong governance, invest in their people and implement robust innovation frameworks. These pillars improve the likelihood of AI adoption enhancing an organization’s operations and service offerings, wh
Traditional business management models such as “People, Process, IT” and “SWOT analysis” have limitations in the current environment of rapid change and innovation. An evolution in these models is necessary to ensure that agencies like ours, and other organizations, can adapt whilst using tools that are familiar, well-understood and make it easier to thrive.
SWOT analysis for agencies in the age of AI
Like it or loath it, SWOT analysis is widely used to guide businesses through change. But, in immature markets, strengths and weaknesses are hard to define. When technology adoption is unclear, it’s better to focus on opportunities and threats rather than current advantages and shortcomings.
Key threats to the current agency model and services
Using our own sector as a guide, a lot has been written about the potential threats that AI brings to our industry including:
Relevancy: With ongoing advancements in AI, agencies might have to revise their services, since some traditional options could lose relevance for clients.
Fee pressures: The efficiency gains enabled by AI might lead clients to expect lower fees, potentially undervaluing the enhanced productivity and higher quality agencies can deliver
In-housing: Clients may see AI as an opportunity to bring more capabilities in-house, reducing their reliance on agency partners for certain tasks or services.
Dogmatism: The rapid pace of technological change presents a significant challenge to agencies dependent on rigid processes.
It is important to remember that these concerns are familiar territory and predate the existence of AI by some time. For agencies that take initiative, the chances to benefit far outnumber the risks.
Agencies have always been required to navigate evolving market conditions and changing client needs. True expertise and adaptability are still at the core of their success.
Agencies are uniquely positioned to draw upon the collective experience gained across a diverse set of clients and sectors. They are well positioned to quickly develop an informed understanding of what strategies and solutions are effective in the marketplace today. This insight enables them to anticipate shifts, stay ahead of trends and lead clients in the adoption of new approaches – ensuring they continue to deliver meaningful outcomes in an environment shaped by rapid technological advancement.
Opportunities Presented by New Technology
As mentioned, I believe AI’s benefits can easily outweigh the threats if businesses address challenges and adapt effectively. Emerging technologies ultimately create new possibilities, enabling agencies to innovate rapidly.
Brands are expected to increase their marketing technology budgets, creating opportunities for agencies to bring these new innovations from concept to reality much faster.
New technology capabilities are also driving channel shifts among end-users. Brands need to respond to this and will turn to agencies to help them establish presence in new channels while restructuring existing ones.
Finally, these new technologies enable individuals in agencies to develop deep and broad capabilities along with extensive sector experience. Combining these factors will allow agencies investing in their people to respond faster and more flexibly than ever before to client needs.
The Big Opportunity: A Shift from Output to Outcome
Perhaps the greatest opportunity for agencies lies in producing higher-level work. A great agency is defined by its ability to direct a process that leads to the right output for a desired outcome, rather than focusing solely on production itself. This approach ensures a clear connection between what is created and the broader result the client aims to achieve.
The true value of an agency is not merely in creating high-quality documents, visuals, code, or ads. Instead, it is defined by the strategic decision on which output to pursue and the process leading to that choice. Whether the output creation shifts from mainly human-driven to largely AI-driven, the intrinsic value provided by the agency remains its ability to identify and justify the chosen output.
Leveraging Technology to Enhance Value
Historically, agencies have consistently adopted the latest technologies to speed up task delivery — whether advanced printers, cutting-edge Apple Macintosh computers, or specialized software like Photoshop or Visual Studio. These technologies were often costly or complex, requiring specialist knowledge and expertise. Today, recent advancements have resulted in tools that are affordable and accessible, thanks to natural language interfaces promising ease of use even for non-experts.
Despite these advances, the expert’s role is more critical than ever. As AI technologies raise the baseline of what’s considered average, the ability to stand out and add real value increasingly depends on the expertise needed to extract that final, elusive 1–5% improvement that makes all the difference.
The Importance of AI Skills and Expert Deployment
Effective use of AI tools has become a skill in itself. These tools have evolved beyond simple chat interfaces into complex suites including assistants, agents, and workflow automation. Determining which tool or combination to deploy requires careful consideration and real expertise. It often involves implementing custom configurations tailored to specific needs.
Agencies can deploy AI tools at scale and deliver specialized outputs. Clients may have the same tools but rarely the expertise required to match the quality or outcomes agencies achieve.
Emerging Products
Inevitable new markets and channels driven by technology require new products. This also presents an opportunity for agencies ready to invest in R&D and product development. IDHL Labs is part of our response to this opportunity and has already launched AI-specific capabilities within our creative and performance marketing product sets.
Empowering People, Evolving Processes, Leveraging Technology, Driving Innovation
I began this article by stating that traditionally, building a business in established markets relied on the well-known formula of investing in “People, Process, and IT.” However, the landscape has changed. In this rapidly evolving and less understood environment, businesses and agencies must do more. The current environment requires ongoing investment not only in people, processes, and technology but also in real innovation to ensure organizations stay at the forefront of their sector, delivering meaningful services to clients.
People: Navigating Change and Empowering Adoption
People often pose the main barrier to adopting new technologies. Change is difficult, often met with resistance due to ingrained habits and organizational culture, making it challenging even for willing individuals to adapt.
To overcome these challenges, it is vital to invest in comprehensive training and development for your teams. Training should not be limited to technical aspects of using new tools. It must enable effective work with AI. Skills like prompt engineering should already be basic requirements for anyone entering the workplace.
Empowerment should enable informed decisions about when to use AI and when to rely more on human judgment. The relationship between AI and human input is best seen as a spectrum rather than a simple either-or choice. It’s not just about whether something is human-created or AI-generated, but the degree of human and AI involvement at each step.
Our colleagues understand not to fully rely on AI-generated outcomes. Instead, we promote scenarios where AI produces output that is then reviewed by humans, alongside efforts where both human and AI collaborate to create outputs. It’s equally valid for a human to create something entirely or with AI review involved.
It is crucial that colleagues are trained to recognize when additional scrutiny is needed and when to double-check AI outputs. They must be aware of potential biases and hallucinations common with LLMs—taking steps to avoid perpetuating unhelpful or harmful outputs.
Furthermore, teams should understand how these tools operate, especially concerning data privacy. They need to know whether data input into AI tools might be used for further training or risk exposure publicly. Handling sensitive information thus calls for careful management and deep knowledge of data governance protocols.
Ultimately, effective and ongoing personnel training is fundamental to ensure teams are prepared to work with AI technologies securely and responsibly.
Process: Automation, Reliability, and Human Oversight
Automation is often the first consideration when adopting AI in the workplace. Automating manual, repeatable processes holds great potential for many organizations, and a common hope is that AI will finally realize this potential.
The current AI toolset also empowers colleagues to build automated processes that used to require a developer or automation expert. For example, when working with a new client, IDHL once used a manual checklist shared across multiple teams to ensure smooth client onboarding. Today, AI agents automatically perform these manual checks and instantly flag anomalies to relevant team members.
As a result, organizations will see more automated workflows across units. Managing and updating these systems will be challenging, particularly as oversight and human involvement must be carefully considered. With autonomous agents deciding, it becomes increasingly important to define where human input is needed within automated processes.
Expecting AI to flawlessly automate processes also presents challenges. LLMs are nondeterministic, meaning they don’t always produce identical outcomes from the same inputs. For processes requiring reliability and consistency, this can be a deal-breaker. Greater opportunity may lie where human intervention is currently needed. Making statistically informed and safe judgments might allow the seeming unpredictability of LLM outputs to be an advantage.
Quality control is essential. Like manufacturing, AI-driven automation must follow strict standards. Organizations must maintain high-quality levels as automation grows, with oversight of automated outputs likely shifting to senior staff rather than less specialized employees.
Information technology: Navigating rapid change
Currently, IT isn’t the barrier; technology is advancing faster than businesses and individuals can adapt. The main challenge is the urge to chase every new product, increasing business risk through frequent pivots and changes.
Compounding this situation is the market dynamic, where major software vendors are aggressively competing for market share, often driving down costs in a bid to secure dominance. The outcome of this intense competition is crucial, as the provider that succeeds in this current 'land grab' over the coming 6 to 18 months sets themselves up as the dominant player for the next 5 to 10 years. Established giants such as Google and Microsoft appear well-positioned to prevail, given their capacity to fund innovation and ensure their products reach key users on a global scale.
For organisations, it is therefore prudent to limit the number of platforms and providers used and deploy their solutions consistently throughout the business. Having many point solutions risks needing to integrate these further down the line. Creating the right balance between deploying general purpose AI capability and specialised AI solutions (such as software development and image generation) is something each organisation should consider.
Over time, new features in the technology will plateau, meaning choice of provider is less critical than many may think at present. First mover advantage has been a key consideration in the past, but in the current environment, its impact is fleeting, often lasting only days or weeks.
Innovation
While the elements of people, processes, and IT provide a strong framework for integrating established technologies and achieving steady, incremental growth, the current pace of innovation demands a more direct and proactive approach. To remain competitive and responsive to market changes, agencies and, in fact, almost all organisations, must dedicate more resource to technology-driven innovation.
Enhancing innovation does not necessarily mean hiring dedicated R&D personnel. AI-driven productivity improvements create capacity within existing teams; organisations should consider utilising this excess capacity in whole or in part to driving technology and AI driven innovation activity.
At IDHL, our approach has been the creation of IDHL Labs - a collaborative initiative bringing together passionate individuals from across our organisation. The purpose of IDHL Labs is to accelerate the adoption of new technologies and to foster a culture of innovation throughout all our teams. We recognise that, as the business environment experiences unprecedented disruption due to advancements in AI and related technologies, innovation will be a key differentiator in the next phase of organisational development.
In uncertain times, success demands experimentation and testing of new ideas. While many attempts may fail, those that succeed will drive the business forward and create key advantages. These successes will empower our people, enabling them to achieve the marginal but vital differentiations that are increasingly difficult to secure in a crowded marketplace. Equally every failure is still a success in our eyes, helping us to divert resource away from unsolvable challenges and evolve hypotheses towards genuinely valuable outcomes.
Moreover, we see value in progressing swiftly through this period of upheaval towards a more stable future, where a return to “people, processes, and IT” is once again the norm. Getting to that point depends on sustained investment in innovation.
Governance in the ‘Age of Innovation’
Governance is a critical consideration given the uncertainties (perceived or real) surrounding AI. The importance of the “human in the loop” has already been highlighted, this represents just one aspect of a broader governance framework.
A comprehensive approach to governance must include clear guidance on tool and provider selection – ensuring safe and appropriate use within the organisation whilst not stifling the ability to experiment.
Managing technological risk and promoting responsible AI usage
In a landscape, where new products launch daily, the risk of selecting a provider that may quickly disappear from the market is greater than ever. The evaluation framework for approving new tools should take this into consideration, working to the modern best practice of composable architectures will help to reduce this risk.
It is equally important for colleagues and teams to understand the implications of unprofessional or careless use of AI tools. Presenting unvetted outputs especially those containing inaccuracies or so-called “hallucinations” - to customers or the wider market can be highly damaging to the organisation’s reputation, just ask Deloitte. When working with AI generated outputs colleagues need to understand that when they accept the output, it becomes their output and they assume full responsibility for it.
Strategic data governance for sustainable AI success
Governance goes beyond tool selection and user behaviour to include ongoing management of organisational data. AI initiatives need high-quality contextual data, making investment in robust data storage and classification essential for long-term success.
Given the pace of change in the AI marketplace, some organisations may benefit from adopting a “wait and see” approach to direct AI technology adoption. A short-term focus on strengthening document governance and data cleanup may pay dividends setting these organisations for greater success over the next two to five years, as new technologies will have greater impact when implemented across a well-organised data and document ecosystem.
Ultimately, the way in which data within the organisation is organised, categorised, and managed will have a profound impact on the success of future AI ventures. Effective governance, therefore, is not just about compliance or risk avoidance - it is a strategic enabler of innovation and long-term competitive advantage.
Where this leaves us
The simple truth is this: AI will force organisations to move faster than traditional management models were designed for. But the answer isn’t to abandon those models, it’s to evolve them. The organisations that will thrive are the ones that continue to invest in their people while modernising systems and processes, strengthening governance and creating the space for real innovation.
The future belongs to those that treat AI not as a shortcut, but as a catalyst for better thinking, better systems and better outcomes. Good luck.
